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App_Function_Libraries/Article_Extractor_Lib.py

@@ -0,0 +1,107 @@
+# Article_Extractor_Lib.py
+#########################################
+# Article Extraction Library
+# This library is used to handle scraping and extraction of articles from web pages.
+# Currently, uses a combination of beatifulsoup4 and trafilatura to extract article text.
+# Firecrawl would be a better option for this, but it is not yet implemented.
+####
+#
+####################
+# Function List
+#
+# 1. get_page_title(url)
+# 2. get_article_text(url)
+# 3. get_article_title(article_url_arg)
+#
+####################
+#
+# Import necessary libraries
+import logging
+# 3rd-Party Imports
+import asyncio
+from playwright.async_api import async_playwright
+from bs4 import BeautifulSoup
+import requests
+import trafilatura
+# Import Local
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+def get_page_title(url: str) -> str:
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.text, 'html.parser')
+        title_tag = soup.find('title')
+        return title_tag.string.strip() if title_tag else "Untitled"
+    except requests.RequestException as e:
+        logging.error(f"Error fetching page title: {e}")
+        return "Untitled"
+
+
+def get_artice_title(article_url_arg: str) -> str:
+    # Use beautifulsoup to get the page title - Really should be using ytdlp for this....
+    article_title = get_page_title(article_url_arg)
+
+
+def scrape_article(url):
+    async def fetch_html(url: str) -> str:
+        async with async_playwright() as p:
+            browser = await p.chromium.launch(headless=True)
+            context = await browser.new_context(
+                user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3")
+            page = await context.new_page()
+            await page.goto(url)
+            await page.wait_for_load_state("networkidle")  # Wait for the network to be idle
+            content = await page.content()
+            await browser.close()
+            return content
+
+    def extract_article_data(html: str) -> dict:
+        downloaded = trafilatura.extract(html, include_comments=False, include_tables=False, include_images=False)
+        if downloaded:
+            metadata = trafilatura.extract_metadata(html)
+            if metadata:
+                return {
+                    'title': metadata.title if metadata.title else 'N/A',
+                    'author': metadata.author if metadata.author else 'N/A',
+                    'content': downloaded,
+                    'date': metadata.date if metadata.date else 'N/A',
+                }
+            else:
+                print("Metadata extraction failed.")
+                return None
+        else:
+            print("Content extraction failed.")
+            return None
+
+    def convert_html_to_markdown(html: str) -> str:
+        soup = BeautifulSoup(html, 'html.parser')
+        # Convert each paragraph to markdown
+        for para in soup.find_all('p'):
+            para.append('\n')  # Add a newline at the end of each paragraph for markdown separation
+
+        # Use .get_text() with separator to keep paragraph separation
+        text = soup.get_text(separator='\n\n')
+
+        return text
+
+    async def fetch_and_extract_article(url: str):
+        html = await fetch_html(url)
+        print("HTML Content:", html[:500])  # Print first 500 characters of the HTML for inspection
+        article_data = extract_article_data(html)
+        if article_data:
+            article_data['content'] = convert_html_to_markdown(article_data['content'])
+            return article_data
+        else:
+            return None
+
+    # Using asyncio.run to handle event loop creation and execution
+    article_data = asyncio.run(fetch_and_extract_article(url))
+    return article_data
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/Article_Summarization_Lib.py

@@ -0,0 +1,284 @@
+# Article_Summarization_Lib.py
+#########################################
+# Article Summarization Library
+# This library is used to handle summarization of articles.
+
+#
+####
+#
+####################
+# Function List
+#
+# 1.
+#
+####################
+#
+# Import necessary libraries
+import datetime
+from datetime import datetime
+import gradio as gr
+import json
+import os
+import logging
+import requests
+# 3rd-Party Imports
+from tqdm import tqdm
+
+from App_Function_Libraries.Utils import sanitize_filename
+# Local Imports
+from Article_Extractor_Lib import scrape_article
+from Local_Summarization_Lib import summarize_with_llama, summarize_with_oobabooga, summarize_with_tabbyapi, \
+    summarize_with_vllm, summarize_with_kobold, save_summary_to_file, summarize_with_local_llm
+from Summarization_General_Lib import summarize_with_openai, summarize_with_anthropic, summarize_with_cohere, summarize_with_groq, summarize_with_openrouter, summarize_with_deepseek, summarize_with_huggingface
+from SQLite_DB import Database, create_tables, add_media_with_keywords
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+def ingest_article_to_db(url, title, author, content, keywords, summary, ingestion_date, custom_prompt):
+    try:
+        # Check if content is not empty or whitespace
+        if not content.strip():
+            raise ValueError("Content is empty.")
+
+        db = Database()
+        create_tables()
+        keyword_list = keywords.split(',') if keywords else ["default"]
+        keyword_str = ', '.join(keyword_list)
+
+        # Set default values for missing fields
+        url = url or 'Unknown'
+        title = title or 'Unknown'
+        author = author or 'Unknown'
+        keywords = keywords or 'default'
+        summary = summary or 'No summary available'
+        ingestion_date = ingestion_date or datetime.datetime.now().strftime('%Y-%m-%d')
+
+        # Log the values of all fields before calling add_media_with_keywords
+        logging.debug(f"URL: {url}")
+        logging.debug(f"Title: {title}")
+        logging.debug(f"Author: {author}")
+        logging.debug(f"Content: {content[:50]}... (length: {len(content)})")  # Log first 50 characters of content
+        logging.debug(f"Keywords: {keywords}")
+        logging.debug(f"Summary: {summary}")
+        logging.debug(f"Ingestion Date: {ingestion_date}")
+        logging.debug(f"Custom Prompt: {custom_prompt}")
+
+        # Check if any required field is empty and log the specific missing field
+        if not url:
+            logging.error("URL is missing.")
+            raise ValueError("URL is missing.")
+        if not title:
+            logging.error("Title is missing.")
+            raise ValueError("Title is missing.")
+        if not content:
+            logging.error("Content is missing.")
+            raise ValueError("Content is missing.")
+        if not keywords:
+            logging.error("Keywords are missing.")
+            raise ValueError("Keywords are missing.")
+        if not summary:
+            logging.error("Summary is missing.")
+            raise ValueError("Summary is missing.")
+        if not ingestion_date:
+            logging.error("Ingestion date is missing.")
+            raise ValueError("Ingestion date is missing.")
+        if not custom_prompt:
+            logging.error("Custom prompt is missing.")
+            raise ValueError("Custom prompt is missing.")
+
+        # Add media with keywords to the database
+        result = add_media_with_keywords(
+            url=url,
+            title=title,
+            media_type='article',
+            content=content,
+            keywords=keyword_str or "article_default",
+            prompt=custom_prompt or None,
+            summary=summary or "No summary generated",
+            transcription_model=None,  # or some default value if applicable
+            author=author or 'Unknown',
+            ingestion_date=ingestion_date
+        )
+        return result
+    except Exception as e:
+        logging.error(f"Failed to ingest article to the database: {e}")
+        return str(e)
+
+
+def scrape_and_summarize_multiple(urls, custom_prompt_arg, api_name, api_key, keywords, custom_article_titles):
+    urls = [url.strip() for url in urls.split('\n') if url.strip()]
+    custom_titles = custom_article_titles.split('\n') if custom_article_titles else []
+
+    results = []
+    errors = []
+
+    # Create a progress bar
+    progress = gr.Progress()
+
+    for i, url in tqdm(enumerate(urls), total=len(urls), desc="Processing URLs"):
+        custom_title = custom_titles[i] if i < len(custom_titles) else None
+        try:
+            result = scrape_and_summarize(url, custom_prompt_arg, api_name, api_key, keywords, custom_title)
+            results.append(f"Results for URL {i + 1}:\n{result}")
+        except Exception as e:
+            error_message = f"Error processing URL {i + 1} ({url}): {str(e)}"
+            errors.append(error_message)
+            results.append(f"Failed to process URL {i + 1}: {url}")
+
+        # Update progress
+        progress((i + 1) / len(urls), desc=f"Processed {i + 1}/{len(urls)} URLs")
+
+    # Combine results and errors
+    combined_output = "\n".join(results)
+    if errors:
+        combined_output += "\n\nErrors encountered:\n" + "\n".join(errors)
+
+    return combined_output
+
+
+def scrape_and_summarize(url, custom_prompt_arg, api_name, api_key, keywords, custom_article_title):
+    try:
+        # Step 1: Scrape the article
+        article_data = scrape_article(url)
+        print(f"Scraped Article Data: {article_data}")  # Debugging statement
+        if not article_data:
+            return "Failed to scrape the article."
+
+        # Use the custom title if provided, otherwise use the scraped title
+        title = custom_article_title.strip() if custom_article_title else article_data.get('title', 'Untitled')
+        author = article_data.get('author', 'Unknown')
+        content = article_data.get('content', '')
+        ingestion_date = datetime.now().strftime('%Y-%m-%d')
+
+        print(f"Title: {title}, Author: {author}, Content Length: {len(content)}")  # Debugging statement
+
+        # Custom prompt for the article
+        article_custom_prompt = custom_prompt_arg or "Summarize this article."
+
+        # Step 2: Summarize the article
+        summary = None
+        if api_name:
+            logging.debug(f"Article_Summarizer: Summarization being performed by {api_name}")
+
+            # Sanitize filename for saving the JSON file
+            sanitized_title = sanitize_filename(title)
+            json_file_path = os.path.join("Results", f"{sanitized_title}_segments.json")
+
+            with open(json_file_path, 'w') as json_file:
+                json.dump([{'text': content}], json_file, indent=2)
+
+            try:
+                if api_name.lower() == 'openai':
+                    # def summarize_with_openai(api_key, input_data, custom_prompt_arg)
+                    summary = summarize_with_openai(api_key, json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "anthropic":
+                    # def summarize_with_anthropic(api_key, input_data, model, custom_prompt_arg, max_retries=3, retry_delay=5):
+                    summary = summarize_with_anthropic(api_key, json_file_path, article_custom_prompt)
+                elif api_name.lower() == "cohere":
+                    # def summarize_with_cohere(api_key, input_data, model, custom_prompt_arg)
+                    summary = summarize_with_cohere(api_key, json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "groq":
+                    logging.debug(f"MAIN: Trying to summarize with groq")
+                    # def summarize_with_groq(api_key, input_data, model, custom_prompt_arg):
+                    summary = summarize_with_groq(api_key, json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "openrouter":
+                    logging.debug(f"MAIN: Trying to summarize with OpenRouter")
+                    # def summarize_with_openrouter(api_key, input_data, custom_prompt_arg):
+                    summary = summarize_with_openrouter(api_key, json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "deepseek":
+                    logging.debug(f"MAIN: Trying to summarize with DeepSeek")
+                    # def summarize_with_deepseek(api_key, input_data, custom_prompt_arg):
+                    summary = summarize_with_deepseek(api_key, json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "llama.cpp":
+                    logging.debug(f"MAIN: Trying to summarize with Llama.cpp")
+                    # def summarize_with_llama(api_url, file_path, token, custom_prompt)
+                    summary = summarize_with_llama(json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "kobold":
+                    logging.debug(f"MAIN: Trying to summarize with Kobold.cpp")
+                    # def summarize_with_kobold(input_data, kobold_api_token, custom_prompt_input, api_url):
+                    summary = summarize_with_kobold(json_file_path, api_key, article_custom_prompt)
+
+                elif api_name.lower() == "ooba":
+                    # def summarize_with_oobabooga(input_data, api_key, custom_prompt, api_url):
+                    summary = summarize_with_oobabooga(json_file_path, api_key, article_custom_prompt)
+
+                elif api_name.lower() == "tabbyapi":
+                    # def summarize_with_tabbyapi(input_data, tabby_model, custom_prompt_input, api_key=None, api_IP):
+                    summary = summarize_with_tabbyapi(json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "vllm":
+                    logging.debug(f"MAIN: Trying to summarize with VLLM")
+                    # def summarize_with_vllm(api_key, input_data, custom_prompt_input):
+                    summary = summarize_with_vllm(json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "local-llm":
+                    logging.debug(f"MAIN: Trying to summarize with Local LLM")
+                    summary = summarize_with_local_llm(json_file_path, article_custom_prompt)
+
+                elif api_name.lower() == "huggingface":
+                    logging.debug(f"MAIN: Trying to summarize with huggingface")
+                    # def summarize_with_huggingface(api_key, input_data, custom_prompt_arg):
+                    summarize_with_huggingface(api_key, json_file_path, article_custom_prompt)
+                # Add additional API handlers here...
+            except requests.exceptions.ConnectionError as e:
+                logging.error(f"Connection error while trying to summarize with {api_name}: {str(e)}")
+
+            if summary:
+                logging.info(f"Article_Summarizer: Summary generated using {api_name} API")
+                save_summary_to_file(summary, json_file_path)
+            else:
+                summary = "Summary not available"
+                logging.warning(f"Failed to generate summary using {api_name} API")
+
+        else:
+            summary = "Article Summarization: No API provided for summarization."
+
+        print(f"Summary: {summary}")  # Debugging statement
+
+        # Step 3: Ingest the article into the database
+        ingestion_result = ingest_article_to_db(url, title, author, content, keywords, summary, ingestion_date,
+                                                article_custom_prompt)
+
+        return f"Title: {title}\nAuthor: {author}\nIngestion Result: {ingestion_result}\n\nSummary: {summary}\n\nArticle Contents: {content}"
+    except Exception as e:
+        logging.error(f"Error processing URL {url}: {str(e)}")
+        return f"Failed to process URL {url}: {str(e)}"
+
+
+def ingest_unstructured_text(text, custom_prompt, api_name, api_key, keywords, custom_article_title):
+    title = custom_article_title.strip() if custom_article_title else "Unstructured Text"
+    author = "Unknown"
+    ingestion_date = datetime.now().strftime('%Y-%m-%d')
+
+    # Summarize the unstructured text
+    if api_name:
+        json_file_path = f"Results/{title.replace(' ', '_')}_segments.json"
+        with open(json_file_path, 'w') as json_file:
+            json.dump([{'text': text}], json_file, indent=2)
+
+        if api_name.lower() == 'openai':
+            summary = summarize_with_openai(api_key, json_file_path, custom_prompt)
+        # Add other APIs as needed
+        else:
+            summary = "Unsupported API."
+    else:
+        summary = "No API provided for summarization."
+
+    # Ingest the unstructured text into the database
+    ingestion_result = ingest_article_to_db('Unstructured Text', title, author, text, keywords, summary, ingestion_date,
+                                            custom_prompt)
+    return f"Title: {title}\nSummary: {summary}\nIngestion Result: {ingestion_result}"
+
+
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/Audio_Files.py

@@ -0,0 +1,629 @@
+# Audio_Files.py
+#########################################
+# Audio Processing Library
+# This library is used to download or load audio files from a local directory.
+#
+####
+#
+# Functions:
+#
+# download_audio_file(url, save_path)
+# process_audio(
+# process_audio_file(audio_url, audio_file, whisper_model="small.en", api_name=None, api_key=None)
+#
+#
+#########################################
+# Imports
+import json
+import logging
+import subprocess
+import sys
+import tempfile
+import uuid
+from datetime import datetime
+
+import requests
+import os
+from gradio import gradio
+import yt_dlp
+
+from App_Function_Libraries.Audio_Transcription_Lib import speech_to_text
+from App_Function_Libraries.Chunk_Lib import improved_chunking_process
+#
+# Local Imports
+from App_Function_Libraries.SQLite_DB import add_media_to_database, add_media_with_keywords
+from App_Function_Libraries.Utils import create_download_directory, save_segments_to_json
+from App_Function_Libraries.Summarization_General_Lib import save_transcription_and_summary, perform_transcription, \
+    perform_summarization
+from App_Function_Libraries.Video_DL_Ingestion_Lib import extract_metadata
+
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+MAX_FILE_SIZE = 500 * 1024 * 1024
+
+
+def download_audio_file(url, use_cookies=False, cookies=None):
+    try:
+        # Set up the request headers
+        headers = {}
+        if use_cookies and cookies:
+            try:
+                cookie_dict = json.loads(cookies)
+                headers['Cookie'] = '; '.join([f'{k}={v}' for k, v in cookie_dict.items()])
+            except json.JSONDecodeError:
+                logging.warning("Invalid cookie format. Proceeding without cookies.")
+
+        # Make the request
+        response = requests.get(url, headers=headers, stream=True)
+        response.raise_for_status()  # Raise an exception for bad status codes
+
+        # Get the file size
+        file_size = int(response.headers.get('content-length', 0))
+        if file_size > 500 * 1024 * 1024:  # 500 MB limit
+            raise ValueError("File size exceeds the 500MB limit.")
+
+        # Generate a unique filename
+        file_name = f"audio_{uuid.uuid4().hex[:8]}.mp3"
+        save_path = os.path.join('downloads', file_name)
+
+        # Ensure the downloads directory exists
+        os.makedirs('downloads', exist_ok=True)
+
+        # Download the file
+        with open(save_path, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+
+        logging.info(f"Audio file downloaded successfully: {save_path}")
+        return save_path
+
+    except requests.RequestException as e:
+        logging.error(f"Error downloading audio file: {str(e)}")
+        raise
+    except ValueError as e:
+        logging.error(str(e))
+        raise
+    except Exception as e:
+        logging.error(f"Unexpected error downloading audio file: {str(e)}")
+        raise
+
+
+def process_audio(
+        audio_file_path,
+        num_speakers=2,
+        whisper_model="small.en",
+        custom_prompt_input=None,
+        offset=0,
+        api_name=None,
+        api_key=None,
+        vad_filter=False,
+        rolling_summarization=False,
+        detail_level=0.01,
+        keywords="default,no_keyword_set",
+        chunk_text_by_words=False,
+        max_words=0,
+        chunk_text_by_sentences=False,
+        max_sentences=0,
+        chunk_text_by_paragraphs=False,
+        max_paragraphs=0,
+        chunk_text_by_tokens=False,
+        max_tokens=0
+):
+    try:
+
+        # Perform transcription
+        audio_file_path, segments = perform_transcription(audio_file_path, offset, whisper_model, vad_filter)
+
+        if audio_file_path is None or segments is None:
+            logging.error("Process_Audio: Transcription failed or segments not available.")
+            return "Process_Audio: Transcription failed.", None, None, None, None, None
+
+        logging.debug(f"Process_Audio: Transcription audio_file: {audio_file_path}")
+        logging.debug(f"Process_Audio: Transcription segments: {segments}")
+
+        transcription_text = {'audio_file': audio_file_path, 'transcription': segments}
+        logging.debug(f"Process_Audio: Transcription text: {transcription_text}")
+
+        # Save segments to JSON
+        segments_json_path = save_segments_to_json(segments)
+
+        # Perform summarization
+        summary_text = None
+        if api_name:
+            if rolling_summarization is not None:
+                pass
+                # FIXME rolling summarization
+                # summary_text = rolling_summarize_function(
+                #     transcription_text,
+                #     detail=detail_level,
+                #     api_name=api_name,
+                #     api_key=api_key,
+                #     custom_prompt=custom_prompt_input,
+                #     chunk_by_words=chunk_text_by_words,
+                #     max_words=max_words,
+                #     chunk_by_sentences=chunk_text_by_sentences,
+                #     max_sentences=max_sentences,
+                #     chunk_by_paragraphs=chunk_text_by_paragraphs,
+                #     max_paragraphs=max_paragraphs,
+                #     chunk_by_tokens=chunk_text_by_tokens,
+                #     max_tokens=max_tokens
+                # )
+            else:
+                summary_text = perform_summarization(api_name, segments_json_path, custom_prompt_input, api_key)
+
+            if summary_text is None:
+                logging.error("Summary text is None. Check summarization function.")
+                summary_file_path = None
+        else:
+            summary_text = 'Summary not available'
+            summary_file_path = None
+
+        # Save transcription and summary
+        download_path = create_download_directory("Audio_Processing")
+        json_file_path, summary_file_path = save_transcription_and_summary(transcription_text, summary_text,
+                                                                           download_path)
+
+        # Update function call to add_media_to_database so that it properly applies the title, author and file type
+        # Add to database
+        add_media_to_database(None, {'title': 'Audio File', 'author': 'Unknown'}, segments, summary_text, keywords,
+                              custom_prompt_input, whisper_model)
+
+        return transcription_text, summary_text, json_file_path, summary_file_path, None, None
+
+    except Exception as e:
+        logging.error(f"Error in process_audio: {str(e)}")
+        return str(e), None, None, None, None, None
+
+
+def process_single_audio(audio_file_path, whisper_model, api_name, api_key, keep_original,custom_keywords, source,
+                         custom_prompt_input, chunk_method, max_chunk_size, chunk_overlap, use_adaptive_chunking,
+                         use_multi_level_chunking, chunk_language):
+    progress = []
+    transcription = ""
+    summary = ""
+
+    def update_progress(message):
+        progress.append(message)
+        return "\n".join(progress)
+
+    try:
+        # Check file size before processing
+        file_size = os.path.getsize(audio_file_path)
+        if file_size > MAX_FILE_SIZE:
+            update_progress(f"File size ({file_size / (1024 * 1024):.2f} MB) exceeds the maximum limit of {MAX_FILE_SIZE / (1024 * 1024):.2f} MB. Skipping this file.")
+            return "\n".join(progress), "", ""
+
+        # Perform transcription
+        update_progress("Starting transcription...")
+        segments = speech_to_text(audio_file_path, whisper_model=whisper_model)
+        transcription = " ".join([segment['Text'] for segment in segments])
+        update_progress("Audio transcribed successfully.")
+
+        # Perform summarization if API is provided
+        if api_name and api_key:
+            update_progress("Starting summarization...")
+            summary = perform_summarization(api_name, transcription, "Summarize the following audio transcript",
+                                            api_key)
+            update_progress("Audio summarized successfully.")
+        else:
+            summary = "No summary available"
+
+        # Prepare keywords
+        keywords = "audio,transcription"
+        if custom_keywords:
+            keywords += f",{custom_keywords}"
+
+        # Add to database
+        add_media_with_keywords(
+            url=source,
+            title=os.path.basename(audio_file_path),
+            media_type='audio',
+            content=transcription,
+            keywords=keywords,
+            prompt="Summarize the following audio transcript",
+            summary=summary,
+            transcription_model=whisper_model,
+            author="Unknown",
+            ingestion_date=None  # This will use the current date
+        )
+        update_progress("Audio file added to database successfully.")
+
+        if not keep_original and source != "Uploaded File":
+            os.remove(audio_file_path)
+            update_progress(f"Temporary file {audio_file_path} removed.")
+        elif keep_original and source != "Uploaded File":
+            update_progress(f"Original audio file kept at: {audio_file_path}")
+
+    except Exception as e:
+        update_progress(f"Error processing {source}: {str(e)}")
+        transcription = f"Error: {str(e)}"
+        summary = "No summary due to error"
+
+    return "\n".join(progress), transcription, summary
+
+
+def process_audio_files(audio_urls, audio_file, whisper_model, api_name, api_key, use_cookies, cookies, keep_original,
+                        custom_keywords, custom_prompt_input, chunk_method, max_chunk_size, chunk_overlap,
+                        use_adaptive_chunking, use_multi_level_chunking, chunk_language, diarize):
+    progress = []
+    temp_files = []
+    all_transcriptions = []
+    all_summaries = []
+
+    def update_progress(message):
+        progress.append(message)
+        return "\n".join(progress)
+
+    def cleanup_files():
+        for file in temp_files:
+            try:
+                if os.path.exists(file):
+                    os.remove(file)
+                    update_progress(f"Temporary file {file} removed.")
+            except Exception as e:
+                update_progress(f"Failed to remove temporary file {file}: {str(e)}")
+
+    def reencode_mp3(mp3_file_path):
+        try:
+            reencoded_mp3_path = mp3_file_path.replace(".mp3", "_reencoded.mp3")
+            subprocess.run([ffmpeg_cmd, '-i', mp3_file_path, '-codec:a', 'libmp3lame', reencoded_mp3_path], check=True)
+            update_progress(f"Re-encoded {mp3_file_path} to {reencoded_mp3_path}.")
+            return reencoded_mp3_path
+        except subprocess.CalledProcessError as e:
+            update_progress(f"Error re-encoding {mp3_file_path}: {str(e)}")
+            raise
+
+    def convert_mp3_to_wav(mp3_file_path):
+        try:
+            wav_file_path = mp3_file_path.replace(".mp3", ".wav")
+            subprocess.run([ffmpeg_cmd, '-i', mp3_file_path, wav_file_path], check=True)
+            update_progress(f"Converted {mp3_file_path} to {wav_file_path}.")
+            return wav_file_path
+        except subprocess.CalledProcessError as e:
+            update_progress(f"Error converting {mp3_file_path} to WAV: {str(e)}")
+            raise
+
+    try:
+        # Check and set the ffmpeg command
+        global ffmpeg_cmd
+        if os.name == "nt":
+            logging.debug("Running on Windows")
+            ffmpeg_cmd = os.path.join(os.getcwd(), "Bin", "ffmpeg.exe")
+        else:
+            ffmpeg_cmd = 'ffmpeg'  # Assume 'ffmpeg' is in PATH for non-Windows systems
+
+        # Ensure ffmpeg is accessible
+        if not os.path.exists(ffmpeg_cmd) and os.name == "nt":
+            raise FileNotFoundError(f"ffmpeg executable not found at path: {ffmpeg_cmd}")
+
+        # Define chunk options early to avoid undefined errors
+        chunk_options = {
+            'method': chunk_method,
+            'max_size': max_chunk_size,
+            'overlap': chunk_overlap,
+            'adaptive': use_adaptive_chunking,
+            'multi_level': use_multi_level_chunking,
+            'language': chunk_language
+        }
+
+        # Process multiple URLs
+        urls = [url.strip() for url in audio_urls.split('\n') if url.strip()]
+
+        for i, url in enumerate(urls):
+            update_progress(f"Processing URL {i + 1}/{len(urls)}: {url}")
+
+            # Download and process audio file
+            audio_file_path = download_audio_file(url, use_cookies, cookies)
+            if not os.path.exists(audio_file_path):
+                update_progress(f"Downloaded file not found: {audio_file_path}")
+                continue
+
+            temp_files.append(audio_file_path)
+            update_progress("Audio file downloaded successfully.")
+
+            # Re-encode MP3 to fix potential issues
+            reencoded_mp3_path = reencode_mp3(audio_file_path)
+            if not os.path.exists(reencoded_mp3_path):
+                update_progress(f"Re-encoded file not found: {reencoded_mp3_path}")
+                continue
+
+            temp_files.append(reencoded_mp3_path)
+
+            # Convert re-encoded MP3 to WAV
+            wav_file_path = convert_mp3_to_wav(reencoded_mp3_path)
+            if not os.path.exists(wav_file_path):
+                update_progress(f"Converted WAV file not found: {wav_file_path}")
+                continue
+
+            temp_files.append(wav_file_path)
+
+            # Initialize transcription
+            transcription = ""
+
+            # Transcribe audio
+            if diarize:
+                segments = speech_to_text(wav_file_path, whisper_model=whisper_model, diarize=True)
+            else:
+                segments = speech_to_text(wav_file_path, whisper_model=whisper_model)
+
+            # Handle segments nested under 'segments' key
+            if isinstance(segments, dict) and 'segments' in segments:
+                segments = segments['segments']
+
+            if isinstance(segments, list):
+                transcription = " ".join([segment.get('Text', '') for segment in segments])
+                update_progress("Audio transcribed successfully.")
+            else:
+                update_progress("Unexpected segments format received from speech_to_text.")
+                logging.error(f"Unexpected segments format: {segments}")
+                continue
+
+            if not transcription.strip():
+                update_progress("Transcription is empty.")
+            else:
+                # Apply chunking
+                chunked_text = improved_chunking_process(transcription, chunk_options)
+
+                # Summarize
+                if api_name:
+                    try:
+                        summary = perform_summarization(api_name, chunked_text, custom_prompt_input, api_key)
+                        update_progress("Audio summarized successfully.")
+                    except Exception as e:
+                        logging.error(f"Error during summarization: {str(e)}")
+                        summary = "Summary generation failed"
+                else:
+                    summary = "No summary available (API not provided)"
+
+                all_transcriptions.append(transcription)
+                all_summaries.append(summary)
+
+                # Add to database
+                add_media_with_keywords(
+                    url=url,
+                    title=os.path.basename(wav_file_path),
+                    media_type='audio',
+                    content=transcription,
+                    keywords=custom_keywords,
+                    prompt=custom_prompt_input,
+                    summary=summary,
+                    transcription_model=whisper_model,
+                    author="Unknown",
+                    ingestion_date=datetime.now().strftime('%Y-%m-%d')
+                )
+                update_progress("Audio file processed and added to database.")
+
+        # Process uploaded file if provided
+        if audio_file:
+            if os.path.getsize(audio_file.name) > MAX_FILE_SIZE:
+                update_progress(
+                    f"Uploaded file size exceeds the maximum limit of {MAX_FILE_SIZE / (1024 * 1024):.2f}MB. Skipping this file.")
+            else:
+                # Re-encode MP3 to fix potential issues
+                reencoded_mp3_path = reencode_mp3(audio_file.name)
+                if not os.path.exists(reencoded_mp3_path):
+                    update_progress(f"Re-encoded file not found: {reencoded_mp3_path}")
+                    return update_progress("Processing failed: Re-encoded file not found"), "", ""
+
+                temp_files.append(reencoded_mp3_path)
+
+                # Convert re-encoded MP3 to WAV
+                wav_file_path = convert_mp3_to_wav(reencoded_mp3_path)
+                if not os.path.exists(wav_file_path):
+                    update_progress(f"Converted WAV file not found: {wav_file_path}")
+                    return update_progress("Processing failed: Converted WAV file not found"), "", ""
+
+                temp_files.append(wav_file_path)
+
+                # Initialize transcription
+                transcription = ""
+
+                if diarize:
+                    segments = speech_to_text(wav_file_path, whisper_model=whisper_model, diarize=True)
+                else:
+                    segments = speech_to_text(wav_file_path, whisper_model=whisper_model)
+
+                # Handle segments nested under 'segments' key
+                if isinstance(segments, dict) and 'segments' in segments:
+                    segments = segments['segments']
+
+                if isinstance(segments, list):
+                    transcription = " ".join([segment.get('Text', '') for segment in segments])
+                else:
+                    update_progress("Unexpected segments format received from speech_to_text.")
+                    logging.error(f"Unexpected segments format: {segments}")
+
+                chunked_text = improved_chunking_process(transcription, chunk_options)
+
+                if api_name and api_key:
+                    try:
+                        summary = perform_summarization(api_name, chunked_text, custom_prompt_input, api_key)
+                        update_progress("Audio summarized successfully.")
+                    except Exception as e:
+                        logging.error(f"Error during summarization: {str(e)}")
+                        summary = "Summary generation failed"
+                else:
+                    summary = "No summary available (API not provided)"
+
+                all_transcriptions.append(transcription)
+                all_summaries.append(summary)
+
+                add_media_with_keywords(
+                    url="Uploaded File",
+                    title=os.path.basename(wav_file_path),
+                    media_type='audio',
+                    content=transcription,
+                    keywords=custom_keywords,
+                    prompt=custom_prompt_input,
+                    summary=summary,
+                    transcription_model=whisper_model,
+                    author="Unknown",
+                    ingestion_date=datetime.now().strftime('%Y-%m-%d')
+                )
+                update_progress("Uploaded file processed and added to database.")
+
+        # Final cleanup
+        if not keep_original:
+            cleanup_files()
+
+        final_progress = update_progress("All processing complete.")
+        final_transcriptions = "\n\n".join(all_transcriptions)
+        final_summaries = "\n\n".join(all_summaries)
+
+        return final_progress, final_transcriptions, final_summaries
+
+    except Exception as e:
+        logging.error(f"Error processing audio files: {str(e)}")
+        cleanup_files()
+        return update_progress(f"Processing failed: {str(e)}"), "", ""
+
+
+def download_youtube_audio(url: str) -> str:
+    ydl_opts = {
+        'format': 'bestaudio/best',
+        'postprocessors': [{
+            'key': 'FFmpegExtractAudio',
+            'preferredcodec': 'wav',
+            'preferredquality': '192',
+        }],
+        'outtmpl': '%(title)s.%(ext)s'
+    }
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info = ydl.extract_info(url, download=True)
+        filename = ydl.prepare_filename(info)
+        return filename.rsplit('.', 1)[0] + '.wav'
+
+
+def process_podcast(url, title, author, keywords, custom_prompt, api_name, api_key, whisper_model,
+                    keep_original=False, enable_diarization=False, use_cookies=False, cookies=None,
+                    chunk_method=None, max_chunk_size=300, chunk_overlap=0, use_adaptive_chunking=False,
+                    use_multi_level_chunking=False, chunk_language='english'):
+    progress = []
+    error_message = ""
+    temp_files = []
+
+    def update_progress(message):
+        progress.append(message)
+        return "\n".join(progress)
+
+    def cleanup_files():
+        if not keep_original:
+            for file in temp_files:
+                try:
+                    if os.path.exists(file):
+                        os.remove(file)
+                        update_progress(f"Temporary file {file} removed.")
+                except Exception as e:
+                    update_progress(f"Failed to remove temporary file {file}: {str(e)}")
+
+    try:
+        # Download podcast
+        audio_file = download_audio_file(url, use_cookies, cookies)
+        temp_files.append(audio_file)
+        update_progress("Podcast downloaded successfully.")
+
+        # Extract metadata
+        metadata = extract_metadata(url)
+        title = title or metadata.get('title', 'Unknown Podcast')
+        author = author or metadata.get('uploader', 'Unknown Author')
+
+        # Format metadata for storage
+        metadata_text = f"""
+Metadata:
+Title: {title}
+Author: {author}
+Series: {metadata.get('series', 'N/A')}
+Episode: {metadata.get('episode', 'N/A')}
+Season: {metadata.get('season', 'N/A')}
+Upload Date: {metadata.get('upload_date', 'N/A')}
+Duration: {metadata.get('duration', 'N/A')} seconds
+Description: {metadata.get('description', 'N/A')}
+"""
+
+        # Update keywords
+        new_keywords = []
+        if metadata.get('series'):
+            new_keywords.append(f"series:{metadata['series']}")
+        if metadata.get('episode'):
+            new_keywords.append(f"episode:{metadata['episode']}")
+        if metadata.get('season'):
+            new_keywords.append(f"season:{metadata['season']}")
+
+        keywords = f"{keywords},{','.join(new_keywords)}" if keywords else ','.join(new_keywords)
+
+        update_progress(f"Metadata extracted - Title: {title}, Author: {author}, Keywords: {keywords}")
+
+        # Transcribe the podcast
+        try:
+            if enable_diarization:
+                segments = speech_to_text(audio_file, whisper_model=whisper_model, diarize=True)
+            else:
+                segments = speech_to_text(audio_file, whisper_model=whisper_model)
+            transcription = " ".join([segment['Text'] for segment in segments])
+            update_progress("Podcast transcribed successfully.")
+        except Exception as e:
+            error_message = f"Transcription failed: {str(e)}"
+            raise
+
+        # Apply chunking
+        chunk_options = {
+            'method': chunk_method,
+            'max_size': max_chunk_size,
+            'overlap': chunk_overlap,
+            'adaptive': use_adaptive_chunking,
+            'multi_level': use_multi_level_chunking,
+            'language': chunk_language
+        }
+        chunked_text = improved_chunking_process(transcription, chunk_options)
+
+        # Combine metadata and transcription
+        full_content = metadata_text + "\n\nTranscription:\n" + transcription
+
+        # Summarize if API is provided
+        summary = None
+        if api_name and api_key:
+            try:
+                summary = perform_summarization(api_name, chunked_text, custom_prompt, api_key)
+                update_progress("Podcast summarized successfully.")
+            except Exception as e:
+                error_message = f"Summarization failed: {str(e)}"
+                raise
+
+        # Add to database
+        try:
+            add_media_with_keywords(
+                url=url,
+                title=title,
+                media_type='podcast',
+                content=full_content,
+                keywords=keywords,
+                prompt=custom_prompt,
+                summary=summary or "No summary available",
+                transcription_model=whisper_model,
+                author=author,
+                ingestion_date=datetime.now().strftime('%Y-%m-%d')
+            )
+            update_progress("Podcast added to database successfully.")
+        except Exception as e:
+            error_message = f"Error adding podcast to database: {str(e)}"
+            raise
+
+        # Cleanup
+        cleanup_files()
+
+        return (update_progress("Processing complete."), full_content, summary or "No summary generated.",
+                title, author, keywords, error_message)
+
+    except Exception as e:
+        logging.error(f"Error processing podcast: {str(e)}")
+        cleanup_files()
+        return update_progress(f"Processing failed: {str(e)}"), "", "", "", "", "", str(e)
+
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/Audio_Transcription_Lib.py

@@ -0,0 +1,158 @@
+# Audio_Transcription_Lib.py
+#########################################
+# Transcription Library
+# This library is used to perform transcription of audio files.
+# Currently, uses faster_whisper for transcription.
+#
+####
+import configparser
+####################
+# Function List
+#
+# 1. convert_to_wav(video_file_path, offset=0, overwrite=False)
+# 2. speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='small.en', vad_filter=False)
+#
+####################
+#
+# Import necessary libraries to run solo for testing
+import json
+import logging
+import os
+import sys
+import subprocess
+import time
+
+# Import Local
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+# Convert video .m4a into .wav using ffmpeg
+#   ffmpeg -i "example.mp4" -ar 16000 -ac 1 -c:a pcm_s16le "output.wav"
+#       https://www.gyan.dev/ffmpeg/builds/
+#
+
+
+# os.system(r'.\Bin\ffmpeg.exe -ss 00:00:00 -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{out_path}"')
+def convert_to_wav(video_file_path, offset=0, overwrite=False):
+    out_path = os.path.splitext(video_file_path)[0] + ".wav"
+
+    if os.path.exists(out_path) and not overwrite:
+        print(f"File '{out_path}' already exists. Skipping conversion.")
+        logging.info(f"Skipping conversion as file already exists: {out_path}")
+        return out_path
+    print("Starting conversion process of .m4a to .WAV")
+    out_path = os.path.splitext(video_file_path)[0] + ".wav"
+
+    try:
+        if os.name == "nt":
+            logging.debug("ffmpeg being ran on windows")
+
+            if sys.platform.startswith('win'):
+                ffmpeg_cmd = ".\\Bin\\ffmpeg.exe"
+                logging.debug(f"ffmpeg_cmd: {ffmpeg_cmd}")
+            else:
+                ffmpeg_cmd = 'ffmpeg'  # Assume 'ffmpeg' is in PATH for non-Windows systems
+
+            command = [
+                ffmpeg_cmd,  # Assuming the working directory is correctly set where .\Bin exists
+                "-ss", "00:00:00",  # Start at the beginning of the video
+                "-i", video_file_path,
+                "-ar", "16000",  # Audio sample rate
+                "-ac", "1",  # Number of audio channels
+                "-c:a", "pcm_s16le",  # Audio codec
+                out_path
+            ]
+            try:
+                # Redirect stdin from null device to prevent ffmpeg from waiting for input
+                with open(os.devnull, 'rb') as null_file:
+                    result = subprocess.run(command, stdin=null_file, text=True, capture_output=True)
+                if result.returncode == 0:
+                    logging.info("FFmpeg executed successfully")
+                    logging.debug("FFmpeg output: %s", result.stdout)
+                else:
+                    logging.error("Error in running FFmpeg")
+                    logging.error("FFmpeg stderr: %s", result.stderr)
+                    raise RuntimeError(f"FFmpeg error: {result.stderr}")
+            except Exception as e:
+                logging.error("Error occurred - ffmpeg doesn't like windows")
+                raise RuntimeError("ffmpeg failed")
+        elif os.name == "posix":
+            os.system(f'ffmpeg -ss 00:00:00 -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{out_path}"')
+        else:
+            raise RuntimeError("Unsupported operating system")
+        logging.info("Conversion to WAV completed: %s", out_path)
+    except subprocess.CalledProcessError as e:
+        logging.error("Error executing FFmpeg command: %s", str(e))
+        raise RuntimeError("Error converting video file to WAV")
+    except Exception as e:
+        logging.error("speech-to-text: Error transcribing audio: %s", str(e))
+        return {"error": str(e)}
+    return out_path
+
+
+# Transcribe .wav into .segments.json
+def speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='medium.en', vad_filter=False, diarize=False):
+    logging.info('speech-to-text: Loading faster_whisper model: %s', whisper_model)
+    from faster_whisper import WhisperModel
+    # Retrieve processing choice from the configuration file
+    config = configparser.ConfigParser()
+    config.read('config.txt')
+    processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')
+    model = WhisperModel(whisper_model, device=f"{processing_choice}")
+    time_start = time.time()
+    if audio_file_path is None:
+        raise ValueError("speech-to-text: No audio file provided")
+    logging.info("speech-to-text: Audio file path: %s", audio_file_path)
+
+    try:
+        _, file_ending = os.path.splitext(audio_file_path)
+        out_file = audio_file_path.replace(file_ending, ".segments.json")
+        prettified_out_file = audio_file_path.replace(file_ending, ".segments_pretty.json")
+        if os.path.exists(out_file):
+            logging.info("speech-to-text: Segments file already exists: %s", out_file)
+            with open(out_file) as f:
+                global segments
+                segments = json.load(f)
+            return segments
+
+        logging.info('speech-to-text: Starting transcription...')
+        options = dict(language=selected_source_lang, beam_size=5, best_of=5, vad_filter=vad_filter)
+        transcribe_options = dict(task="transcribe", **options)
+        segments_raw, info = model.transcribe(audio_file_path, **transcribe_options)
+
+        segments = []
+        for segment_chunk in segments_raw:
+            chunk = {
+                "Time_Start": segment_chunk.start,
+                "Time_End": segment_chunk.end,
+                "Text": segment_chunk.text
+            }
+            logging.debug("Segment: %s", chunk)
+            segments.append(chunk)
+        if not segments:
+            raise RuntimeError("No transcription produced. The audio file may be invalid or empty.")
+        logging.info("speech-to-text: Transcription completed in %.2f seconds", time.time() - time_start)
+
+        # Create a dictionary with the 'segments' key
+        output_data = {'segments': segments}
+
+        # Save prettified JSON
+        logging.info("speech-to-text: Saving prettified JSON to %s", prettified_out_file)
+        with open(prettified_out_file, 'w') as f:
+            json.dump(output_data, f, indent=2)
+
+        # Save non-prettified JSON
+        logging.info("speech-to-text: Saving JSON to %s", out_file)
+        with open(out_file, 'w') as f:
+            json.dump(output_data, f)
+
+    except Exception as e:
+        logging.error("speech-to-text: Error transcribing audio: %s", str(e))
+        raise RuntimeError("speech-to-text: Error transcribing audio")
+    return segments
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/Book_Ingestion_Lib.py

@@ -0,0 +1,95 @@
+# Book_Ingestion_Lib.py
+#########################################
+# Library to hold functions for ingesting book files.#
+#
+####################
+# Function List
+#
+# 1. ingest_text_file(file_path, title=None, author=None, keywords=None):
+# 2.
+#
+#
+####################
+
+
+# Import necessary libraries
+import os
+import re
+from datetime import datetime
+import logging
+
+
+# Import Local
+from SQLite_DB import add_media_with_keywords
+
+#######################################################################################################################
+# Function Definitions
+#
+
+# Ingest a text file into the database with Title/Author/Keywords
+
+def extract_epub_metadata(content):
+    title_match = re.search(r'Title:\s*(.*?)\n', content)
+    author_match = re.search(r'Author:\s*(.*?)\n', content)
+
+    title = title_match.group(1) if title_match else None
+    author = author_match.group(1) if author_match else None
+
+    return title, author
+
+
+def ingest_text_file(file_path, title=None, author=None, keywords=None):
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            content = file.read()
+
+        # Check if it's a converted epub and extract metadata if so
+        if 'epub_converted' in (keywords or ''):
+            extracted_title, extracted_author = extract_epub_metadata(content)
+            title = title or extracted_title
+            author = author or extracted_author
+
+        # If title is still not provided, use the filename without extension
+        if not title:
+            title = os.path.splitext(os.path.basename(file_path))[0]
+
+        # If author is still not provided, set it to 'Unknown'
+        if not author:
+            author = 'Unknown'
+
+        # If keywords are not provided, use a default keyword
+        if not keywords:
+            keywords = 'text_file,epub_converted'
+        else:
+            keywords = f'text_file,epub_converted,{keywords}'
+
+        # Add the text file to the database
+        add_media_with_keywords(
+            url=file_path,
+            title=title,
+            media_type='document',
+            content=content,
+            keywords=keywords,
+            prompt='No prompt for text files',
+            summary='No summary for text files',
+            transcription_model='None',
+            author=author,
+            ingestion_date=datetime.now().strftime('%Y-%m-%d')
+        )
+
+        return f"Text file '{title}' by {author} ingested successfully."
+    except Exception as e:
+        logging.error(f"Error ingesting text file: {str(e)}")
+        return f"Error ingesting text file: {str(e)}"
+
+
+def ingest_folder(folder_path, keywords=None):
+    results = []
+    for filename in os.listdir(folder_path):
+        if filename.lower().endswith('.txt'):
+            file_path = os.path.join(folder_path, filename)
+            result = ingest_text_file(file_path, keywords=keywords)
+            results.append(result)
+
+
+

App_Function_Libraries/Chunk_Lib.py

@@ -0,0 +1,467 @@
+# Chunk_Lib.py
+#########################################
+# Chunking Library
+# This library is used to perform chunking of input files.
+# Currently, uses naive approaches. Nothing fancy.
+#
+####
+# Import necessary libraries
+import logging
+import re
+
+from typing import List, Optional, Tuple, Dict, Any
+
+from openai import OpenAI
+from tqdm import tqdm
+#
+# Import 3rd party
+from transformers import GPT2Tokenizer
+import nltk
+from nltk.tokenize import sent_tokenize, word_tokenize
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+#
+# Import Local
+from App_Function_Libraries.Tokenization_Methods_Lib import openai_tokenize
+from App_Function_Libraries.Utils import load_comprehensive_config
+
+
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+# FIXME - Make sure it only downloads if it already exists, and does a check first.
+# Ensure NLTK data is downloaded
+def ntlk_prep():
+    nltk.download('punkt')
+
+# Load GPT2 tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+# Load Config file for API keys
+config = load_comprehensive_config()
+openai_api_key = config.get('API', 'openai_api_key', fallback=None)
+
+def load_document(file_path):
+    with open(file_path, 'r') as file:
+        text = file.read()
+    return re.sub('\\s+', ' ', text).strip()
+
+
+def improved_chunking_process(text: str, chunk_options: Dict[str, Any]) -> List[Dict[str, Any]]:
+    chunk_method = chunk_options.get('method', 'words')
+    max_chunk_size = chunk_options.get('max_size', 300)
+    overlap = chunk_options.get('overlap', 0)
+    language = chunk_options.get('language', 'english')
+    adaptive = chunk_options.get('adaptive', False)
+    multi_level = chunk_options.get('multi_level', False)
+
+    if adaptive:
+        max_chunk_size = adaptive_chunk_size(text, max_chunk_size)
+
+    if multi_level:
+        chunks = multi_level_chunking(text, chunk_method, max_chunk_size, overlap, language)
+    else:
+        if chunk_method == 'words':
+            chunks = chunk_text_by_words(text, max_chunk_size, overlap)
+        elif chunk_method == 'sentences':
+            chunks = chunk_text_by_sentences(text, max_chunk_size, overlap, language)
+        elif chunk_method == 'paragraphs':
+            chunks = chunk_text_by_paragraphs(text, max_chunk_size, overlap)
+        elif chunk_method == 'tokens':
+            chunks = chunk_text_by_tokens(text, max_chunk_size, overlap)
+        else:
+            chunks = [text]  # No chunking applied
+
+    return [{'text': chunk, 'metadata': get_chunk_metadata(chunk, text)} for chunk in chunks]
+
+
+def adaptive_chunk_size(text: str, base_size: int) -> int:
+    # Simple adaptive logic: adjust chunk size based on text complexity
+    avg_word_length = sum(len(word) for word in text.split()) / len(text.split())
+    if avg_word_length > 6:  # Arbitrary threshold for "complex" text
+        return int(base_size * 0.8)  # Reduce chunk size for complex text
+    return base_size
+
+
+def multi_level_chunking(text: str, method: str, max_size: int, overlap: int, language: str) -> List[str]:
+    # First level: chunk by paragraphs
+    paragraphs = chunk_text_by_paragraphs(text, max_size * 2, overlap)
+
+    # Second level: chunk each paragraph further
+    chunks = []
+    for para in paragraphs:
+        if method == 'words':
+            chunks.extend(chunk_text_by_words(para, max_size, overlap))
+        elif method == 'sentences':
+            chunks.extend(chunk_text_by_sentences(para, max_size, overlap, language))
+        else:
+            chunks.append(para)
+
+    return chunks
+
+
+def chunk_text_by_words(text: str, max_words: int = 300, overlap: int = 0) -> List[str]:
+    words = text.split()
+    chunks = []
+    for i in range(0, len(words), max_words - overlap):
+        chunk = ' '.join(words[i:i + max_words])
+        chunks.append(chunk)
+    return post_process_chunks(chunks)
+
+
+def chunk_text_by_sentences(text: str, max_sentences: int = 10, overlap: int = 0, language: str = 'english') -> List[
+    str]:
+    nltk.download('punkt', quiet=True)
+    sentences = nltk.sent_tokenize(text, language=language)
+    chunks = []
+    for i in range(0, len(sentences), max_sentences - overlap):
+        chunk = ' '.join(sentences[i:i + max_sentences])
+        chunks.append(chunk)
+    return post_process_chunks(chunks)
+
+
+def chunk_text_by_paragraphs(text: str, max_paragraphs: int = 5, overlap: int = 0) -> List[str]:
+    paragraphs = re.split(r'\n\s*\n', text)
+    chunks = []
+    for i in range(0, len(paragraphs), max_paragraphs - overlap):
+        chunk = '\n\n'.join(paragraphs[i:i + max_paragraphs])
+        chunks.append(chunk)
+    return post_process_chunks(chunks)
+
+
+def chunk_text_by_tokens(text: str, max_tokens: int = 1000, overlap: int = 0) -> List[str]:
+    # This is a simplified token-based chunking. For more accurate tokenization,
+    # consider using a proper tokenizer like GPT-2 TokenizerFast
+    words = text.split()
+    chunks = []
+    current_chunk = []
+    current_token_count = 0
+
+    for word in words:
+        word_token_count = len(word) // 4 + 1  # Rough estimate of token count
+        if current_token_count + word_token_count > max_tokens and current_chunk:
+            chunks.append(' '.join(current_chunk))
+            current_chunk = current_chunk[-overlap:] if overlap > 0 else []
+            current_token_count = sum(len(w) // 4 + 1 for w in current_chunk)
+
+        current_chunk.append(word)
+        current_token_count += word_token_count
+
+    if current_chunk:
+        chunks.append(' '.join(current_chunk))
+
+    return post_process_chunks(chunks)
+
+
+def post_process_chunks(chunks: List[str]) -> List[str]:
+    return [chunk.strip() for chunk in chunks if chunk.strip()]
+
+
+def get_chunk_metadata(chunk: str, full_text: str) -> Dict[str, Any]:
+    start_index = full_text.index(chunk)
+    return {
+        'start_index': start_index,
+        'end_index': start_index + len(chunk),
+        'word_count': len(chunk.split()),
+        'char_count': len(chunk)
+    }
+
+
+# Hybrid approach, chunk each sentence while ensuring total token size does not exceed a maximum number
+def chunk_text_hybrid(text, max_tokens=1000):
+    sentences = nltk.tokenize.sent_tokenize(text)
+    chunks = []
+    current_chunk = []
+    current_length = 0
+
+    for sentence in sentences:
+        tokens = tokenizer.encode(sentence)
+        if current_length + len(tokens) <= max_tokens:
+            current_chunk.append(sentence)
+            current_length += len(tokens)
+        else:
+            chunks.append(' '.join(current_chunk))
+            current_chunk = [sentence]
+            current_length = len(tokens)
+
+    if current_chunk:
+        chunks.append(' '.join(current_chunk))
+
+    return chunks
+
+# Thanks openai
+def chunk_on_delimiter(input_string: str,
+                       max_tokens: int,
+                       delimiter: str) -> List[str]:
+    chunks = input_string.split(delimiter)
+    combined_chunks, _, dropped_chunk_count = combine_chunks_with_no_minimum(
+        chunks, max_tokens, chunk_delimiter=delimiter, add_ellipsis_for_overflow=True)
+    if dropped_chunk_count > 0:
+        print(f"Warning: {dropped_chunk_count} chunks were dropped due to exceeding the token limit.")
+    combined_chunks = [f"{chunk}{delimiter}" for chunk in combined_chunks]
+    return combined_chunks
+
+
+def recursive_summarize_chunks(chunks, summarize_func, custom_prompt):
+    summarized_chunks = []
+    current_summary = ""
+
+    for i, chunk in enumerate(chunks):
+        if i == 0:
+            current_summary = summarize_func(chunk, custom_prompt)
+        else:
+            combined_text = current_summary + "\n\n" + chunk
+            current_summary = summarize_func(combined_text, custom_prompt)
+
+        summarized_chunks.append(current_summary)
+
+    return summarized_chunks
+
+
+# Sample text for testing
+sample_text = """
+Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence 
+concerned with the interactions between computers and human language, in particular how to program computers 
+to process and analyze large amounts of natural language data. The result is a computer capable of "understanding" 
+the contents of documents, including the contextual nuances of the language within them. The technology can then 
+accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
+
+Challenges in natural language processing frequently involve speech recognition, natural language understanding, 
+and natural language generation.
+
+Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled 
+"Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence.
+"""
+
+# Example usage of different chunking methods
+# print("Chunking by words:")
+# print(chunk_text_by_words(sample_text, max_words=50))
+#
+# print("\nChunking by sentences:")
+# print(chunk_text_by_sentences(sample_text, max_sentences=2))
+#
+# print("\nChunking by paragraphs:")
+# print(chunk_text_by_paragraphs(sample_text, max_paragraphs=1))
+#
+# print("\nChunking by tokens:")
+# print(chunk_text_by_tokens(sample_text, max_tokens=50))
+#
+# print("\nHybrid chunking:")
+# print(chunk_text_hybrid(sample_text, max_tokens=50))
+
+
+
+#######################################################################################################################
+#
+# Experimental Semantic Chunking
+#
+
+# Chunk text into segments based on semantic similarity
+def count_units(text, unit='tokens'):
+    if unit == 'words':
+        return len(text.split())
+    elif unit == 'tokens':
+        return len(word_tokenize(text))
+    elif unit == 'characters':
+        return len(text)
+    else:
+        raise ValueError("Invalid unit. Choose 'words', 'tokens', or 'characters'.")
+
+
+def semantic_chunking(text, max_chunk_size=2000, unit='words'):
+    nltk.download('punkt', quiet=True)
+    sentences = sent_tokenize(text)
+    vectorizer = TfidfVectorizer()
+    sentence_vectors = vectorizer.fit_transform(sentences)
+
+    chunks = []
+    current_chunk = []
+    current_size = 0
+
+    for i, sentence in enumerate(sentences):
+        sentence_size = count_units(sentence, unit)
+        if current_size + sentence_size > max_chunk_size and current_chunk:
+            chunks.append(' '.join(current_chunk))
+            overlap_size = count_units(' '.join(current_chunk[-3:]), unit)  # Use last 3 sentences for overlap
+            current_chunk = current_chunk[-3:]  # Keep last 3 sentences for overlap
+            current_size = overlap_size
+
+        current_chunk.append(sentence)
+        current_size += sentence_size
+
+        if i + 1 < len(sentences):
+            current_vector = sentence_vectors[i]
+            next_vector = sentence_vectors[i + 1]
+            similarity = cosine_similarity(current_vector, next_vector)[0][0]
+            if similarity < 0.5 and current_size >= max_chunk_size // 2:
+                chunks.append(' '.join(current_chunk))
+                overlap_size = count_units(' '.join(current_chunk[-3:]), unit)
+                current_chunk = current_chunk[-3:]
+                current_size = overlap_size
+
+    if current_chunk:
+        chunks.append(' '.join(current_chunk))
+
+    return chunks
+
+
+def semantic_chunk_long_file(file_path, max_chunk_size=1000, overlap=100):
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            content = file.read()
+
+        chunks = semantic_chunking(content, max_chunk_size, overlap)
+        return chunks
+    except Exception as e:
+        logging.error(f"Error chunking text file: {str(e)}")
+        return None
+#######################################################################################################################
+
+
+
+
+
+
+#######################################################################################################################
+#
+# OpenAI Rolling Summarization
+#
+
+client = OpenAI(api_key=openai_api_key)
+def get_chat_completion(messages, model='gpt-4-turbo'):
+    response = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=0,
+    )
+    return response.choices[0].message.content
+
+
+# This function combines text chunks into larger blocks without exceeding a specified token count.
+#   It returns the combined chunks, their original indices, and the number of dropped chunks due to overflow.
+def combine_chunks_with_no_minimum(
+        chunks: List[str],
+        max_tokens: int,
+        chunk_delimiter="\n\n",
+        header: Optional[str] = None,
+        add_ellipsis_for_overflow=False,
+) -> Tuple[List[str], List[int]]:
+    dropped_chunk_count = 0
+    output = []  # list to hold the final combined chunks
+    output_indices = []  # list to hold the indices of the final combined chunks
+    candidate = (
+        [] if header is None else [header]
+    )  # list to hold the current combined chunk candidate
+    candidate_indices = []
+    for chunk_i, chunk in enumerate(chunks):
+        chunk_with_header = [chunk] if header is None else [header, chunk]
+        # FIXME MAKE NOT OPENAI SPECIFIC
+        if len(openai_tokenize(chunk_delimiter.join(chunk_with_header))) > max_tokens:
+            print(f"warning: chunk overflow")
+            if (
+                    add_ellipsis_for_overflow
+                    # FIXME MAKE NOT OPENAI SPECIFIC
+                    and len(openai_tokenize(chunk_delimiter.join(candidate + ["..."]))) <= max_tokens
+            ):
+                candidate.append("...")
+                dropped_chunk_count += 1
+            continue  # this case would break downstream assumptions
+        # estimate token count with the current chunk added
+        # FIXME MAKE NOT OPENAI SPECIFIC
+        extended_candidate_token_count = len(openai_tokenize(chunk_delimiter.join(candidate + [chunk])))
+        # If the token count exceeds max_tokens, add the current candidate to output and start a new candidate
+        if extended_candidate_token_count > max_tokens:
+            output.append(chunk_delimiter.join(candidate))
+            output_indices.append(candidate_indices)
+            candidate = chunk_with_header  # re-initialize candidate
+            candidate_indices = [chunk_i]
+        # otherwise keep extending the candidate
+        else:
+            candidate.append(chunk)
+            candidate_indices.append(chunk_i)
+    # add the remaining candidate to output if it's not empty
+    if (header is not None and len(candidate) > 1) or (header is None and len(candidate) > 0):
+        output.append(chunk_delimiter.join(candidate))
+        output_indices.append(candidate_indices)
+    return output, output_indices, dropped_chunk_count
+
+
+def rolling_summarize(text: str,
+                      detail: float = 0,
+                      model: str = 'gpt-4-turbo',
+                      additional_instructions: Optional[str] = None,
+                      minimum_chunk_size: Optional[int] = 500,
+                      chunk_delimiter: str = ".",
+                      summarize_recursively=False,
+                      verbose=False):
+    """
+    Summarizes a given text by splitting it into chunks, each of which is summarized individually.
+    The level of detail in the summary can be adjusted, and the process can optionally be made recursive.
+
+    Parameters:
+        - text (str): The text to be summarized.
+        - detail (float, optional): A value between 0 and 1
+            indicating the desired level of detail in the summary. 0 leads to a higher level summary, and 1 results in a more
+            detailed summary. Defaults to 0.
+        - additional_instructions (Optional[str], optional): Additional instructions to provide to the
+            model for customizing summaries. - minimum_chunk_size (Optional[int], optional): The minimum size for text
+            chunks. Defaults to 500.
+        - chunk_delimiter (str, optional): The delimiter used to split the text into chunks. Defaults to ".".
+        - summarize_recursively (bool, optional): If True, summaries are generated recursively, using previous summaries for context.
+        - verbose (bool, optional): If True, prints detailed information about the chunking process.
+    Returns:
+    - str: The final compiled summary of the text.
+
+    The function first determines the number of chunks by interpolating between a minimum and a maximum chunk count
+    based on the `detail` parameter. It then splits the text into chunks and summarizes each chunk. If
+    `summarize_recursively` is True, each summary is based on the previous summaries, adding more context to the
+    summarization process. The function returns a compiled summary of all chunks.
+    """
+
+    # check detail is set correctly
+    assert 0 <= detail <= 1
+
+    # interpolate the number of chunks based to get specified level of detail
+    max_chunks = len(chunk_on_delimiter(text, minimum_chunk_size, chunk_delimiter))
+    min_chunks = 1
+    num_chunks = int(min_chunks + detail * (max_chunks - min_chunks))
+
+    # adjust chunk_size based on interpolated number of chunks
+    # FIXME MAKE NOT OPENAI SPECIFIC
+    document_length = len(openai_tokenize(text))
+    chunk_size = max(minimum_chunk_size, document_length // num_chunks)
+    text_chunks = chunk_on_delimiter(text, chunk_size, chunk_delimiter)
+    if verbose:
+        print(f"Splitting the text into {len(text_chunks)} chunks to be summarized.")
+        # FIXME MAKE NOT OPENAI SPECIFIC
+        print(f"Chunk lengths are {[len(openai_tokenize(x)) for x in text_chunks]}")
+
+    # set system message - FIXME
+    system_message_content = "Rewrite this text in summarized form."
+    if additional_instructions is not None:
+        system_message_content += f"\n\n{additional_instructions}"
+
+    accumulated_summaries = []
+    for i, chunk in enumerate(tqdm(text_chunks)):
+        if summarize_recursively and accumulated_summaries:
+            # Combine previous summary with current chunk for recursive summarization
+            combined_text = accumulated_summaries[-1] + "\n\n" + chunk
+            user_message_content = f"Previous summary and new content to summarize:\n\n{combined_text}"
+        else:
+            user_message_content = chunk
+
+        messages = [
+            {"role": "system", "content": system_message_content},
+            {"role": "user", "content": user_message_content}
+        ]
+
+        response = get_chat_completion(messages, model=model)
+        accumulated_summaries.append(response)
+
+    final_summary = '\n\n'.join(accumulated_summaries)
+    return final_summary
+
+
+

App_Function_Libraries/Diarization_Lib.py

@@ -0,0 +1,177 @@
+# Diarization_Lib.py
+#########################################
+# Diarization Library
+# This library is used to perform diarization of audio files.
+# Currently, uses FIXME for transcription.
+#
+####################
+####################
+# Function List
+#
+# 1. speaker_diarize(video_file_path, segments, embedding_model = "pyannote/embedding", embedding_size=512, num_speakers=0)
+#
+####################
+# Import necessary libraries
+import configparser
+import json
+import logging
+import os
+from pathlib import Path
+import time
+# Import Local
+from App_Function_Libraries.Audio_Transcription_Lib import speech_to_text
+#
+# Import 3rd Party
+from pyannote.audio import Model
+from pyannote.audio.pipelines.speaker_diarization import SpeakerDiarization
+import torch
+import yaml
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+def load_pipeline_from_pretrained(path_to_config: str | Path) -> SpeakerDiarization:
+    path_to_config = Path(path_to_config).resolve()
+    print(f"Loading pyannote pipeline from {path_to_config}...")
+
+    if not path_to_config.exists():
+        raise FileNotFoundError(f"Config file not found: {path_to_config}")
+
+    # Load the YAML configuration
+    with open(path_to_config, 'r') as config_file:
+        config = yaml.safe_load(config_file)
+
+    # Store current working directory
+    cwd = Path.cwd().resolve()
+
+    # Change to the directory containing the config file
+    cd_to = path_to_config.parent.resolve()
+    print(f"Changing working directory to {cd_to}")
+    os.chdir(cd_to)
+
+    try:
+        # Create a SpeakerDiarization pipeline
+        pipeline = SpeakerDiarization()
+
+        # Load models explicitly from local paths
+        embedding_path = Path(config['pipeline']['params']['embedding']).resolve()
+        segmentation_path = Path(config['pipeline']['params']['segmentation']).resolve()
+
+        if not embedding_path.exists():
+            raise FileNotFoundError(f"Embedding model file not found: {embedding_path}")
+        if not segmentation_path.exists():
+            raise FileNotFoundError(f"Segmentation model file not found: {segmentation_path}")
+
+        # Load the models from local paths using pyannote's Model class
+        pipeline.embedding = Model.from_pretrained(str(embedding_path), map_location=torch.device('cpu'))
+        pipeline.segmentation = Model.from_pretrained(str(segmentation_path), map_location=torch.device('cpu'))
+
+        # Set other parameters
+        pipeline.clustering = config['pipeline']['params']['clustering']
+        pipeline.embedding_batch_size = config['pipeline']['params']['embedding_batch_size']
+        pipeline.embedding_exclude_overlap = config['pipeline']['params']['embedding_exclude_overlap']
+        pipeline.segmentation_batch_size = config['pipeline']['params']['segmentation_batch_size']
+
+        # Set additional parameters
+        pipeline.instantiate(config['params'])
+
+    finally:
+        # Change back to the original working directory
+        print(f"Changing working directory back to {cwd}")
+        os.chdir(cwd)
+
+    return pipeline
+
+def audio_diarization(audio_file_path):
+    logging.info('audio-diarization: Loading pyannote pipeline')
+    config = configparser.ConfigParser()
+    config.read('config.txt')
+    processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')
+
+    base_dir = Path(__file__).parent.resolve()
+    config_path = base_dir / 'models' / 'config.yaml'
+    pipeline = load_pipeline_from_pretrained(config_path)
+
+    time_start = time.time()
+    if audio_file_path is None:
+        raise ValueError("audio-diarization: No audio file provided")
+    logging.info("audio-diarization: Audio file path: %s", audio_file_path)
+
+    try:
+        _, file_ending = os.path.splitext(audio_file_path)
+        out_file = audio_file_path.replace(file_ending, ".diarization.json")
+        prettified_out_file = audio_file_path.replace(file_ending, ".diarization_pretty.json")
+        if os.path.exists(out_file):
+            logging.info("audio-diarization: Diarization file already exists: %s", out_file)
+            with open(out_file) as f:
+                global diarization_result
+                diarization_result = json.load(f)
+            return diarization_result
+
+        logging.info('audio-diarization: Starting diarization...')
+        diarization_result = pipeline(audio_file_path)
+
+        segments = []
+        for turn, _, speaker in diarization_result.itertracks(yield_label=True):
+            chunk = {
+                "Time_Start": turn.start,
+                "Time_End": turn.end,
+                "Speaker": speaker
+            }
+            logging.debug("Segment: %s", chunk)
+            segments.append(chunk)
+        logging.info("audio-diarization: Diarization completed with pyannote")
+
+        output_data = {'segments': segments}
+
+        logging.info("audio-diarization: Saving prettified JSON to %s", prettified_out_file)
+        with open(prettified_out_file, 'w') as f:
+            json.dump(output_data, f, indent=2)
+
+        logging.info("audio-diarization: Saving JSON to %s", out_file)
+        with open(out_file, 'w') as f:
+            json.dump(output_data, f)
+
+    except Exception as e:
+        logging.error("audio-diarization: Error performing diarization: %s", str(e))
+        raise RuntimeError("audio-diarization: Error performing diarization")
+    return segments
+
+def combine_transcription_and_diarization(audio_file_path):
+    logging.info('combine-transcription-and-diarization: Starting transcription and diarization...')
+
+    transcription_result = speech_to_text(audio_file_path)
+
+    diarization_result = audio_diarization(audio_file_path)
+
+    combined_result = []
+    for transcription_segment in transcription_result:
+        for diarization_segment in diarization_result:
+            if transcription_segment['Time_Start'] >= diarization_segment['Time_Start'] and transcription_segment[
+                'Time_End'] <= diarization_segment['Time_End']:
+                combined_segment = {
+                    "Time_Start": transcription_segment['Time_Start'],
+                    "Time_End": transcription_segment['Time_End'],
+                    "Speaker": diarization_segment['Speaker'],
+                    "Text": transcription_segment['Text']
+                }
+                combined_result.append(combined_segment)
+                break
+
+    _, file_ending = os.path.splitext(audio_file_path)
+    out_file = audio_file_path.replace(file_ending, ".combined.json")
+    prettified_out_file = audio_file_path.replace(file_ending, ".combined_pretty.json")
+
+    logging.info("combine-transcription-and-diarization: Saving prettified JSON to %s", prettified_out_file)
+    with open(prettified_out_file, 'w') as f:
+        json.dump(combined_result, f, indent=2)
+
+    logging.info("combine-transcription-and-diarization: Saving JSON to %s", out_file)
+    with open(out_file, 'w') as f:
+        json.dump(combined_result, f)
+
+    return combined_result
+#
+#
+#######################################################################################################################

App_Function_Libraries/LLM_API_Calls.py

@@ -0,0 +1,633 @@
+# Summarization_General_Lib.py
+#########################################
+# General Summarization Library
+# This library is used to perform summarization.
+#
+####
+####################
+# Function List
+#
+# 1. extract_text_from_segments(segments: List[Dict]) -> str
+# 2. chat_with_openai(api_key, file_path, custom_prompt_arg)
+# 3. chat_with_anthropic(api_key, file_path, model, custom_prompt_arg, max_retries=3, retry_delay=5)
+# 4. chat_with_cohere(api_key, file_path, model, custom_prompt_arg)
+# 5. chat_with_groq(api_key, input_data, custom_prompt_arg, system_prompt=None):
+# 6. chat_with_openrouter(api_key, input_data, custom_prompt_arg, system_prompt=None)
+# 7. chat_with_huggingface(api_key, input_data, custom_prompt_arg, system_prompt=None)
+# 8. chat_with_deepseek(api_key, input_data, custom_prompt_arg, system_prompt=None)
+# 9. chat_with_vllm(input_data, custom_prompt_input, api_key=None, vllm_api_url="http://127.0.0.1:8000/v1/chat/completions", system_prompt=None)
+#
+#
+####################
+import json
+# Import necessary libraries
+import os
+import logging
+import time
+import requests
+import configparser
+# Import 3rd-Party Libraries
+from openai import OpenAI
+from requests import RequestException
+# Import Local libraries
+from App_Function_Libraries.Local_Summarization_Lib import openai_api_key, client
+from App_Function_Libraries.Utils import load_and_log_configs
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+def extract_text_from_segments(segments):
+    logging.debug(f"Segments received: {segments}")
+    logging.debug(f"Type of segments: {type(segments)}")
+
+    text = ""
+
+    if isinstance(segments, list):
+        for segment in segments:
+            logging.debug(f"Current segment: {segment}")
+            logging.debug(f"Type of segment: {type(segment)}")
+            if 'Text' in segment:
+                text += segment['Text'] + " "
+            else:
+                logging.warning(f"Skipping segment due to missing 'Text' key: {segment}")
+    else:
+        logging.warning(f"Unexpected type of 'segments': {type(segments)}")
+
+    return text.strip()
+
+
+
+
+
+def chat_with_openai(api_key, input_data, custom_prompt_arg, system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("OpenAI: API key not provided as parameter")
+            logging.info("OpenAI: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['openai']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("OpenAI: API key not found or is empty")
+            return "OpenAI: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"OpenAI: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        logging.debug("OpenAI: Using provided string data for chat input")
+        data = input_data
+
+        logging.debug(f"OpenAI: Loaded data: {data}")
+        logging.debug(f"OpenAI: Type of data: {type(data)}")
+
+        if system_prompt is not None:
+            logging.debug(f"OpenAI: Using provided system prompt:\n\n {system_prompt}")
+            pass
+        else:
+            system_prompt = "You are a helpful assistant"
+            logging.debug(f"OpenAI: Using default system prompt:\n\n {system_prompt}")
+
+        openai_model = loaded_config_data['models']['openai'] or "gpt-4o"
+
+        headers = {
+            'Authorization': f'Bearer {api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug(
+            f"OpenAI API Key: {openai_api_key[:5]}...{openai_api_key[-5:] if openai_api_key else None}")
+        logging.debug("openai: Preparing data + prompt for submittal")
+        openai_prompt = f"{data} \n\n\n\n{custom_prompt_arg}"
+        data = {
+            "model": openai_model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": openai_prompt}
+            ],
+            "max_tokens": 4096,
+            "temperature": 0.1
+        }
+
+        logging.debug("openai: Posting request")
+        response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                chat_response = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("openai: Chat Sent successfully")
+                return chat_response
+            else:
+                logging.warning("openai: Chat response not found in the response data")
+                return "openai: Chat not available"
+        else:
+            logging.error(f"openai: Chat request failed with status code {response.status_code}")
+            logging.error(f"openai: Error response: {response.text}")
+            return f"openai: Failed to process chat request. Status code: {response.status_code}"
+    except Exception as e:
+        logging.error(f"openai: Error in processing: {str(e)}", exc_info=True)
+        return f"openai: Error occurred while processing chat request: {str(e)}"
+
+
+def chat_with_anthropic(api_key, input_data, model, custom_prompt_arg, max_retries=3, retry_delay=5, system_prompt=None):
+    try:
+        loaded_config_data = load_and_log_configs()
+        global anthropic_api_key
+        # API key validation
+        if api_key is None:
+            logging.info("Anthropic: API key not provided as parameter")
+            logging.info("Anthropic: Attempting to use API key from config file")
+            anthropic_api_key = loaded_config_data['api_keys']['anthropic']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("Anthropic: API key not found or is empty")
+            return "Anthropic: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"Anthropic: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        if system_prompt is not None:
+            logging.debug("Anthropic: Using provided system prompt")
+            pass
+        else:
+            system_prompt = "You are a helpful assistant"
+
+        logging.debug(f"AnthropicAI: Loaded data: {input_data}")
+        logging.debug(f"AnthropicAI: Type of data: {type(input_data)}")
+
+        anthropic_model = loaded_config_data['models']['anthropic']
+
+        headers = {
+            'x-api-key': anthropic_api_key,
+            'anthropic-version': '2023-06-01',
+            'Content-Type': 'application/json'
+        }
+
+        anthropic_user_prompt = custom_prompt_arg
+        logging.debug(f"Anthropic: User Prompt is {anthropic_user_prompt}")
+        user_message = {
+            "role": "user",
+            "content": f"{input_data} \n\n\n\n{anthropic_user_prompt}"
+        }
+
+        data = {
+            "model": model,
+            "max_tokens": 4096,  # max _possible_ tokens to return
+            "messages": [user_message],
+            "stop_sequences": ["\n\nHuman:"],
+            "temperature": 0.1,
+            "top_k": 0,
+            "top_p": 1.0,
+            "metadata": {
+                "user_id": "example_user_id",
+            },
+            "stream": False,
+            "system": f"{system_prompt}"
+        }
+
+        for attempt in range(max_retries):
+            try:
+                logging.debug("anthropic: Posting request to API")
+                response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data)
+
+                # Check if the status code indicates success
+                if response.status_code == 200:
+                    logging.debug("anthropic: Post submittal successful")
+                    response_data = response.json()
+                    try:
+                        chat_response = response_data['content'][0]['text'].strip()
+                        logging.debug("anthropic: Chat request successful")
+                        print("Chat request processed successfully.")
+                        return chat_response
+                    except (IndexError, KeyError) as e:
+                        logging.debug("anthropic: Unexpected data in response")
+                        print("Unexpected response format from Anthropic API:", response.text)
+                        return None
+                elif response.status_code == 500:  # Handle internal server error specifically
+                    logging.debug("anthropic: Internal server error")
+                    print("Internal server error from API. Retrying may be necessary.")
+                    time.sleep(retry_delay)
+                else:
+                    logging.debug(
+                        f"anthropic: Failed to process chat request, status code {response.status_code}: {response.text}")
+                    print(f"Failed to process chat request, status code {response.status_code}: {response.text}")
+                    return None
+
+            except RequestException as e:
+                logging.error(f"anthropic: Network error during attempt {attempt + 1}/{max_retries}: {str(e)}")
+                if attempt < max_retries - 1:
+                    time.sleep(retry_delay)
+                else:
+                    return f"anthropic: Network error: {str(e)}"
+    except Exception as e:
+        logging.error(f"anthropic: Error in processing: {str(e)}")
+        return f"anthropic: Error occurred while processing summary with Anthropic: {str(e)}"
+
+
+# Summarize with Cohere
+def chat_with_cohere(api_key, input_data, model, custom_prompt_arg, system_prompt=None):
+    global cohere_api_key
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("cohere: API key not provided as parameter")
+            logging.info("cohere: Attempting to use API key from config file")
+            cohere_api_key = loaded_config_data['api_keys']['cohere']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("cohere: API key not found or is empty")
+            return "cohere: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"cohere: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        logging.debug(f"Cohere: Loaded data: {input_data}")
+        logging.debug(f"Cohere: Type of data: {type(input_data)}")
+
+        cohere_model = loaded_config_data['models']['cohere']
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+            'Authorization': f'Bearer {cohere_api_key}'
+        }
+
+        if system_prompt is not None:
+            logging.debug("Anthropic: Using provided system prompt")
+            pass
+        else:
+            system_prompt = "You are a helpful assistant"
+
+        cohere_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
+        logging.debug(f"cohere: User Prompt being sent is {cohere_prompt}")
+
+        logging.debug(f"cohere: System Prompt being sent is {system_prompt}")
+
+        data = {
+            "chat_history": [
+                {"role": "SYSTEM", "message": f"system_prompt"},
+            ],
+            "message": f"{cohere_prompt}",
+            "model": model,
+            "connectors": [{"id": "web-search"}]
+        }
+
+        logging.debug("cohere: Submitting request to API endpoint")
+        print("cohere: Submitting request to API endpoint")
+        response = requests.post('https://api.cohere.ai/v1/chat', headers=headers, json=data)
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'text' in response_data:
+                chat_response = response_data['text'].strip()
+                logging.debug("cohere: Chat request successful")
+                print("Chat request processed successfully.")
+                return chat_response
+            else:
+                logging.error("Expected data not found in API response.")
+                return "Expected data not found in API response."
+        else:
+            logging.error(f"cohere: API request failed with status code {response.status_code}: {response.text}")
+            print(f"Failed to process summary, status code {response.status_code}: {response.text}")
+            return f"cohere: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("cohere: Error in processing: %s", str(e))
+        return f"cohere: Error occurred while processing summary with Cohere: {str(e)}"
+
+
+# https://console.groq.com/docs/quickstart
+def chat_with_groq(api_key, input_data, custom_prompt_arg, system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("groq: API key not provided as parameter")
+            logging.info("groq: Attempting to use API key from config file")
+            groq_api_key = loaded_config_data['api_keys']['groq']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("groq: API key not found or is empty")
+            return "groq: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"groq: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        logging.debug(f"Groq: Loaded data: {input_data}")
+        logging.debug(f"Groq: Type of data: {type(input_data)}")
+
+        # Set the model to be used
+        groq_model = loaded_config_data['models']['groq']
+
+        headers = {
+            'Authorization': f'Bearer {api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        if system_prompt is not None:
+            logging.debug("Groq: Using provided system prompt")
+            pass
+        else:
+            system_prompt = "You are a helpful assistant"
+
+        groq_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
+        logging.debug("groq: User Prompt being sent is {groq_prompt}")
+
+        logging.debug("groq: System Prompt being sent is {system_prompt}")
+
+        data = {
+            "messages": [
+                {
+                    "role": "system",
+                    "content": f"{system_prompt}"
+                },
+                {
+                    "role": "user",
+                    "content": groq_prompt
+                }
+            ],
+            "model": groq_model
+        }
+
+        logging.debug("groq: Submitting request to API endpoint")
+        print("groq: Submitting request to API endpoint")
+        response = requests.post('https://api.groq.com/openai/v1/chat/completions', headers=headers, json=data)
+
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("groq: Summarization successful")
+                print("Summarization successful.")
+                return summary
+            else:
+                logging.error("Expected data not found in API response.")
+                return "Expected data not found in API response."
+        else:
+            logging.error(f"groq: API request failed with status code {response.status_code}: {response.text}")
+            return f"groq: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("groq: Error in processing: %s", str(e))
+        return f"groq: Error occurred while processing summary with groq: {str(e)}"
+
+
+def chat_with_openrouter(api_key, input_data, custom_prompt_arg, system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    import requests
+    import json
+    global openrouter_model, openrouter_api_key
+    # API key validation
+    if api_key is None:
+        logging.info("openrouter: API key not provided as parameter")
+        logging.info("openrouter: Attempting to use API key from config file")
+        openrouter_api_key = loaded_config_data['api_keys']['openrouter']
+
+    if api_key is None or api_key.strip() == "":
+        logging.error("openrouter: API key not found or is empty")
+        return "openrouter: API Key Not Provided/Found in Config file or is empty"
+
+    logging.debug(f"openai: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+    logging.debug(f"openrouter: Loaded data: {input_data}")
+    logging.debug(f"openrouter: Type of data: {type(input_data)}")
+
+    config = configparser.ConfigParser()
+    file_path = 'config.txt'
+
+    # Check if the file exists in the specified path
+    if os.path.exists(file_path):
+        config.read(file_path)
+    elif os.path.exists('config.txt'):  # Check in the current directory
+        config.read('../config.txt')
+    else:
+        print("config.txt not found in the specified path or current directory.")
+    openrouter_model = loaded_config_data['models']['openrouter']
+
+    if system_prompt is not None:
+        logging.debug("OpenRouter: Using provided system prompt")
+        pass
+    else:
+        system_prompt = "You are a helpful assistant"
+
+    openrouter_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
+    logging.debug(f"openrouter: User Prompt being sent is {openrouter_prompt}")
+
+    logging.debug(f"openrouter: System Prompt being sent is {system_prompt}")
+
+    try:
+        logging.debug("openrouter: Submitting request to API endpoint")
+        print("openrouter: Submitting request to API endpoint")
+        response = requests.post(
+            url="https://openrouter.ai/api/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {openrouter_api_key}",
+            },
+            data=json.dumps({
+                "model": f"{openrouter_model}",
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": openrouter_prompt}
+                ]
+            })
+        )
+
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("openrouter: Chat request successful")
+                print("openrouter: Chat request successful.")
+                return summary
+            else:
+                logging.error("openrouter: Expected data not found in API response.")
+                return "openrouter: Expected data not found in API response."
+        else:
+            logging.error(f"openrouter:  API request failed with status code {response.status_code}: {response.text}")
+            return f"openrouter: API request failed: {response.text}"
+    except Exception as e:
+        logging.error("openrouter: Error in processing: %s", str(e))
+        return f"openrouter: Error occurred while processing chat request with openrouter: {str(e)}"
+
+# FIXME: This function is not yet implemented properly
+def chat_with_huggingface(api_key, input_data, custom_prompt_arg, system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    global huggingface_api_key
+    logging.debug(f"huggingface: Summarization process starting...")
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("HuggingFace: API key not provided as parameter")
+            logging.info("HuggingFace: Attempting to use API key from config file")
+            huggingface_api_key = loaded_config_data['api_keys']['openai']
+        if api_key is None or api_key.strip() == "":
+            logging.error("HuggingFace: API key not found or is empty")
+            return "HuggingFace: API Key Not Provided/Found in Config file or is empty"
+        logging.debug(f"HuggingFace: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+        headers = {
+            "Authorization": f"Bearer {api_key}"
+        }
+
+        # Setup model
+        huggingface_model = loaded_config_data['models']['huggingface']
+
+        API_URL = f"https://api-inference.huggingface.co/models/{huggingface_model}"
+        if system_prompt is not None:
+            logging.debug("HuggingFace: Using provided system prompt")
+            pass
+        else:
+            system_prompt = "You are a helpful assistant"
+
+        huggingface_prompt = f"{input_data}\n\n\n\n{custom_prompt_arg}"
+        logging.debug("huggingface: Prompt being sent is {huggingface_prompt}")
+        data = {
+            "inputs": f"{input_data}",
+            "parameters": {"max_length": 8192, "min_length": 100}  # You can adjust max_length and min_length as needed
+        }
+        logging.debug("huggingface: Submitting request...")
+
+        response = requests.post(API_URL, headers=headers, json=data)
+
+        if response.status_code == 200:
+            summary = response.json()[0]['summary_text']
+            logging.debug("huggingface: Chat request successful")
+            print("Chat request successful.")
+            return summary
+        else:
+            logging.error(f"huggingface: Chat request failed with status code {response.status_code}: {response.text}")
+            return f"Failed to process chat request, status code {response.status_code}: {response.text}"
+    except Exception as e:
+        logging.error("huggingface: Error in processing: %s", str(e))
+        print(f"Error occurred while processing chat request with huggingface: {str(e)}")
+        return None
+
+
+def chat_with_deepseek(api_key, input_data, custom_prompt_arg, system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("DeepSeek: API key not provided as parameter")
+            logging.info("DeepSeek: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['deepseek']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("DeepSeek: API key not found or is empty")
+            return "DeepSeek: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"DeepSeek: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        deepseek_model = loaded_config_data['models']['deepseek'] or "deepseek-chat"
+
+        headers = {
+            'Authorization': f'Bearer {api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        if system_prompt is not None:
+            logging.debug(f"Deepseek: Using provided system prompt: {system_prompt}")
+            pass
+        else:
+            system_prompt = "You are a helpful assistant"
+
+        logging.debug(
+            f"Deepseek API Key: {api_key[:5]}...{api_key[-5:] if api_key else None}")
+        logging.debug("openai: Preparing data + prompt for submittal")
+        deepseek_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
+        data = {
+            "model": deepseek_model,
+            "messages": [
+                {"role": "system", "content": f"{system_prompt}"},
+                {"role": "user", "content": deepseek_prompt}
+            ],
+            "stream": False,
+            "temperature": 0.8
+        }
+
+        logging.debug("DeepSeek: Posting request")
+        response = requests.post('https://api.deepseek.com/chat/completions', headers=headers, json=data)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("DeepSeek: Chat request successful")
+                return summary
+            else:
+                logging.warning("DeepSeek: Chat response not found in the response data")
+                return "DeepSeek: Chat response not available"
+        else:
+            logging.error(f"DeepSeek: Chat request failed with status code {response.status_code}")
+            logging.error(f"DeepSeek: Error response: {response.text}")
+            return f"DeepSeek: Failed to chat request summary. Status code: {response.status_code}"
+    except Exception as e:
+        logging.error(f"DeepSeek: Error in processing: {str(e)}", exc_info=True)
+        return f"DeepSeek: Error occurred while processing chat request: {str(e)}"
+
+
+
+# Stashed in here since OpenAI usage.... #FIXME
+# FIXME - https://docs.vllm.ai/en/latest/getting_started/quickstart.html .... Great docs.
+def chat_with_vllm(input_data, custom_prompt_input, api_key=None, vllm_api_url="http://127.0.0.1:8000/v1/chat/completions", system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    llm_model = loaded_config_data['models']['vllm']
+    # API key validation
+    if api_key is None:
+        logging.info("vLLM: API key not provided as parameter")
+        logging.info("vLLM: Attempting to use API key from config file")
+        api_key = loaded_config_data['api_keys']['llama']
+
+    if api_key is None or api_key.strip() == "":
+        logging.info("vLLM: API key not found or is empty")
+    vllm_client = OpenAI(
+        base_url=vllm_api_url,
+        api_key=custom_prompt_input
+    )
+
+    if isinstance(input_data, str) and os.path.isfile(input_data):
+        logging.debug("vLLM: Loading json data for summarization")
+        with open(input_data, 'r') as file:
+            data = json.load(file)
+    else:
+        logging.debug("vLLM: Using provided string data for summarization")
+        data = input_data
+
+    logging.debug(f"vLLM: Loaded data: {data}")
+    logging.debug(f"vLLM: Type of data: {type(data)}")
+
+    if isinstance(data, dict) and 'summary' in data:
+        # If the loaded data is a dictionary and already contains a summary, return it
+        logging.debug("vLLM: Summary already exists in the loaded data")
+        return data['summary']
+
+    # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+    if isinstance(data, list):
+        segments = data
+        text = extract_text_from_segments(segments)
+    elif isinstance(data, str):
+        text = data
+    else:
+        raise ValueError("Invalid input data format")
+
+
+    custom_prompt = custom_prompt_input
+
+    completion = client.chat.completions.create(
+        model=llm_model,
+        messages=[
+            {"role": "system", "content": f"{system_prompt}"},
+            {"role": "user", "content": f"{text} \n\n\n\n{custom_prompt}"}
+        ]
+    )
+    vllm_summary = completion.choices[0].message.content
+    return vllm_summary
+
+
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/LLM_API_Calls_Local.py

@@ -0,0 +1,348 @@
+# Local_Summarization_Lib.py
+#########################################
+# Local Summarization Library
+# This library is used to perform summarization with a 'local' inference engine.
+#
+####
+
+####################
+# Function List
+# FIXME - UPDATE Function Arguments
+# 1. chat_with_local_llm(text, custom_prompt_arg)
+# 2. chat_with_llama(api_url, text, token, custom_prompt)
+# 3. chat_with_kobold(api_url, text, kobold_api_token, custom_prompt)
+# 4. chat_with_oobabooga(api_url, text, ooba_api_token, custom_prompt)
+# 5. chat_with_vllm(vllm_api_url, vllm_api_key_function_arg, llm_model, text, vllm_custom_prompt_function_arg)
+# 6. chat_with_tabbyapi(tabby_api_key, tabby_api_IP, text, tabby_model, custom_prompt)
+# 7. save_summary_to_file(summary, file_path)
+#
+#
+####################
+# Import necessary libraries
+import json
+# Import Local
+from Utils import *
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+
+def chat_with_local_llm(input_data, user_prompt, system_prompt=None):
+    try:
+        if system_prompt is None:
+            system_prompt_arg = "You are a helpful assistant."
+
+        headers = {
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug("Local LLM: Preparing data + prompt for submittal")
+        local_llm_prompt = f"{user_prompt}\n\n\n\n{input_data} "
+        data = {
+            "messages": [
+                {
+                    "role": "system",
+                    "content": f"{system_prompt}"
+                },
+                {
+                    "role": "user",
+                    "content": f"{local_llm_prompt}"
+                }
+            ],
+            "max_tokens": 28000,  # Adjust tokens as needed
+        }
+        logging.debug("Local LLM: System Prompt to be used: %s", system_prompt)
+        logging.debug("Local LLM: User Prompt to be used: %s", user_prompt)
+        logging.debug("Local LLM: Posting request")
+        response = requests.post('http://127.0.0.1:8080/v1/chat/completions', headers=headers, json=data)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("Local LLM: Chat request successful")
+                print("Local LLM: Chat request successful.")
+                return summary
+            else:
+                logging.warning("Local LLM: Chat response not found in the response data")
+                return "Local LLM: Chat response not available"
+        else:
+            logging.debug("Local LLM: Chat request failed")
+            print("Local LLM: Failed to process Chat response:", response.text)
+            return "Local LLM: Failed to process Chat response"
+    except Exception as e:
+        logging.debug("Local LLM: Error in processing: %s", str(e))
+        print("Error occurred while processing Chat request with Local LLM:", str(e))
+        return "Local LLM: Error occurred while processing Chat response"
+
+def chat_with_llama(input_data, custom_prompt, api_url="http://127.0.0.1:8080/completion", api_key=None, system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("llama.cpp: API key not provided as parameter")
+            logging.info("llama.cpp: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['llama']
+
+        if api_key is None or api_key.strip() == "":
+            logging.info("llama.cpp: API key not found or is empty")
+
+        logging.debug(f"llama.cpp: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+        }
+        if len(api_key) > 5:
+            headers['Authorization'] = f'Bearer {api_key}'
+
+        if system_prompt is None:
+            system_prompt = "You are a helpful AI assistant that provides accurate and concise information."
+
+        logging.debug("Llama.cpp: System prompt being used is: %s", system_prompt)
+        logging.debug("Llama.cpp: User prompt being used is: %s", custom_prompt)
+
+
+        llama_prompt = f"{custom_prompt} \n\n\n\n{input_data}"
+        logging.debug(f"llama: Prompt being sent is {llama_prompt}")
+
+        data = {
+            "prompt": f"{llama_prompt}",
+            "system_prompt": f"{system_prompt}"
+        }
+
+        logging.debug("llama: Submitting request to API endpoint")
+        print("llama: Submitting request to API endpoint")
+        response = requests.post(api_url, headers=headers, json=data)
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            # if 'X' in response_data:
+            logging.debug(response_data)
+            summary = response_data['content'].strip()
+            logging.debug("llama: Summarization successful")
+            print("Summarization successful.")
+            return summary
+        else:
+            logging.error(f"Llama: API request failed with status code {response.status_code}: {response.text}")
+            return f"Llama: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("Llama: Error in processing: %s", str(e))
+        return f"Llama: Error occurred while processing summary with llama: {str(e)}"
+
+
+# System prompts not supported through API requests.
+# https://lite.koboldai.net/koboldcpp_api#/api%2Fv1/post_api_v1_generate
+def chat_with_kobold(input_data, api_key, custom_prompt_input, kobold_api_IP="http://127.0.0.1:5001/api/v1/generate"):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("Kobold.cpp: API key not provided as parameter")
+            logging.info("Kobold.cpp: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['kobold']
+
+        if api_key is None or api_key.strip() == "":
+            logging.info("Kobold.cpp: API key not found or is empty")
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+        }
+
+        kobold_prompt = f"{custom_prompt_input} \n\n\n\n{input_data}"
+        logging.debug("kobold: Prompt being sent is {kobold_prompt}")
+
+        # FIXME
+        # Values literally c/p from the api docs....
+        data = {
+            "max_context_length": 8096,
+            "max_length": 4096,
+            "prompt": f"{custom_prompt_input}\n\n\n\n{input_data}"
+        }
+
+        logging.debug("kobold: Submitting request to API endpoint")
+        print("kobold: Submitting request to API endpoint")
+        response = requests.post(kobold_api_IP, headers=headers, json=data)
+        response_data = response.json()
+        logging.debug("kobold: API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'results' in response_data and len(response_data['results']) > 0:
+                summary = response_data['results'][0]['text'].strip()
+                logging.debug("kobold: Chat request successful!")
+                print("Chat request successful!")
+                return summary
+            else:
+                logging.error("Expected data not found in API response.")
+                return "Expected data not found in API response."
+        else:
+            logging.error(f"kobold: API request failed with status code {response.status_code}: {response.text}")
+            return f"kobold: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("kobold: Error in processing: %s", str(e))
+        return f"kobold: Error occurred while processing chat response with kobold: {str(e)}"
+
+# System prompt doesn't work. FIXME
+# https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API
+def chat_with_oobabooga(input_data, api_key, custom_prompt, api_url="http://127.0.0.1:5000/v1/chat/completions", system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("ooba: API key not provided as parameter")
+            logging.info("ooba: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['ooba']
+
+        if api_key is None or api_key.strip() == "":
+            logging.info("ooba: API key not found or is empty")
+
+        if system_prompt is None:
+            system_prompt = "You are a helpful AI assistant that provides accurate and concise information."
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+        }
+
+        # prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a French bakery baking cakes. It
+        # is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are
+        # my favorite." prompt_text += f"\n\n{text}"  # Uncomment this line if you want to include the text variable
+        ooba_prompt = f"{input_data}" + f"\n\n\n\n{custom_prompt}"
+        logging.debug("ooba: Prompt being sent is {ooba_prompt}")
+
+        data = {
+            "mode": "chat",
+            "character": "Example",
+            "messages": [{"role": "user", "content": ooba_prompt}]
+        }
+
+        logging.debug("ooba: Submitting request to API endpoint")
+        print("ooba: Submitting request to API endpoint")
+        response = requests.post(api_url, headers=headers, json=data, verify=False)
+        logging.debug("ooba: API Response Data: %s", response)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            summary = response.json()['choices'][0]['message']['content']
+            logging.debug("ooba: Summarization successful")
+            print("Summarization successful.")
+            return summary
+        else:
+            logging.error(f"oobabooga: API request failed with status code {response.status_code}: {response.text}")
+            return f"ooba: API request failed with status code {response.status_code}: {response.text}"
+
+    except Exception as e:
+        logging.error("ooba: Error in processing: %s", str(e))
+        return f"ooba: Error occurred while processing summary with oobabooga: {str(e)}"
+
+
+# FIXME - Install is more trouble than care to deal with right now.
+def chat_with_tabbyapi(input_data, custom_prompt_input, api_key=None, api_IP="http://127.0.0.1:5000/v1/chat/completions"):
+    loaded_config_data = load_and_log_configs()
+    model = loaded_config_data['models']['tabby']
+    # API key validation
+    if api_key is None:
+        logging.info("tabby: API key not provided as parameter")
+        logging.info("tabby: Attempting to use API key from config file")
+        api_key = loaded_config_data['api_keys']['tabby']
+
+    if api_key is None or api_key.strip() == "":
+        logging.info("tabby: API key not found or is empty")
+
+    if isinstance(input_data, str) and os.path.isfile(input_data):
+        logging.debug("tabby: Loading json data for summarization")
+        with open(input_data, 'r') as file:
+            data = json.load(file)
+    else:
+        logging.debug("tabby: Using provided string data for summarization")
+        data = input_data
+
+    logging.debug(f"tabby: Loaded data: {data}")
+    logging.debug(f"tabby: Type of data: {type(data)}")
+
+    if isinstance(data, dict) and 'summary' in data:
+        # If the loaded data is a dictionary and already contains a summary, return it
+        logging.debug("tabby: Summary already exists in the loaded data")
+        return data['summary']
+
+    # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+    if isinstance(data, list):
+        segments = data
+        text = extract_text_from_segments(segments)
+    elif isinstance(data, str):
+        text = data
+    else:
+        raise ValueError("Invalid input data format")
+
+    headers = {
+        'Authorization': f'Bearer {api_key}',
+        'Content-Type': 'application/json'
+    }
+    data2 = {
+        'text': text,
+        'model': 'tabby'  # Specify the model if needed
+    }
+    tabby_api_ip = loaded_config_data['local_apis']['tabby']['ip']
+    try:
+        response = requests.post(tabby_api_ip, headers=headers, json=data2)
+        response.raise_for_status()
+        summary = response.json().get('summary', '')
+        return summary
+    except requests.exceptions.RequestException as e:
+        logging.error(f"Error summarizing with TabbyAPI: {e}")
+        return "Error summarizing with TabbyAPI."
+
+
+# FIXME aphrodite engine - code was literally tab complete in one go from copilot... :/
+def chat_with_aphrodite(input_data, custom_prompt_input, api_key=None, api_IP="http://" + load_and_log_configs()['local_apis']['aphrodite']['ip']):
+    loaded_config_data = load_and_log_configs()
+    model = loaded_config_data['models']['aphrodite']
+    # API key validation
+    if api_key is None:
+        logging.info("aphrodite: API key not provided as parameter")
+        logging.info("aphrodite: Attempting to use API key from config file")
+        api_key = loaded_config_data['api_keys']['aphrodite']
+
+    if api_key is None or api_key.strip() == "":
+        logging.info("aphrodite: API key not found or is empty")
+
+    headers = {
+        'Authorization': f'Bearer {api_key}',
+        'Content-Type': 'application/json'
+    }
+    data2 = {
+        'text': input_data,
+    }
+    try:
+        response = requests.post(api_IP, headers=headers, json=data2)
+        response.raise_for_status()
+        summary = response.json().get('summary', '')
+        return summary
+    except requests.exceptions.RequestException as e:
+        logging.error(f"Error summarizing with Aphrodite: {e}")
+        return "Error summarizing with Aphrodite."
+
+
+
+
+def save_summary_to_file(summary, file_path):
+    logging.debug("Now saving summary to file...")
+    base_name = os.path.splitext(os.path.basename(file_path))[0]
+    summary_file_path = os.path.join(os.path.dirname(file_path), base_name + '_summary.txt')
+    os.makedirs(os.path.dirname(summary_file_path), exist_ok=True)
+    logging.debug("Opening summary file for writing, *segments.json with *_summary.txt")
+    with open(summary_file_path, 'w') as file:
+        file.write(summary)
+    logging.info(f"Summary saved to file: {summary_file_path}")
+
+#
+#
+#######################################################################################################################
+
+
+

App_Function_Libraries/Local_File_Processing_Lib.py

@@ -0,0 +1,90 @@
+# Local_File_Processing_Lib.py
+#########################################
+# Local File Processing and File Path Handling Library
+# This library is used to handle processing local filepaths and URLs.
+# It checks for the OS, the availability of the GPU, and the availability of the ffmpeg executable.
+# If the GPU is available, it asks the user if they would like to use it for processing.
+# If ffmpeg is not found, it asks the user if they would like to download it.
+# The script will exit if the user chooses not to download ffmpeg.
+####
+
+####################
+# Function List
+#
+# 1. read_paths_from_file(file_path)
+# 2. process_path(path)
+# 3. process_local_file(file_path)
+# 4. read_paths_from_file(file_path: str) -> List[str]
+#
+####################
+
+# Import necessary libraries
+# Import Local
+from App_Function_Libraries.Audio_Transcription_Lib import convert_to_wav
+from App_Function_Libraries.Video_DL_Ingestion_Lib import *
+from App_Function_Libraries.Video_DL_Ingestion_Lib import get_youtube
+from App_Function_Libraries.Utils import normalize_title, create_download_directory
+
+#######################################################################################################################
+# Function Definitions
+#
+
+def read_paths_from_file(file_path):
+    """ Reads a file containing URLs or local file paths and returns them as a list. """
+    paths = []  # Initialize paths as an empty list
+    with open(file_path, 'r') as file:
+        paths = file.readlines()
+    return [path.strip() for path in paths]
+
+
+def process_path(path):
+    """ Decides whether the path is a URL or a local file and processes accordingly. """
+    if path.startswith('http'):
+        logging.debug("file is a URL")
+        # For YouTube URLs, modify to download and extract info
+        return get_youtube(path)
+    elif os.path.exists(path):
+        logging.debug("File is a path")
+        # For local files, define a function to handle them
+        return process_local_file(path)
+    else:
+        logging.error(f"Path does not exist: {path}")
+        return None
+
+
+# FIXME - ingest_text is not used, need to confirm.
+def process_local_file(file_path, ingest_text=False):
+    logging.info(f"Processing local file: {file_path}")
+    file_extension = os.path.splitext(file_path)[1].lower()
+
+    if os.path.isfile(file_path):
+        if file_path.lower().endswith('.txt'):
+            if ingest_text:
+                # Treat as content to be ingested
+                return os.path.dirname(file_path), {'title': os.path.basename(file_path)}, file_path
+            else:
+                # Treat as potential list of URLs
+                with open(file_path, 'r') as file:
+                    urls = file.read().splitlines()
+                return None, None, urls
+        elif file_path.lower().endswith(('.mp4', '.avi', '.mov', '.wav', '.mp3', '.m4a')):
+            # Handle video and audio files (existing code)
+            title = normalize_title(os.path.splitext(os.path.basename(file_path))[0])
+            info_dict = {'title': title}
+            logging.debug(f"Creating {title} directory...")
+            download_path = create_download_directory(title)
+            logging.debug(f"Converting '{title}' to an audio file (wav).")
+            audio_file = convert_to_wav(file_path)
+            logging.debug(f"'{title}' successfully converted to an audio file (wav).")
+            return download_path, info_dict, audio_file
+    else:
+        logging.error(f"File not found: {file_path}")
+        return None, None, None
+
+
+
+
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/Local_LLM_Inference_Engine_Lib.py

@@ -0,0 +1,590 @@
+# Local_LLM_Inference_Engine_Lib.py
+#########################################
+# Local LLM Inference Engine Library
+# This library is used to handle downloading, configuring, and launching the Local LLM Inference Engine
+#   via (llama.cpp via llamafile)
+#
+#
+####
+####################
+# Function List
+#
+# 1. download_latest_llamafile(repo, asset_name_prefix, output_filename)
+# 2. download_file(url, dest_path, expected_checksum=None, max_retries=3, delay=5)
+# 3. verify_checksum(file_path, expected_checksum)
+# 4. cleanup_process()
+# 5. signal_handler(sig, frame)
+# 6. local_llm_function()
+# 7. launch_in_new_terminal_windows(executable, args)
+# 8. launch_in_new_terminal_linux(executable, args)
+# 9. launch_in_new_terminal_mac(executable, args)
+#
+####################
+# Import necessary libraries
+from asyncio import subprocess
+import atexit
+import re
+import sys
+import time
+# Import 3rd-pary Libraries
+#
+# Import Local
+from Article_Summarization_Lib import *
+from App_Function_Libraries.Utils import download_file
+#
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+# Download latest llamafile from Github
+    # Example usage
+    #repo = "Mozilla-Ocho/llamafile"
+    #asset_name_prefix = "llamafile-"
+    #output_filename = "llamafile"
+    #download_latest_llamafile(repo, asset_name_prefix, output_filename)
+
+# THIS SHOULD ONLY BE CALLED IF THE USER IS USING THE GUI TO SETUP LLAMAFILE
+# Function is used to download only llamafile
+def download_latest_llamafile_no_model(output_filename):
+    # Check if the file already exists
+    print("Checking for and downloading Llamafile it it doesn't already exist...")
+    if os.path.exists(output_filename):
+        print("Llamafile already exists. Skipping download.")
+        logging.debug(f"{output_filename} already exists. Skipping download.")
+        llamafile_exists = True
+    else:
+        llamafile_exists = False
+
+    if llamafile_exists == True:
+        pass
+    else:
+        # Establish variables for Llamafile download
+        repo = "Mozilla-Ocho/llamafile"
+        asset_name_prefix = "llamafile-"
+        # Get the latest release information
+        latest_release_url = f"https://api.github.com/repos/{repo}/releases/latest"
+        response = requests.get(latest_release_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch latest release info: {response.status_code}")
+
+        latest_release_data = response.json()
+        tag_name = latest_release_data['tag_name']
+
+        # Get the release details using the tag name
+        release_details_url = f"https://api.github.com/repos/{repo}/releases/tags/{tag_name}"
+        response = requests.get(release_details_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch release details for tag {tag_name}: {response.status_code}")
+
+        release_data = response.json()
+        assets = release_data.get('assets', [])
+
+        # Find the asset with the specified prefix
+        asset_url = None
+        for asset in assets:
+            if re.match(f"{asset_name_prefix}.*", asset['name']):
+                asset_url = asset['browser_download_url']
+                break
+
+        if not asset_url:
+            raise Exception(f"No asset found with prefix {asset_name_prefix}")
+
+        # Download the asset
+        response = requests.get(asset_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to download asset: {response.status_code}")
+
+        print("Llamafile downloaded successfully.")
+        logging.debug("Main: Llamafile downloaded successfully.")
+
+        # Save the file
+        with open(output_filename, 'wb') as file:
+            file.write(response.content)
+
+        logging.debug(f"Downloaded {output_filename} from {asset_url}")
+        print(f"Downloaded {output_filename} from {asset_url}")
+    return output_filename
+
+
+# FIXME - Add option in GUI for selecting the other models for download
+# Should only be called from 'local_llm_gui_function' - if its called from anywhere else, shits broken.
+# Function is used to download llamafile + A model from Huggingface
+def download_latest_llamafile_through_gui(repo, asset_name_prefix, output_filename):
+    # Check if the file already exists
+    print("Checking for and downloading Llamafile it it doesn't already exist...")
+    if os.path.exists(output_filename):
+        print("Llamafile already exists. Skipping download.")
+        logging.debug(f"{output_filename} already exists. Skipping download.")
+        llamafile_exists = True
+    else:
+        llamafile_exists = False
+
+    if llamafile_exists == True:
+        pass
+    else:
+        # Get the latest release information
+        latest_release_url = f"https://api.github.com/repos/{repo}/releases/latest"
+        response = requests.get(latest_release_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch latest release info: {response.status_code}")
+
+        latest_release_data = response.json()
+        tag_name = latest_release_data['tag_name']
+
+        # Get the release details using the tag name
+        release_details_url = f"https://api.github.com/repos/{repo}/releases/tags/{tag_name}"
+        response = requests.get(release_details_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch release details for tag {tag_name}: {response.status_code}")
+
+        release_data = response.json()
+        assets = release_data.get('assets', [])
+
+        # Find the asset with the specified prefix
+        asset_url = None
+        for asset in assets:
+            if re.match(f"{asset_name_prefix}.*", asset['name']):
+                asset_url = asset['browser_download_url']
+                break
+
+        if not asset_url:
+            raise Exception(f"No asset found with prefix {asset_name_prefix}")
+
+        # Download the asset
+        response = requests.get(asset_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to download asset: {response.status_code}")
+
+        print("Llamafile downloaded successfully.")
+        logging.debug("Main: Llamafile downloaded successfully.")
+
+        # Save the file
+        with open(output_filename, 'wb') as file:
+            file.write(response.content)
+
+        logging.debug(f"Downloaded {output_filename} from {asset_url}")
+        print(f"Downloaded {output_filename} from {asset_url}")
+
+    # Check to see if the LLM already exists, and if not, download the LLM
+    print("Checking for and downloading LLM from Huggingface if needed...")
+    logging.debug("Main: Checking and downloading LLM from Huggingface if needed...")
+    mistral_7b_instruct_v0_2_q8_0_llamafile = "mistral-7b-instruct-v0.2.Q8_0.llamafile"
+    Samantha_Mistral_Instruct_7B_Bulleted_Notes_Q8 = "samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"
+    Phi_3_mini_128k_instruct_Q8_0_gguf = "Phi-3-mini-128k-instruct-Q8_0.gguf"
+    if os.path.exists(mistral_7b_instruct_v0_2_q8_0_llamafile):
+        llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+        print("Model is already downloaded. Skipping download.")
+        pass
+    elif os.path.exists(Samantha_Mistral_Instruct_7B_Bulleted_Notes_Q8):
+        llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+        print("Model is already downloaded. Skipping download.")
+        pass
+    elif os.path.exists(mistral_7b_instruct_v0_2_q8_0_llamafile):
+        llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+        print("Model is already downloaded. Skipping download.")
+        pass
+    else:
+        logging.debug("Main: Checking and downloading LLM from Huggingface if needed...")
+        print("Downloading LLM from Huggingface...")
+        time.sleep(1)
+        print("Gonna be a bit...")
+        time.sleep(1)
+        print("Like seriously, an 8GB file...")
+        time.sleep(2)
+        # Not needed for GUI
+        # dl_check = input("Final chance to back out, hit 'N'/'n' to cancel, or 'Y'/'y' to continue: ")
+        #if dl_check == "N" or dl_check == "n":
+        #     exit()
+        x = 2
+        if x != 1:
+            print("Uhhhh how'd you get here...?")
+            exit()
+        else:
+            print("Downloading LLM from Huggingface...")
+            # Establish hash values for LLM models
+            mistral_7b_instruct_v0_2_q8_gguf_sha256 = "f326f5f4f137f3ad30f8c9cc21d4d39e54476583e8306ee2931d5a022cb85b06"
+            samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256 = "6334c1ab56c565afd86535271fab52b03e67a5e31376946bce7bf5c144e847e4"
+            mistral_7b_instruct_v0_2_q8_0_llamafile_sha256 = "1ee6114517d2f770425c880e5abc443da36b193c82abec8e2885dd7ce3b9bfa6"
+            global llm_choice
+
+            # FIXME - llm_choice
+            llm_choice = 2
+            llm_choice = input("Which LLM model would you like to download? 1. Mistral-7B-Instruct-v0.2-GGUF or 2. Samantha-Mistral-Instruct-7B-Bulleted-Notes) (plain or 'custom') or MS Flavor: Phi-3-mini-128k-instruct-Q8_0.gguf  \n\n\tPress '1' or '2' or '3' to specify: ")
+            while llm_choice != "1" and llm_choice != "2" and llm_choice != "3":
+                print("Invalid choice. Please try again.")
+            if llm_choice == "1":
+                llm_download_model = "Mistral-7B-Instruct-v0.2-Q8.llamafile"
+                mistral_7b_instruct_v0_2_q8_0_llamafile_sha256 = "1ee6114517d2f770425c880e5abc443da36b193c82abec8e2885dd7ce3b9bfa6"
+                llm_download_model_hash = mistral_7b_instruct_v0_2_q8_0_llamafile_sha256
+                llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+                llamafile_llm_output_filename = "mistral-7b-instruct-v0.2.Q8_0.llamafile"
+                download_file(llamafile_llm_url, llamafile_llm_output_filename, llm_download_model_hash)
+            elif llm_choice == "2":
+                llm_download_model = "Samantha-Mistral-Instruct-7B-Bulleted-Notes-Q8.gguf"
+                samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256 = "6334c1ab56c565afd86535271fab52b03e67a5e31376946bce7bf5c144e847e4"
+                llm_download_model_hash = samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256
+                llamafile_llm_output_filename = "samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"
+                llamafile_llm_url = "https://huggingface.co/cognitivetech/samantha-mistral-instruct-7b-bulleted-notes-GGUF/resolve/main/samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf?download=true"
+                download_file(llamafile_llm_url, llamafile_llm_output_filename, llm_download_model_hash)
+            elif llm_choice == "3":
+                llm_download_model = "Phi-3-mini-128k-instruct-Q8_0.gguf"
+                Phi_3_mini_128k_instruct_Q8_0_gguf_sha256 = "6817b66d1c3c59ab06822e9732f0e594eea44e64cae2110906eac9d17f75d193"
+                llm_download_model_hash = Phi_3_mini_128k_instruct_Q8_0_gguf_sha256
+                llamafile_llm_output_filename = "Phi-3-mini-128k-instruct-Q8_0.gguf"
+                llamafile_llm_url = "https://huggingface.co/gaianet/Phi-3-mini-128k-instruct-GGUF/resolve/main/Phi-3-mini-128k-instruct-Q8_0.gguf?download=true"
+                download_file(llamafile_llm_url, llamafile_llm_output_filename, llm_download_model_hash)
+            elif llm_choice == "4": # FIXME - and meta_Llama_3_8B_Instruct_Q8_0_llamafile_exists == False:
+                meta_Llama_3_8B_Instruct_Q8_0_llamafile_sha256 = "406868a97f02f57183716c7e4441d427f223fdbc7fa42964ef10c4d60dd8ed37"
+                llm_download_model_hash = meta_Llama_3_8B_Instruct_Q8_0_llamafile_sha256
+                llamafile_llm_output_filename = " Meta-Llama-3-8B-Instruct.Q8_0.llamafile"
+                llamafile_llm_url = "https://huggingface.co/Mozilla/Meta-Llama-3-8B-Instruct-llamafile/resolve/main/Meta-Llama-3-8B-Instruct.Q8_0.llamafile?download=true"
+            else:
+                print("Invalid choice. Please try again.")
+    return output_filename
+
+
+# Maybe replace/ dead code? FIXME
+# Function is used to download llamafile + A model from Huggingface
+def download_latest_llamafile(repo, asset_name_prefix, output_filename):
+    # Check if the file already exists
+    print("Checking for and downloading Llamafile it it doesn't already exist...")
+    if os.path.exists(output_filename):
+        print("Llamafile already exists. Skipping download.")
+        logging.debug(f"{output_filename} already exists. Skipping download.")
+        llamafile_exists = True
+    else:
+        llamafile_exists = False
+
+    if llamafile_exists == True:
+        pass
+    else:
+        # Get the latest release information
+        latest_release_url = f"https://api.github.com/repos/{repo}/releases/latest"
+        response = requests.get(latest_release_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch latest release info: {response.status_code}")
+
+        latest_release_data = response.json()
+        tag_name = latest_release_data['tag_name']
+
+        # Get the release details using the tag name
+        release_details_url = f"https://api.github.com/repos/{repo}/releases/tags/{tag_name}"
+        response = requests.get(release_details_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch release details for tag {tag_name}: {response.status_code}")
+
+        release_data = response.json()
+        assets = release_data.get('assets', [])
+
+        # Find the asset with the specified prefix
+        asset_url = None
+        for asset in assets:
+            if re.match(f"{asset_name_prefix}.*", asset['name']):
+                asset_url = asset['browser_download_url']
+                break
+
+        if not asset_url:
+            raise Exception(f"No asset found with prefix {asset_name_prefix}")
+
+        # Download the asset
+        response = requests.get(asset_url)
+        if response.status_code != 200:
+            raise Exception(f"Failed to download asset: {response.status_code}")
+
+        print("Llamafile downloaded successfully.")
+        logging.debug("Main: Llamafile downloaded successfully.")
+
+        # Save the file
+        with open(output_filename, 'wb') as file:
+            file.write(response.content)
+
+        logging.debug(f"Downloaded {output_filename} from {asset_url}")
+        print(f"Downloaded {output_filename} from {asset_url}")
+
+    # Check to see if the LLM already exists, and if not, download the LLM
+    print("Checking for and downloading LLM from Huggingface if needed...")
+    logging.debug("Main: Checking and downloading LLM from Huggingface if needed...")
+    mistral_7b_instruct_v0_2_q8_0_llamafile = "mistral-7b-instruct-v0.2.Q8_0.llamafile"
+    Samantha_Mistral_Instruct_7B_Bulleted_Notes_Q8 = "samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"
+    Phi_3_mini_128k_instruct_Q8_0_gguf = "Phi-3-mini-128k-instruct-Q8_0.gguf"
+    if os.path.exists(mistral_7b_instruct_v0_2_q8_0_llamafile):
+        llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+        print("Model is already downloaded. Skipping download.")
+        pass
+    elif os.path.exists(Samantha_Mistral_Instruct_7B_Bulleted_Notes_Q8):
+        llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+        print("Model is already downloaded. Skipping download.")
+        pass
+    elif os.path.exists(mistral_7b_instruct_v0_2_q8_0_llamafile):
+        llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+        print("Model is already downloaded. Skipping download.")
+        pass
+    else:
+        logging.debug("Main: Checking and downloading LLM from Huggingface if needed...")
+        print("Downloading LLM from Huggingface...")
+        time.sleep(1)
+        print("Gonna be a bit...")
+        time.sleep(1)
+        print("Like seriously, an 8GB file...")
+        time.sleep(2)
+        dl_check = input("Final chance to back out, hit 'N'/'n' to cancel, or 'Y'/'y' to continue: ")
+        if dl_check == "N" or dl_check == "n":
+            exit()
+        else:
+            print("Downloading LLM from Huggingface...")
+            # Establish hash values for LLM models
+            mistral_7b_instruct_v0_2_q8_gguf_sha256 = "f326f5f4f137f3ad30f8c9cc21d4d39e54476583e8306ee2931d5a022cb85b06"
+            samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256 = "6334c1ab56c565afd86535271fab52b03e67a5e31376946bce7bf5c144e847e4"
+            mistral_7b_instruct_v0_2_q8_0_llamafile_sha256 = "1ee6114517d2f770425c880e5abc443da36b193c82abec8e2885dd7ce3b9bfa6"
+
+            # FIXME - llm_choice
+            llm_choice = 2
+            llm_choice = input("Which LLM model would you like to download? 1. Mistral-7B-Instruct-v0.2-GGUF or 2. Samantha-Mistral-Instruct-7B-Bulleted-Notes) (plain or 'custom') or MS Flavor: Phi-3-mini-128k-instruct-Q8_0.gguf  \n\n\tPress '1' or '2' or '3' to specify: ")
+            while llm_choice != "1" and llm_choice != "2" and llm_choice != "3":
+                print("Invalid choice. Please try again.")
+            if llm_choice == "1":
+                llm_download_model = "Mistral-7B-Instruct-v0.2-Q8.llamafile"
+                mistral_7b_instruct_v0_2_q8_0_llamafile_sha256 = "1ee6114517d2f770425c880e5abc443da36b193c82abec8e2885dd7ce3b9bfa6"
+                llm_download_model_hash = mistral_7b_instruct_v0_2_q8_0_llamafile_sha256
+                llamafile_llm_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+                llamafile_llm_output_filename = "mistral-7b-instruct-v0.2.Q8_0.llamafile"
+                download_file(llamafile_llm_url, llamafile_llm_output_filename, llm_download_model_hash)
+            elif llm_choice == "2":
+                llm_download_model = "Samantha-Mistral-Instruct-7B-Bulleted-Notes-Q8.gguf"
+                samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256 = "6334c1ab56c565afd86535271fab52b03e67a5e31376946bce7bf5c144e847e4"
+                llm_download_model_hash = samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256
+                llamafile_llm_output_filename = "samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"
+                llamafile_llm_url = "https://huggingface.co/cognitivetech/samantha-mistral-instruct-7b_bulleted-notes_GGUF/resolve/main/samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf?download=true"
+                download_file(llamafile_llm_url, llamafile_llm_output_filename, llm_download_model_hash)
+            elif llm_choice == "3":
+                llm_download_model = "Phi-3-mini-128k-instruct-Q8_0.gguf"
+                Phi_3_mini_128k_instruct_Q8_0_gguf_sha256 = "6817b66d1c3c59ab06822e9732f0e594eea44e64cae2110906eac9d17f75d193"
+                llm_download_model_hash = Phi_3_mini_128k_instruct_Q8_0_gguf_sha256
+                llamafile_llm_output_filename = "Phi-3-mini-128k-instruct-Q8_0.gguf"
+                llamafile_llm_url = "https://huggingface.co/gaianet/Phi-3-mini-128k-instruct-GGUF/resolve/main/Phi-3-mini-128k-instruct-Q8_0.gguf?download=true"
+                download_file(llamafile_llm_url, llamafile_llm_output_filename, llm_download_model_hash)
+            elif llm_choice == "4": # FIXME - and meta_Llama_3_8B_Instruct_Q8_0_llamafile_exists == False:
+                meta_Llama_3_8B_Instruct_Q8_0_llamafile_sha256 = "406868a97f02f57183716c7e4441d427f223fdbc7fa42964ef10c4d60dd8ed37"
+                llm_download_model_hash = meta_Llama_3_8B_Instruct_Q8_0_llamafile_sha256
+                llamafile_llm_output_filename = " Meta-Llama-3-8B-Instruct.Q8_0.llamafile"
+                llamafile_llm_url = "https://huggingface.co/Mozilla/Meta-Llama-3-8B-Instruct-llamafile/resolve/main/Meta-Llama-3-8B-Instruct.Q8_0.llamafile?download=true"
+            else:
+                print("Invalid choice. Please try again.")
+    return output_filename
+
+
+
+
+# FIXME / IMPLEMENT FULLY
+# File download verification
+#mistral_7b_llamafile_instruct_v02_q8_url = "https://huggingface.co/Mozilla/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q8_0.llamafile?download=true"
+#global mistral_7b_instruct_v0_2_q8_0_llamafile_sha256
+#mistral_7b_instruct_v0_2_q8_0_llamafile_sha256 = "1ee6114517d2f770425c880e5abc443da36b193c82abec8e2885dd7ce3b9bfa6"
+
+#mistral_7b_v02_instruct_model_q8_gguf_url = "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q8_0.gguf?download=true"
+#global mistral_7b_instruct_v0_2_q8_gguf_sha256
+#mistral_7b_instruct_v0_2_q8_gguf_sha256 = "f326f5f4f137f3ad30f8c9cc21d4d39e54476583e8306ee2931d5a022cb85b06"
+
+#samantha_instruct_model_q8_gguf_url = "https://huggingface.co/cognitivetech/samantha-mistral-instruct-7b_bulleted-notes_GGUF/resolve/main/samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf?download=true"
+#global samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256
+#samantha_mistral_instruct_7b_bulleted_notes_q8_0_gguf_sha256 = "6334c1ab56c565afd86535271fab52b03e67a5e31376946bce7bf5c144e847e4"
+
+
+process = None
+# Function to close out llamafile process on script exit.
+def cleanup_process():
+    global process
+    if process is not None:
+        process.kill()
+        logging.debug("Main: Terminated the external process")
+
+
+def signal_handler(sig, frame):
+    logging.info('Signal handler called with signal: %s', sig)
+    cleanup_process()
+    sys.exit(0)
+
+
+# FIXME - Add callout to gradio UI
+def local_llm_function():
+    global process
+    repo = "Mozilla-Ocho/llamafile"
+    asset_name_prefix = "llamafile-"
+    useros = os.name
+    if useros == "nt":
+        output_filename = "llamafile.exe"
+    else:
+        output_filename = "llamafile"
+    print(
+        "WARNING - Checking for existence of llamafile and HuggingFace model, downloading if needed...This could be a while")
+    print("WARNING - and I mean a while. We're talking an 8 Gigabyte model here...")
+    print("WARNING - Hope you're comfy. Or it's already downloaded.")
+    time.sleep(6)
+    logging.debug("Main: Checking and downloading Llamafile from Github if needed...")
+    llamafile_path = download_latest_llamafile(repo, asset_name_prefix, output_filename)
+    logging.debug("Main: Llamafile downloaded successfully.")
+
+    # FIXME - llm_choice
+    global llm_choice
+    llm_choice = 1
+    # Launch the llamafile in an external process with the specified argument
+    if llm_choice == 1:
+        arguments = ["--ctx-size", "8192 ", " -m", "mistral-7b-instruct-v0.2.Q8_0.llamafile"]
+    elif llm_choice == 2:
+        arguments = ["--ctx-size", "8192 ", " -m", "samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"]
+    elif llm_choice == 3:
+        arguments = ["--ctx-size", "8192 ", " -m", "Phi-3-mini-128k-instruct-Q8_0.gguf"]
+    elif llm_choice == 4:
+        arguments = ["--ctx-size", "8192 ", " -m", "llama-3"] # FIXME
+
+    try:
+        logging.info("Main: Launching the LLM (llamafile) in an external terminal window...")
+        if useros == "nt":
+            launch_in_new_terminal_windows(llamafile_path, arguments)
+        elif useros == "posix":
+            launch_in_new_terminal_linux(llamafile_path, arguments)
+        else:
+            launch_in_new_terminal_mac(llamafile_path, arguments)
+        # FIXME - pid doesn't exist in this context
+        #logging.info(f"Main: Launched the {llamafile_path} with PID {process.pid}")
+        atexit.register(cleanup_process, process)
+    except Exception as e:
+        logging.error(f"Failed to launch the process: {e}")
+        print(f"Failed to launch the process: {e}")
+
+
+# This function is used to dl a llamafile binary + the Samantha Mistral Finetune model.
+# It should only be called when the user is using the GUI to set up and interact with Llamafile.
+def local_llm_gui_function(am_noob, verbose_checked, threads_checked, threads_value, http_threads_checked, http_threads_value,
+                 model_checked, model_value, hf_repo_checked, hf_repo_value, hf_file_checked, hf_file_value,
+                 ctx_size_checked, ctx_size_value, ngl_checked, ngl_value, host_checked, host_value, port_checked,
+                 port_value):
+    # Identify running OS
+    useros = os.name
+    if useros == "nt":
+        output_filename = "llamafile.exe"
+    else:
+        output_filename = "llamafile"
+
+    # Build up the commands for llamafile
+    built_up_args = []
+
+    # Identify if the user wants us to do everything for them
+    if am_noob == True:
+        print("You're a noob. (lol j/k; they're good settings)")
+
+        # Setup variables for Model download from HF
+        repo = "Mozilla-Ocho/llamafile"
+        asset_name_prefix = "llamafile-"
+        print(
+            "WARNING - Checking for existence of llamafile or HuggingFace model (GGUF type), downloading if needed...This could be a while")
+        print("WARNING - and I mean a while. We're talking an 8 Gigabyte model here...")
+        print("WARNING - Hope you're comfy. Or it's already downloaded.")
+        time.sleep(6)
+        logging.debug("Main: Checking for Llamafile and downloading  from Github if needed...\n\tAlso checking for a "
+                      "local LLM model...\n\tDownloading if needed...\n\tThis could take a while...\n\tWill be the "
+                      "'samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf' model...")
+        llamafile_path = download_latest_llamafile_through_gui(repo, asset_name_prefix, output_filename)
+        logging.debug("Main: Llamafile downloaded successfully.")
+
+        arguments = []
+        # FIXME - llm_choice
+        # This is the gui, we can add this as options later
+        llm_choice = 2
+        # Launch the llamafile in an external process with the specified argument
+        if llm_choice == 1:
+            arguments = ["--ctx-size", "8192 ", " -m", "mistral-7b-instruct-v0.2.Q8_0.llamafile"]
+        elif llm_choice == 2:
+            arguments = """--ctx-size 8192 -m samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"""
+        elif llm_choice == 3:
+            arguments = ["--ctx-size", "8192 ", " -m", "Phi-3-mini-128k-instruct-Q8_0.gguf"]
+        elif llm_choice == 4:
+            arguments = ["--ctx-size", "8192 ", " -m", "llama-3"]
+
+        try:
+            logging.info("Main(Local-LLM-GUI-noob): Launching the LLM (llamafile) in an external terminal window...")
+
+            if useros == "nt":
+                command = 'start cmd /k "llamafile.exe --ctx-size 8192 -m samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"'
+                subprocess.Popen(command, shell=True)
+            elif useros == "posix":
+                command = "llamafile --ctx-size 8192 -m samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"
+                subprocess.Popen(command, shell=True)
+            else:
+                command = "llamafile.exe --ctx-size 8192 -m samantha-mistral-instruct-7b-bulleted-notes.Q8_0.gguf"
+                subprocess.Popen(command, shell=True)
+            # FIXME - pid doesn't exist in this context
+            # logging.info(f"Main: Launched the {llamafile_path} with PID {process.pid}")
+            atexit.register(cleanup_process, process)
+        except Exception as e:
+            logging.error(f"Failed to launch the process: {e}")
+            print(f"Failed to launch the process: {e}")
+
+    else:
+        print("You're not a noob.")
+        llamafile_path = download_latest_llamafile_no_model(output_filename)
+        if verbose_checked == True:
+            print("Verbose mode enabled.")
+            built_up_args.append("--verbose")
+        if threads_checked == True:
+            print(f"Threads enabled with value: {threads_value}")
+            built_up_args.append(f"--threads {threads_value}")
+        if http_threads_checked == True:
+            print(f"HTTP Threads enabled with value: {http_threads_value}")
+            built_up_args.append(f"--http-threads {http_threads_value}")
+        if model_checked == True:
+            print(f"Model enabled with value: {model_value}")
+            built_up_args.append(f"--model {model_value}")
+        if hf_repo_checked == True:
+            print(f"Huggingface repo enabled with value: {hf_repo_value}")
+            built_up_args.append(f"--hf-repo {hf_repo_value}")
+        if hf_file_checked == True:
+            print(f"Huggingface file enabled with value: {hf_file_value}")
+            built_up_args.append(f"--hf-file {hf_file_value}")
+        if ctx_size_checked == True:
+            print(f"Context size enabled with value: {ctx_size_value}")
+            built_up_args.append(f"--ctx-size {ctx_size_value}")
+        if ngl_checked == True:
+            print(f"NGL enabled with value: {ngl_value}")
+            built_up_args.append(f"--ngl {ngl_value}")
+        if host_checked == True:
+            print(f"Host enabled with value: {host_value}")
+            built_up_args.append(f"--host {host_value}")
+        if port_checked == True:
+            print(f"Port enabled with value: {port_value}")
+            built_up_args.append(f"--port {port_value}")
+
+        # Lets go ahead and finally launch the bastard...
+        try:
+            logging.info("Main(Local-LLM-GUI-Main): Launching the LLM (llamafile) in an external terminal window...")
+            if useros == "nt":
+                launch_in_new_terminal_windows(llamafile_path, built_up_args)
+            elif useros == "posix":
+                launch_in_new_terminal_linux(llamafile_path, built_up_args)
+            else:
+                launch_in_new_terminal_mac(llamafile_path, built_up_args)
+            # FIXME - pid doesn't exist in this context
+            #logging.info(f"Main: Launched the {llamafile_path} with PID {process.pid}")
+            atexit.register(cleanup_process, process)
+        except Exception as e:
+            logging.error(f"Failed to launch the process: {e}")
+            print(f"Failed to launch the process: {e}")
+
+
+# Launch the executable in a new terminal window # FIXME - really should figure out a cleaner way of doing this...
+def launch_in_new_terminal_windows(executable, args):
+    command = f'start cmd /k "{executable} {" ".join(args)}"'
+    subprocess.Popen(command, shell=True)
+
+
+# FIXME
+def launch_in_new_terminal_linux(executable, args):
+    command = f'gnome-terminal -- {executable} {" ".join(args)}'
+    subprocess.Popen(command, shell=True)
+
+
+# FIXME
+def launch_in_new_terminal_mac(executable, args):
+    command = f'open -a Terminal.app {executable} {" ".join(args)}'
+    subprocess.Popen(command, shell=True)

App_Function_Libraries/Local_Summarization_Lib.py

@@ -0,0 +1,467 @@
+# Local_Summarization_Lib.py
+#########################################
+# Local Summarization Library
+# This library is used to perform summarization with a 'local' inference engine.
+#
+####
+#
+####################
+# Function List
+# FIXME - UPDATE Function Arguments
+# 1. summarize_with_local_llm(text, custom_prompt_arg)
+# 2. summarize_with_llama(api_url, text, token, custom_prompt)
+# 3. summarize_with_kobold(api_url, text, kobold_api_token, custom_prompt)
+# 4. summarize_with_oobabooga(api_url, text, ooba_api_token, custom_prompt)
+# 5. summarize_with_vllm(vllm_api_url, vllm_api_key_function_arg, llm_model, text, vllm_custom_prompt_function_arg)
+# 6. summarize_with_tabbyapi(tabby_api_key, tabby_api_IP, text, tabby_model, custom_prompt)
+# 7. save_summary_to_file(summary, file_path)
+#
+###############################
+# Import necessary libraries
+import json
+import logging
+import os
+import requests
+# Import 3rd-party Libraries
+from openai import OpenAI
+# Import Local
+from App_Function_Libraries.Utils import load_and_log_configs
+from App_Function_Libraries.Utils import extract_text_from_segments
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+logger = logging.getLogger()
+
+# Dirty hack for vLLM
+openai_api_key = "Fake_key"
+client = OpenAI(api_key=openai_api_key)
+
+def summarize_with_local_llm(input_data, custom_prompt_arg):
+    try:
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("Local LLM: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("openai: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"Local LLM: Loaded data: {data}")
+        logging.debug(f"Local LLM: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Local LLM: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Invalid input data format")
+
+        headers = {
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug("Local LLM: Preparing data + prompt for submittal")
+        local_llm_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        data = {
+            "messages": [
+                {
+                    "role": "system",
+                    "content": "You are a professional summarizer."
+                },
+                {
+                    "role": "user",
+                    "content": local_llm_prompt
+                }
+            ],
+            "max_tokens": 28000,  # Adjust tokens as needed
+        }
+        logging.debug("Local LLM: Posting request")
+        response = requests.post('http://127.0.0.1:8080/v1/chat/completions', headers=headers, json=data)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("Local LLM: Summarization successful")
+                print("Local LLM: Summarization successful.")
+                return summary
+            else:
+                logging.warning("Local LLM: Summary not found in the response data")
+                return "Local LLM: Summary not available"
+        else:
+            logging.debug("Local LLM: Summarization failed")
+            print("Local LLM: Failed to process summary:", response.text)
+            return "Local LLM: Failed to process summary"
+    except Exception as e:
+        logging.debug("Local LLM: Error in processing: %s", str(e))
+        print("Error occurred while processing summary with Local LLM:", str(e))
+        return "Local LLM: Error occurred while processing summary"
+
+def summarize_with_llama(input_data, custom_prompt, api_url="http://127.0.0.1:8080/completion", api_key=None):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("llama.cpp: API key not provided as parameter")
+            logging.info("llama.cpp: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['llama']
+
+        if api_key is None or api_key.strip() == "":
+            logging.info("llama.cpp: API key not found or is empty")
+
+        logging.debug(f"llama.cpp: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        # Load transcript
+        logging.debug("llama.cpp: Loading JSON data")
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("Llama.cpp: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("Llama.cpp: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"Llama.cpp: Loaded data: {data}")
+        logging.debug(f"Llama.cpp: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Llama.cpp: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Llama.cpp: Invalid input data format")
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+        }
+        if len(api_key) > 5:
+            headers['Authorization'] = f'Bearer {api_key}'
+
+        llama_prompt = f"{text} \n\n\n\n{custom_prompt}"
+        logging.debug("llama: Prompt being sent is {llama_prompt}")
+
+        data = {
+            "prompt": llama_prompt
+        }
+
+        logging.debug("llama: Submitting request to API endpoint")
+        print("llama: Submitting request to API endpoint")
+        response = requests.post(api_url, headers=headers, json=data)
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            # if 'X' in response_data:
+            logging.debug(response_data)
+            summary = response_data['content'].strip()
+            logging.debug("llama: Summarization successful")
+            print("Summarization successful.")
+            return summary
+        else:
+            logging.error(f"Llama: API request failed with status code {response.status_code}: {response.text}")
+            return f"Llama: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("Llama: Error in processing: %s", str(e))
+        return f"Llama: Error occurred while processing summary with llama: {str(e)}"
+
+
+# https://lite.koboldai.net/koboldcpp_api#/api%2Fv1/post_api_v1_generate
+def summarize_with_kobold(input_data, api_key, custom_prompt_input, kobold_api_IP="http://127.0.0.1:5001/api/v1/generate"):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("Kobold.cpp: API key not provided as parameter")
+            logging.info("Kobold.cpp: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['kobold']
+
+        if api_key is None or api_key.strip() == "":
+            logging.info("Kobold.cpp: API key not found or is empty")
+
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("Kobold.cpp: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("Kobold.cpp: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"Kobold.cpp: Loaded data: {data}")
+        logging.debug(f"Kobold.cpp: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Kobold.cpp: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Kobold.cpp: Invalid input data format")
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+        }
+
+        kobold_prompt = f"{text} \n\n\n\n{custom_prompt_input}"
+        logging.debug("kobold: Prompt being sent is {kobold_prompt}")
+
+        # FIXME
+        # Values literally c/p from the api docs....
+        data = {
+            "max_context_length": 8096,
+            "max_length": 4096,
+            "prompt": f"{text}\n\n\n\n{custom_prompt_input}"
+        }
+
+        logging.debug("kobold: Submitting request to API endpoint")
+        print("kobold: Submitting request to API endpoint")
+        response = requests.post(kobold_api_IP, headers=headers, json=data)
+        response_data = response.json()
+        logging.debug("kobold: API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'results' in response_data and len(response_data['results']) > 0:
+                summary = response_data['results'][0]['text'].strip()
+                logging.debug("kobold: Summarization successful")
+                print("Summarization successful.")
+                return summary
+            else:
+                logging.error("Expected data not found in API response.")
+                return "Expected data not found in API response."
+        else:
+            logging.error(f"kobold: API request failed with status code {response.status_code}: {response.text}")
+            return f"kobold: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("kobold: Error in processing: %s", str(e))
+        return f"kobold: Error occurred while processing summary with kobold: {str(e)}"
+
+
+# https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API
+def summarize_with_oobabooga(input_data, api_key, custom_prompt, api_url="http://127.0.0.1:5000/v1/chat/completions"):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("ooba: API key not provided as parameter")
+            logging.info("ooba: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['ooba']
+
+        if api_key is None or api_key.strip() == "":
+            logging.info("ooba: API key not found or is empty")
+
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("Oobabooga: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("Oobabooga: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"Oobabooga: Loaded data: {data}")
+        logging.debug(f"Oobabooga: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Oobabooga: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Invalid input data format")
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+        }
+
+        # prompt_text = "I like to eat cake and bake cakes. I am a baker. I work in a French bakery baking cakes. It
+        # is a fun job. I have been baking cakes for ten years. I also bake lots of other baked goods, but cakes are
+        # my favorite." prompt_text += f"\n\n{text}"  # Uncomment this line if you want to include the text variable
+        ooba_prompt = f"{text}" + f"\n\n\n\n{custom_prompt}"
+        logging.debug("ooba: Prompt being sent is {ooba_prompt}")
+
+        data = {
+            "mode": "chat",
+            "character": "Example",
+            "messages": [{"role": "user", "content": ooba_prompt}]
+        }
+
+        logging.debug("ooba: Submitting request to API endpoint")
+        print("ooba: Submitting request to API endpoint")
+        response = requests.post(api_url, headers=headers, json=data, verify=False)
+        logging.debug("ooba: API Response Data: %s", response)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            summary = response.json()['choices'][0]['message']['content']
+            logging.debug("ooba: Summarization successful")
+            print("Summarization successful.")
+            return summary
+        else:
+            logging.error(f"oobabooga: API request failed with status code {response.status_code}: {response.text}")
+            return f"ooba: API request failed with status code {response.status_code}: {response.text}"
+
+    except Exception as e:
+        logging.error("ooba: Error in processing: %s", str(e))
+        return f"ooba: Error occurred while processing summary with oobabooga: {str(e)}"
+
+
+# FIXME - Install is more trouble than care to deal with right now.
+def summarize_with_tabbyapi(input_data, custom_prompt_input, api_key=None, api_IP="http://127.0.0.1:5000/v1/chat/completions"):
+    loaded_config_data = load_and_log_configs()
+    model = loaded_config_data['models']['tabby']
+    # API key validation
+    if api_key is None:
+        logging.info("tabby: API key not provided as parameter")
+        logging.info("tabby: Attempting to use API key from config file")
+        api_key = loaded_config_data['api_keys']['tabby']
+
+    if api_key is None or api_key.strip() == "":
+        logging.info("tabby: API key not found or is empty")
+
+    if isinstance(input_data, str) and os.path.isfile(input_data):
+        logging.debug("tabby: Loading json data for summarization")
+        with open(input_data, 'r') as file:
+            data = json.load(file)
+    else:
+        logging.debug("tabby: Using provided string data for summarization")
+        data = input_data
+
+    logging.debug(f"tabby: Loaded data: {data}")
+    logging.debug(f"tabby: Type of data: {type(data)}")
+
+    if isinstance(data, dict) and 'summary' in data:
+        # If the loaded data is a dictionary and already contains a summary, return it
+        logging.debug("tabby: Summary already exists in the loaded data")
+        return data['summary']
+
+    # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+    if isinstance(data, list):
+        segments = data
+        text = extract_text_from_segments(segments)
+    elif isinstance(data, str):
+        text = data
+    else:
+        raise ValueError("Invalid input data format")
+
+    headers = {
+        'Authorization': f'Bearer {api_key}',
+        'Content-Type': 'application/json'
+    }
+    data2 = {
+        'text': text,
+        'model': 'tabby'  # Specify the model if needed
+    }
+    tabby_api_ip = loaded_config_data['local_apis']['tabby']['ip']
+    try:
+        response = requests.post(tabby_api_ip, headers=headers, json=data2)
+        response.raise_for_status()
+        summary = response.json().get('summary', '')
+        return summary
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Error summarizing with TabbyAPI: {e}")
+        return "Error summarizing with TabbyAPI."
+
+
+# FIXME - https://docs.vllm.ai/en/latest/getting_started/quickstart.html .... Great docs.
+def summarize_with_vllm(input_data, custom_prompt_input, api_key=None, vllm_api_url="http://127.0.0.1:8000/v1/chat/completions"):
+    loaded_config_data = load_and_log_configs()
+    llm_model = loaded_config_data['models']['vllm']
+    # API key validation
+    if api_key is None:
+        logging.info("vLLM: API key not provided as parameter")
+        logging.info("vLLM: Attempting to use API key from config file")
+        api_key = loaded_config_data['api_keys']['llama']
+
+    if api_key is None or api_key.strip() == "":
+        logging.info("vLLM: API key not found or is empty")
+    vllm_client = OpenAI(
+        base_url=vllm_api_url,
+        api_key=custom_prompt_input
+    )
+
+    if isinstance(input_data, str) and os.path.isfile(input_data):
+        logging.debug("vLLM: Loading json data for summarization")
+        with open(input_data, 'r') as file:
+            data = json.load(file)
+    else:
+        logging.debug("vLLM: Using provided string data for summarization")
+        data = input_data
+
+    logging.debug(f"vLLM: Loaded data: {data}")
+    logging.debug(f"vLLM: Type of data: {type(data)}")
+
+    if isinstance(data, dict) and 'summary' in data:
+        # If the loaded data is a dictionary and already contains a summary, return it
+        logging.debug("vLLM: Summary already exists in the loaded data")
+        return data['summary']
+
+    # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+    if isinstance(data, list):
+        segments = data
+        text = extract_text_from_segments(segments)
+    elif isinstance(data, str):
+        text = data
+    else:
+        raise ValueError("Invalid input data format")
+
+
+    custom_prompt = custom_prompt_input
+
+    completion = client.chat.completions.create(
+        model=llm_model,
+        messages=[
+            {"role": "system", "content": "You are a professional summarizer."},
+            {"role": "user", "content": f"{text} \n\n\n\n{custom_prompt}"}
+        ]
+    )
+    vllm_summary = completion.choices[0].message.content
+    return vllm_summary
+
+
+def save_summary_to_file(summary, file_path):
+    logging.debug("Now saving summary to file...")
+    base_name = os.path.splitext(os.path.basename(file_path))[0]
+    summary_file_path = os.path.join(os.path.dirname(file_path), base_name + '_summary.txt')
+    os.makedirs(os.path.dirname(summary_file_path), exist_ok=True)
+    logging.debug("Opening summary file for writing, *segments.json with *_summary.txt")
+    with open(summary_file_path, 'w') as file:
+        file.write(summary)
+    logging.info(f"Summary saved to file: {summary_file_path}")
+
+#
+#
+#######################################################################################################################
+
+
+

App_Function_Libraries/Markdown_Export-improvement.py

@@ -0,0 +1,234 @@
+import gradio as gr
+import logging
+import sqlite3
+from typing import List, Dict
+import os
+import zipfile
+import tempfile
+import shutil
+
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Database connection (you'll need to set this up)
+db = None  # Replace with your actual database connection
+
+
+class DatabaseError(Exception):
+    pass
+
+
+# Database functions
+def fetch_items_by_keyword(search_query: str) -> List[Dict]:
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT m.id, m.title, m.url
+                FROM Media m
+                JOIN MediaKeywords mk ON m.id = mk.media_id
+                JOIN Keywords k ON mk.keyword_id = k.id
+                WHERE k.keyword LIKE ?
+            """, (f'%{search_query}%',))
+            results = cursor.fetchall()
+            return [{"id": r[0], "title": r[1], "url": r[2]} for r in results]
+    except sqlite3.Error as e:
+        logger.error(f"Error fetching items by keyword: {e}")
+        raise DatabaseError(f"Error fetching items by keyword: {e}")
+
+
+def fetch_item_details(media_id: int) -> tuple:
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT prompt, summary 
+                FROM MediaModifications 
+                WHERE media_id = ? 
+                ORDER BY modification_date DESC 
+                LIMIT 1
+            """, (media_id,))
+            prompt_summary_result = cursor.fetchone()
+            cursor.execute("SELECT content FROM Media WHERE id = ?", (media_id,))
+            content_result = cursor.fetchone()
+
+            prompt = prompt_summary_result[0] if prompt_summary_result else ""
+            summary = prompt_summary_result[1] if prompt_summary_result else ""
+            content = content_result[0] if content_result else ""
+
+            return content, prompt, summary
+    except sqlite3.Error as e:
+        logger.error(f"Error fetching item details: {e}")
+        return "", "", ""
+
+
+def browse_items(search_query: str, search_type: str) -> List[Dict]:
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            if search_type == 'Title':
+                cursor.execute("SELECT id, title, url FROM Media WHERE title LIKE ?", (f'%{search_query}%',))
+            elif search_type == 'URL':
+                cursor.execute("SELECT id, title, url FROM Media WHERE url LIKE ?", (f'%{search_query}%',))
+            elif search_type == 'Keyword':
+                return fetch_items_by_keyword(search_query)
+            elif search_type == 'Content':
+                cursor.execute("SELECT id, title, url FROM Media WHERE content LIKE ?", (f'%{search_query}%',))
+            else:
+                raise ValueError(f"Invalid search type: {search_type}")
+
+            results = cursor.fetchall()
+            return [{"id": r[0], "title": r[1], "url": r[2]} for r in results]
+    except sqlite3.Error as e:
+        logger.error(f"Error fetching items by {search_type}: {e}")
+        raise DatabaseError(f"Error fetching items by {search_type}: {e}")
+
+
+# Export functions
+def export_item_as_markdown(media_id: int) -> str:
+    try:
+        content, prompt, summary = fetch_item_details(media_id)
+        title = f"Item {media_id}"  # You might want to fetch the actual title
+        markdown_content = f"# {title}\n\n## Prompt\n{prompt}\n\n## Summary\n{summary}\n\n## Content\n{content}"
+
+        filename = f"export_item_{media_id}.md"
+        with open(filename, "w", encoding='utf-8') as f:
+            f.write(markdown_content)
+
+        logger.info(f"Successfully exported item {media_id} to {filename}")
+        return filename
+    except Exception as e:
+        logger.error(f"Error exporting item {media_id}: {str(e)}")
+        return None
+
+
+def export_items_by_keyword(keyword: str) -> str:
+    try:
+        items = fetch_items_by_keyword(keyword)
+        if not items:
+            logger.warning(f"No items found for keyword: {keyword}")
+            return None
+
+        # Create a temporary directory to store individual markdown files
+        with tempfile.TemporaryDirectory() as temp_dir:
+            folder_name = f"export_keyword_{keyword}"
+            export_folder = os.path.join(temp_dir, folder_name)
+            os.makedirs(export_folder)
+
+            for item in items:
+                content, prompt, summary = fetch_item_details(item['id'])
+                markdown_content = f"# {item['title']}\n\n## Prompt\n{prompt}\n\n## Summary\n{summary}\n\n## Content\n{content}"
+
+                # Create individual markdown file for each item
+                file_name = f"{item['id']}_{item['title'][:50]}.md"  # Limit filename length
+                file_path = os.path.join(export_folder, file_name)
+                with open(file_path, "w", encoding='utf-8') as f:
+                    f.write(markdown_content)
+
+            # Create a zip file containing all markdown files
+            zip_filename = f"{folder_name}.zip"
+            shutil.make_archive(os.path.join(temp_dir, folder_name), 'zip', export_folder)
+
+            # Move the zip file to a location accessible by Gradio
+            final_zip_path = os.path.join(os.getcwd(), zip_filename)
+            shutil.move(os.path.join(temp_dir, zip_filename), final_zip_path)
+
+        logger.info(f"Successfully exported {len(items)} items for keyword '{keyword}' to {zip_filename}")
+        return final_zip_path
+    except Exception as e:
+        logger.error(f"Error exporting items for keyword '{keyword}': {str(e)}")
+        return None
+
+
+def export_selected_items(selected_items: List[Dict]) -> str:
+    try:
+        if not selected_items:
+            logger.warning("No items selected for export")
+            return None
+
+        markdown_content = "# Selected Items\n\n"
+        for item in selected_items:
+            content, prompt, summary = fetch_item_details(item['id'])
+            markdown_content += f"## {item['title']}\n\n### Prompt\n{prompt}\n\n### Summary\n{summary}\n\n### Content\n{content}\n\n---\n\n"
+
+        filename = "export_selected_items.md"
+        with open(filename, "w", encoding='utf-8') as f:
+            f.write(markdown_content)
+
+        logger.info(f"Successfully exported {len(selected_items)} selected items to {filename}")
+        return filename
+    except Exception as e:
+        logger.error(f"Error exporting selected items: {str(e)}")
+        return None
+
+
+# Gradio interface functions
+def display_search_results(search_query: str, search_type: str) -> List[Dict]:
+    try:
+        results = browse_items(search_query, search_type)
+        return [{"name": f"{item['title']} ({item['url']})", "value": item} for item in results]
+    except DatabaseError as e:
+        logger.error(f"Error in display_search_results: {str(e)}")
+        return []
+
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Content Export Interface")
+
+    with gr.Tab("Search and Export"):
+        search_query = gr.Textbox(label="Search Query")
+        search_type = gr.Radio(["Title", "URL", "Keyword", "Content"], label="Search By")
+        search_button = gr.Button("Search")
+
+        search_results = gr.CheckboxGroup(label="Search Results")
+        export_selected_button = gr.Button("Export Selected Items")
+
+        keyword_input = gr.Textbox(label="Enter keyword for export")
+        export_by_keyword_button = gr.Button("Export items by keyword")
+
+        export_output = gr.File(label="Download Exported File")
+
+        error_output = gr.Textbox(label="Status/Error Messages", interactive=False)
+
+    search_button.click(
+        fn=display_search_results,
+        inputs=[search_query, search_type],
+        outputs=[search_results, error_output]
+    )
+
+    export_selected_button.click(
+        fn=lambda selected: (export_selected_items(selected), "Exported selected items") if selected else (
+        None, "No items selected"),
+        inputs=[search_results],
+        outputs=[export_output, error_output]
+    )
+
+    export_by_keyword_button.click(
+        fn=lambda keyword: (
+        export_items_by_keyword(keyword), f"Exported items for keyword: {keyword}") if keyword else (
+        None, "No keyword provided"),
+        inputs=[keyword_input],
+        outputs=[export_output, error_output]
+    )
+
+    # Add functionality to export individual items
+    search_results.select(
+        fn=lambda item: (export_item_as_markdown(item['id']), f"Exported item: {item['title']}") if item else (
+        None, "No item selected"),
+        inputs=[gr.State(lambda: search_results.value)],
+        outputs=[export_output, error_output]
+    )
+
+demo.launch()
+
+
+# This modified version of export_items_by_keyword does the following:
+#
+# Creates a temporary directory to store individual markdown files.
+# For each item associated with the keyword, it creates a separate markdown file.
+# Places all markdown files in a folder named export_keyword_{keyword}.
+# Creates a zip file containing the folder with all markdown files.
+# Moves the zip file to a location accessible by Gradio for download.

App_Function_Libraries/Obsidian-Importer.py

@@ -0,0 +1,210 @@
+import os
+import re
+import yaml
+import sqlite3
+import traceback
+import time
+import zipfile
+import tempfile
+import shutil
+import gradio as gr
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Assume db connection is set up elsewhere
+db = None  # Replace with your actual database connection
+
+
+class DatabaseError(Exception):
+    pass
+
+
+def scan_obsidian_vault(vault_path):
+    markdown_files = []
+    for root, dirs, files in os.walk(vault_path):
+        for file in files:
+            if file.endswith('.md'):
+                markdown_files.append(os.path.join(root, file))
+    return markdown_files
+
+
+def parse_obsidian_note(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        content = file.read()
+
+    frontmatter = {}
+    frontmatter_match = re.match(r'^---\s*\n(.*?)\n---\s*\n', content, re.DOTALL)
+    if frontmatter_match:
+        frontmatter_text = frontmatter_match.group(1)
+        frontmatter = yaml.safe_load(frontmatter_text)
+        content = content[frontmatter_match.end():]
+
+    tags = re.findall(r'#(\w+)', content)
+    links = re.findall(r'\[\[(.*?)\]\]', content)
+
+    return {
+        'title': os.path.basename(file_path).replace('.md', ''),
+        'content': content,
+        'frontmatter': frontmatter,
+        'tags': tags,
+        'links': links,
+        'file_path': file_path  # Add this line
+    }
+
+
+def import_obsidian_note_to_db(note_data):
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+
+            cursor.execute("SELECT id FROM Media WHERE title = ? AND type = 'obsidian_note'", (note_data['title'],))
+            existing_note = cursor.fetchone()
+
+            if existing_note:
+                media_id = existing_note[0]
+                cursor.execute("""
+                    UPDATE Media
+                    SET content = ?, author = ?, ingestion_date = CURRENT_TIMESTAMP
+                    WHERE id = ?
+                """, (note_data['content'], note_data['frontmatter'].get('author', 'Unknown'), media_id))
+
+                cursor.execute("DELETE FROM MediaKeywords WHERE media_id = ?", (media_id,))
+            else:
+                cursor.execute("""
+                    INSERT INTO Media (title, content, type, author, ingestion_date, url)
+                    VALUES (?, ?, 'obsidian_note', ?, CURRENT_TIMESTAMP, ?)
+                """, (note_data['title'], note_data['content'], note_data['frontmatter'].get('author', 'Unknown'),
+                      note_data['file_path']))
+
+                media_id = cursor.lastrowid
+
+            for tag in note_data['tags']:
+                cursor.execute("INSERT OR IGNORE INTO Keywords (keyword) VALUES (?)", (tag,))
+                cursor.execute("SELECT id FROM Keywords WHERE keyword = ?", (tag,))
+                keyword_id = cursor.fetchone()[0]
+                cursor.execute("INSERT OR IGNORE INTO MediaKeywords (media_id, keyword_id) VALUES (?, ?)",
+                               (media_id, keyword_id))
+
+            frontmatter_str = yaml.dump(note_data['frontmatter'])
+            cursor.execute("""
+                INSERT INTO MediaModifications (media_id, prompt, summary, modification_date)
+                VALUES (?, 'Obsidian Frontmatter', ?, CURRENT_TIMESTAMP)
+            """, (media_id, frontmatter_str))
+
+            # Update full-text search index
+            cursor.execute('INSERT OR REPLACE INTO media_fts (rowid, title, content) VALUES (?, ?, ?)',
+                           (media_id, note_data['title'], note_data['content']))
+
+        action = "Updated" if existing_note else "Imported"
+        logger.info(f"{action} Obsidian note: {note_data['title']}")
+        return True, None
+    except sqlite3.Error as e:
+        error_msg = f"Database error {'updating' if existing_note else 'importing'} note {note_data['title']}: {str(e)}"
+        logger.error(error_msg)
+        return False, error_msg
+    except Exception as e:
+        error_msg = f"Unexpected error {'updating' if existing_note else 'importing'} note {note_data['title']}: {str(e)}\n{traceback.format_exc()}"
+        logger.error(error_msg)
+        return False, error_msg
+
+
+def import_obsidian_vault(vault_path, progress=gr.Progress()):
+    try:
+        markdown_files = scan_obsidian_vault(vault_path)
+        total_files = len(markdown_files)
+        imported_files = 0
+        errors = []
+
+        for i, file_path in enumerate(markdown_files):
+            try:
+                note_data = parse_obsidian_note(file_path)
+                success, error_msg = import_obsidian_note_to_db(note_data)
+                if success:
+                    imported_files += 1
+                else:
+                    errors.append(error_msg)
+            except Exception as e:
+                error_msg = f"Error processing {file_path}: {str(e)}"
+                logger.error(error_msg)
+                errors.append(error_msg)
+
+            progress((i + 1) / total_files, f"Imported {imported_files} of {total_files} files")
+            time.sleep(0.1)  # Small delay to prevent UI freezing
+
+        return imported_files, total_files, errors
+    except Exception as e:
+        error_msg = f"Error scanning vault: {str(e)}\n{traceback.format_exc()}"
+        logger.error(error_msg)
+        return 0, 0, [error_msg]
+
+
+def process_obsidian_zip(zip_file):
+    with tempfile.TemporaryDirectory() as temp_dir:
+        try:
+            with zipfile.ZipFile(zip_file, 'r') as zip_ref:
+                zip_ref.extractall(temp_dir)
+
+            imported_files, total_files, errors = import_obsidian_vault(temp_dir)
+
+            return imported_files, total_files, errors
+        except zipfile.BadZipFile:
+            error_msg = "The uploaded file is not a valid zip file."
+            logger.error(error_msg)
+            return 0, 0, [error_msg]
+        except Exception as e:
+            error_msg = f"Error processing zip file: {str(e)}\n{traceback.format_exc()}"
+            logger.error(error_msg)
+            return 0, 0, [error_msg]
+        finally:
+            shutil.rmtree(temp_dir, ignore_errors=True)
+
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Content Export and Import Interface")
+
+    # ... (your existing tabs and components)
+
+    with gr.Tab("Import Obsidian Vault"):
+        gr.Markdown("## Import Obsidian Vault")
+        with gr.Row():
+            vault_path_input = gr.Textbox(label="Obsidian Vault Path (Local)")
+            vault_zip_input = gr.File(label="Upload Obsidian Vault (Zip)")
+        import_vault_button = gr.Button("Import Obsidian Vault")
+        import_status = gr.Textbox(label="Import Status", interactive=False)
+
+
+    def import_vault(vault_path, vault_zip):
+        if vault_zip:
+            imported, total, errors = process_obsidian_zip(vault_zip.name)
+        elif vault_path:
+            imported, total, errors = import_obsidian_vault(vault_path)
+        else:
+            return "Please provide either a local vault path or upload a zip file."
+
+        status = f"Imported {imported} out of {total} files.\n"
+        if errors:
+            status += f"Encountered {len(errors)} errors:\n" + "\n".join(errors)
+        return status
+
+
+    import_vault_button.click(
+        fn=import_vault,
+        inputs=[vault_path_input, vault_zip_input],
+        outputs=[import_status],
+        show_progress=True
+    )
+
+    # ... (rest of your existing code)
+
+demo.launch()
+
+# This comprehensive solution includes:
+#
+# Enhanced error handling throughout the import process.
+# Progress updates for large vaults using Gradio's progress bar.
+# The ability to update existing notes if they're reimported.
+# Support for importing Obsidian vaults from both local directories and uploaded zip files.

App_Function_Libraries/Old_Chunking_Lib.py

@@ -0,0 +1,159 @@
+# Old_Chunking_Lib.py
+#########################################
+# Old Chunking Library
+# This library is used to handle chunking of text for summarization.
+#
+####
+import logging
+####################
+# Function List
+#
+# 1. chunk_transcript(transcript: str, chunk_duration: int, words_per_second) -> List[str]
+# 2. summarize_chunks(api_name: str, api_key: str, transcript: List[dict], chunk_duration: int, words_per_second: int) -> str
+# 3. get_chat_completion(messages, model='gpt-4-turbo')
+# 4. chunk_on_delimiter(input_string: str, max_tokens: int, delimiter: str) -> List[str]
+# 5. combine_chunks_with_no_minimum(chunks: List[str], max_tokens: int, chunk_delimiter="\n\n", header: Optional[str] = None, add_ellipsis_for_overflow=False) -> Tuple[List[str], List[int]]
+# 6. rolling_summarize(text: str, detail: float = 0, model: str = 'gpt-4-turbo', additional_instructions: Optional[str] = None, minimum_chunk_size: Optional[int] = 500, chunk_delimiter: str = ".", summarize_recursively=False, verbose=False)
+# 7. chunk_transcript(transcript: str, chunk_duration: int, words_per_second) -> List[str]
+# 8. summarize_chunks(api_name: str, api_key: str, transcript: List[dict], chunk_duration: int, words_per_second: int) -> str
+#
+####################
+
+# Import necessary libraries
+import os
+from typing import Optional, List, Tuple
+#
+# Import 3rd party
+from openai import OpenAI
+from App_Function_Libraries.Tokenization_Methods_Lib import openai_tokenize
+#
+# Import Local
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+######### Words-per-second Chunking #########
+def chunk_transcript(transcript: str, chunk_duration: int, words_per_second) -> List[str]:
+    words = transcript.split()
+    words_per_chunk = chunk_duration * words_per_second
+    chunks = [' '.join(words[i:i + words_per_chunk]) for i in range(0, len(words), words_per_chunk)]
+    return chunks
+
+
+# def summarize_chunks(api_name: str, api_key: str, transcript: List[dict], chunk_duration: int,
+#                      words_per_second: int) -> str:
+#     if api_name not in summarizers:  # See 'summarizers' dict in the main script
+#         return f"Unsupported API: {api_name}"
+#
+#     summarizer = summarizers[api_name]
+#     text = extract_text_from_segments(transcript)
+#     chunks = chunk_transcript(text, chunk_duration, words_per_second)
+#
+#     summaries = []
+#     for chunk in chunks:
+#         if api_name == 'openai':
+#             # Ensure the correct model and prompt are passed
+#             summaries.append(summarizer(api_key, chunk, custom_prompt))
+#         else:
+#             summaries.append(summarizer(api_key, chunk))
+#
+#     return "\n\n".join(summaries)
+
+
+################## ####################
+
+
+######### Token-size Chunking ######### FIXME - OpenAI only currently
+# This is dirty and shameful and terrible. It should be replaced with a proper implementation.
+# anyways lets get to it....
+openai_api_key = "Fake_key" # FIXME
+client = OpenAI(api_key=openai_api_key)
+
+
+
+
+
+# This function chunks a text into smaller pieces based on a maximum token count and a delimiter
+def chunk_on_delimiter(input_string: str,
+                       max_tokens: int,
+                       delimiter: str) -> List[str]:
+    chunks = input_string.split(delimiter)
+    combined_chunks, _, dropped_chunk_count = combine_chunks_with_no_minimum(
+        chunks, max_tokens, chunk_delimiter=delimiter, add_ellipsis_for_overflow=True)
+    if dropped_chunk_count > 0:
+        print(f"Warning: {dropped_chunk_count} chunks were dropped due to exceeding the token limit.")
+    combined_chunks = [f"{chunk}{delimiter}" for chunk in combined_chunks]
+    return combined_chunks
+
+
+
+
+
+#######################################
+
+
+######### Words-per-second Chunking #########
+# FIXME - WHole section needs to be re-written
+def chunk_transcript(transcript: str, chunk_duration: int, words_per_second) -> List[str]:
+    words = transcript.split()
+    words_per_chunk = chunk_duration * words_per_second
+    chunks = [' '.join(words[i:i + words_per_chunk]) for i in range(0, len(words), words_per_chunk)]
+    return chunks
+
+
+# def summarize_chunks(api_name: str, api_key: str, transcript: List[dict], chunk_duration: int,
+#                      words_per_second: int) -> str:
+    # if api_name not in summarizers:  # See 'summarizers' dict in the main script
+    #     return f"Unsupported API: {api_name}"
+    #
+    # if not transcript:
+    #     logging.error("Empty or None transcript provided to summarize_chunks")
+    #     return "Error: Empty or None transcript provided"
+    #
+    # text = extract_text_from_segments(transcript)
+    # chunks = chunk_transcript(text, chunk_duration, words_per_second)
+    #
+    # #FIXME
+    # custom_prompt = args.custom_prompt
+    #
+    # summaries = []
+    # for chunk in chunks:
+    #     if api_name == 'openai':
+    #         # Ensure the correct model and prompt are passed
+    #         summaries.append(summarize_with_openai(api_key, chunk, custom_prompt))
+    #     elif api_name == 'anthropic':
+    #         summaries.append(summarize_with_cohere(api_key, chunk, anthropic_model, custom_prompt))
+    #     elif api_name == 'cohere':
+    #         summaries.append(summarize_with_anthropic(api_key, chunk, cohere_model, custom_prompt))
+    #     elif api_name == 'groq':
+    #         summaries.append(summarize_with_groq(api_key, chunk, groq_model, custom_prompt))
+    #     elif api_name == 'llama':
+    #         summaries.append(summarize_with_llama(llama_api_IP, chunk, api_key, custom_prompt))
+    #     elif api_name == 'kobold':
+    #         summaries.append(summarize_with_kobold(kobold_api_IP, chunk, api_key, custom_prompt))
+    #     elif api_name == 'ooba':
+    #         summaries.append(summarize_with_oobabooga(ooba_api_IP, chunk, api_key, custom_prompt))
+    #     elif api_name == 'tabbyapi':
+    #         summaries.append(summarize_with_vllm(api_key, tabby_api_IP, chunk, summarize.llm_model, custom_prompt))
+    #     elif api_name == 'local-llm':
+    #         summaries.append(summarize_with_local_llm(chunk, custom_prompt))
+    #     else:
+    #         return f"Unsupported API: {api_name}"
+    #
+    # return "\n\n".join(summaries)
+
+# FIXME - WHole section needs to be re-written
+def summarize_with_detail_openai(text, detail, verbose=False):
+    summary_with_detail_variable = rolling_summarize(text, detail=detail, verbose=True)
+    print(len(openai_tokenize(summary_with_detail_variable)))
+    return summary_with_detail_variable
+
+
+def summarize_with_detail_recursive_openai(text, detail, verbose=False):
+    summary_with_recursive_summarization = rolling_summarize(text, detail=detail, summarize_recursively=True)
+    print(summary_with_recursive_summarization)
+
+#
+#
+#################################################################################

App_Function_Libraries/PDF_Ingestion_Lib.py

@@ -0,0 +1,166 @@
+# PDF_Ingestion_Lib.py
+#########################################
+# Library to hold functions for ingesting PDF files.#
+#
+####################
+# Function List
+#
+# 1. convert_pdf_to_markdown(pdf_path)
+# 2. ingest_pdf_file(file_path, title=None, author=None, keywords=None):
+# 3.
+#
+#
+####################
+
+
+# Import necessary libraries
+from datetime import datetime
+import logging
+import subprocess
+import os
+import shutil
+import tempfile
+
+
+# Import Local
+from App_Function_Libraries.SQLite_DB import add_media_with_keywords
+
+#######################################################################################################################
+# Function Definitions
+#
+
+# Ingest a text file into the database with Title/Author/Keywords
+
+
+# Constants
+MAX_FILE_SIZE_MB = 50
+CONVERSION_TIMEOUT_SECONDS = 300
+
+
+def convert_pdf_to_markdown(pdf_path):
+    """
+    Convert a PDF file to Markdown by calling a script in another virtual environment.
+    """
+
+    logging.debug(f"Marker: Converting PDF file to Markdown: {pdf_path}")
+    # Check if the file size exceeds the maximum allowed size
+    file_size_mb = os.path.getsize(pdf_path) / (1024 * 1024)
+    if file_size_mb > MAX_FILE_SIZE_MB:
+        raise ValueError(f"File size ({file_size_mb:.2f} MB) exceeds the maximum allowed size of {MAX_FILE_SIZE_MB} MB")
+
+    logging.debug("Marker: Converting PDF file to Markdown using Marker virtual environment")
+    # Path to the Python interpreter in the other virtual environment
+    other_venv_python = "Helper_Scripts/marker_venv/bin/python"
+
+    # Path to the conversion script
+    converter_script = "Helper_Scripts/PDF_Converter.py"
+
+    logging.debug("Marker: Attempting to convert PDF file to Markdown...")
+    try:
+        result = subprocess.run(
+            [other_venv_python, converter_script, pdf_path],
+            capture_output=True,
+            text=True,
+            timeout=CONVERSION_TIMEOUT_SECONDS
+        )
+        if result.returncode != 0:
+            raise Exception(f"Conversion failed: {result.stderr}")
+        return result.stdout
+    except subprocess.TimeoutExpired:
+        raise Exception(f"PDF conversion timed out after {CONVERSION_TIMEOUT_SECONDS} seconds")
+
+
+def process_and_ingest_pdf(file, title, author, keywords):
+    if file is None:
+        return "Please select a PDF file to upload."
+
+    try:
+        # Create a temporary directory
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # Create a path for the temporary PDF file
+            temp_path = os.path.join(temp_dir, "temp.pdf")
+
+            # Copy the contents of the uploaded file to the temporary file
+            shutil.copy(file.name, temp_path)
+
+            # Call the ingest_pdf_file function with the temporary file path
+            result = ingest_pdf_file(temp_path, title, author, keywords)
+
+        return result
+    except Exception as e:
+        return f"Error processing PDF: {str(e)}"
+
+
+def ingest_pdf_file(file_path, title=None, author=None, keywords=None):
+    try:
+        # Convert PDF to Markdown
+        markdown_content = convert_pdf_to_markdown(file_path)
+
+        # If title is not provided, use the filename without extension
+        if not title:
+            title = os.path.splitext(os.path.basename(file_path))[0]
+
+        # If author is not provided, set it to 'Unknown'
+        if not author:
+            author = 'Unknown'
+
+        # If keywords are not provided, use a default keyword
+        if not keywords:
+            keywords = 'pdf_file,markdown_converted'
+        else:
+            keywords = f'pdf_file,markdown_converted,{keywords}'
+
+        # Add the markdown content to the database
+        add_media_with_keywords(
+            url=file_path,
+            title=title,
+            media_type='document',
+            content=markdown_content,
+            keywords=keywords,
+            prompt='No prompt for PDF files',
+            summary='No summary for PDF files',
+            transcription_model='None',
+            author=author,
+            ingestion_date=datetime.now().strftime('%Y-%m-%d')
+        )
+
+        return f"PDF file '{title}' converted to Markdown and ingested successfully.", file_path
+    except ValueError as e:
+        logging.error(f"File size error: {str(e)}")
+        return f"Error: {str(e)}", file_path
+    except Exception as e:
+        logging.error(f"Error ingesting PDF file: {str(e)}")
+        return f"Error ingesting PDF file: {str(e)}", file_path
+
+
+def process_and_cleanup_pdf(file, title, author, keywords):
+    if file is None:
+        return "No file uploaded. Please upload a PDF file."
+
+    temp_dir = tempfile.mkdtemp()
+    temp_file_path = os.path.join(temp_dir, "temp.pdf")
+
+    try:
+        # Copy the uploaded file to a temporary location
+        shutil.copy2(file.name, temp_file_path)
+
+        # Process the file
+        result, _ = ingest_pdf_file(temp_file_path, title, author, keywords)
+
+        return result
+    except Exception as e:
+        logging.error(f"Error in processing and cleanup: {str(e)}")
+        return f"Error: {str(e)}"
+    finally:
+        # Clean up the temporary directory and its contents
+        try:
+            shutil.rmtree(temp_dir)
+            logging.info(f"Removed temporary directory: {temp_dir}")
+        except Exception as cleanup_error:
+            logging.error(f"Error during cleanup: {str(cleanup_error)}")
+            result += f"\nWarning: Could not remove temporary files: {str(cleanup_error)}"
+
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/RAG_Library.py

@@ -0,0 +1,812 @@
+# RAG_Library.py
+#########################################
+# RAG Search & Related Functions Library
+# This library is used to hold any/all RAG-related operations.
+# Currently, all of this code was generated from Sonnet 3.5. 0_0
+#
+####
+
+import os
+from typing import List, Tuple, Callable, Optional
+from contextlib import contextmanager
+import sqlite3
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import logging
+from dotenv import load_dotenv
+
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+class RAGException(Exception):
+    """Custom exception class for RAG-related errors"""
+    pass
+
+
+class BaseRAGSystem:
+    def __init__(self, db_path: str, model_name: Optional[str] = None):
+        """
+        Initialize the RAG system.
+
+        :param db_path: Path to the SQLite database
+        :param model_name: Name of the SentenceTransformer model to use
+        """
+        self.db_path = db_path
+        self.model_name = model_name or os.getenv('DEFAULT_MODEL_NAME', 'all-MiniLM-L6-v2')
+        try:
+            self.model = SentenceTransformer(self.model_name)
+            logger.info(f"Initialized SentenceTransformer with model: {self.model_name}")
+        except Exception as e:
+            logger.error(f"Failed to initialize SentenceTransformer: {e}")
+            raise RAGException(f"Model initialization failed: {e}")
+
+        self.init_db()
+
+    @contextmanager
+    def get_db_connection(self):
+        conn = sqlite3.connect(self.db_path)
+        try:
+            yield conn
+        finally:
+            conn.close()
+
+    def init_db(self):
+        try:
+            with self.get_db_connection() as conn:
+                cursor = conn.cursor()
+                cursor.execute('''
+                CREATE TABLE IF NOT EXISTS documents (
+                    id INTEGER PRIMARY KEY,
+                    title TEXT,
+                    content TEXT,
+                    embedding BLOB
+                )
+                ''')
+                conn.commit()
+            logger.info("Initialized database schema")
+        except sqlite3.Error as e:
+            logger.error(f"Failed to initialize database schema: {e}")
+            raise RAGException(f"Database schema initialization failed: {e}")
+
+    def add_documents(self, documents: List[Tuple[str, str]]):
+        try:
+            embeddings = self.model.encode([content for _, content in documents])
+            with self.get_db_connection() as conn:
+                cursor = conn.cursor()
+                cursor.executemany(
+                    'INSERT INTO documents (title, content, embedding) VALUES (?, ?, ?)',
+                    [(title, content, embedding.tobytes()) for (title, content), embedding in zip(documents, embeddings)]
+                )
+                conn.commit()
+            logger.info(f"Added {len(documents)} documents in batch")
+        except Exception as e:
+            logger.error(f"Failed to add documents in batch: {e}")
+            raise RAGException(f"Batch document addition failed: {e}")
+
+    def get_documents(self) -> List[Tuple[int, str, str, np.ndarray]]:
+        try:
+            with self.get_db_connection() as conn:
+                cursor = conn.cursor()
+                cursor.execute('SELECT id, title, content, embedding FROM documents')
+                documents = [(id, title, content, np.frombuffer(embedding, dtype=np.float32))
+                             for id, title, content, embedding in cursor.fetchall()]
+            logger.info(f"Retrieved {len(documents)} documents")
+            return documents
+        except sqlite3.Error as e:
+            logger.error(f"Failed to retrieve documents: {e}")
+            raise RAGException(f"Document retrieval failed: {e}")
+
+    def close(self):
+        try:
+            self.conn.close()
+            logger.info("Closed database connection")
+        except sqlite3.Error as e:
+            logger.error(f"Error closing database connection: {e}")
+
+
+class StandardRAGSystem(BaseRAGSystem):
+    def get_relevant_documents(self, query: str, top_k: int = 3) -> List[Tuple[int, str, str, float]]:
+        try:
+            query_embedding = self.model.encode([query])[0]
+            documents = self.get_documents()
+            similarities = [
+                (id, title, content, cosine_similarity([query_embedding], [doc_embedding])[0][0])
+                for id, title, content, doc_embedding in documents
+            ]
+            similarities.sort(key=lambda x: x[3], reverse=True)
+            logger.info(f"Retrieved top {top_k} relevant documents for query")
+            return similarities[:top_k]
+        except Exception as e:
+            logger.error(f"Error in getting relevant documents: {e}")
+            raise RAGException(f"Retrieval of relevant documents failed: {e}")
+
+    def rag_query(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> str:
+        try:
+            relevant_docs = self.get_relevant_documents(query, top_k)
+            context = "\n\n".join([f"Title: {title}\nContent: {content}" for _, title, content, _ in relevant_docs])
+
+            llm_prompt = f"Based on the following context, please answer the query:\n\nContext:\n{context}\n\nQuery: {query}"
+
+            response = llm_function(llm_prompt)
+            logger.info("Generated response for query")
+            return response
+        except Exception as e:
+            logger.error(f"Error in RAG query: {e}")
+            raise RAGException(f"RAG query failed: {e}")
+
+
+class HyDERAGSystem(BaseRAGSystem):
+    def generate_hypothetical_document(self, query: str, llm_function: Callable[[str], str]) -> str:
+        try:
+            prompt = f"Given the question '{query}', write a short paragraph that would answer this question. Do not include the question itself in your response."
+            hypothetical_doc = llm_function(prompt)
+            logger.info("Generated hypothetical document")
+            return hypothetical_doc
+        except Exception as e:
+            logger.error(f"Error generating hypothetical document: {e}")
+            raise RAGException(f"Hypothetical document generation failed: {e}")
+
+    def get_relevant_documents(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> List[
+        Tuple[int, str, str, float]]:
+        try:
+            hypothetical_doc = self.generate_hypothetical_document(query, llm_function)
+            hyde_embedding = self.model.encode([hypothetical_doc])[0]
+
+            documents = self.get_documents()
+            similarities = [
+                (id, title, content, cosine_similarity([hyde_embedding], [doc_embedding])[0][0])
+                for id, title, content, doc_embedding in documents
+            ]
+            similarities.sort(key=lambda x: x[3], reverse=True)
+            logger.info(f"Retrieved top {top_k} relevant documents using HyDE")
+            return similarities[:top_k]
+        except Exception as e:
+            logger.error(f"Error in getting relevant documents with HyDE: {e}")
+            raise RAGException(f"HyDE retrieval of relevant documents failed: {e}")
+
+    def rag_query(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> str:
+        try:
+            relevant_docs = self.get_relevant_documents(query, llm_function, top_k)
+            context = "\n\n".join([f"Title: {title}\nContent: {content}" for _, title, content, _ in relevant_docs])
+
+            llm_prompt = f"Based on the following context, please answer the query:\n\nContext:\n{context}\n\nQuery: {query}"
+
+            response = llm_function(llm_prompt)
+            logger.info("Generated response for query using HyDE")
+            return response
+        except Exception as e:
+            logger.error(f"Error in HyDE RAG query: {e}")
+            raise RAGException(f"HyDE RAG query failed: {e}")
+
+
+# Example usage with error handling
+def mock_llm(prompt: str) -> str:
+    if "write a short paragraph" in prompt:
+        return "Paris, the capital of France, is renowned for its iconic Eiffel Tower and rich cultural heritage."
+    else:
+        return f"This is a mock LLM response for the prompt: {prompt}"
+
+
+def main():
+    use_hyde = False  # Set this to True when you want to enable HyDE
+
+    try:
+        if use_hyde:
+            rag_system = HyDERAGSystem('rag_database.db')
+            logger.info("Using HyDE RAG System")
+        else:
+            rag_system = StandardRAGSystem('rag_database.db')
+            logger.info("Using Standard RAG System")
+
+        # Add sample documents in batch
+        sample_docs = [
+            ("Paris", "Paris is the capital of France and is known for the Eiffel Tower."),
+            ("London", "London is the capital of the United Kingdom and home to Big Ben."),
+            ("Tokyo", "Tokyo is the capital of Japan and is famous for its bustling city life.")
+        ]
+
+        for title, content in sample_docs:
+            rag_system.add_document(title, content)
+
+        query = "What is the capital of France?"
+        result = rag_system.rag_query(query, mock_llm)
+        print(f"Query: {query}")
+        print(f"Result: {result}")
+
+    except RAGException as e:
+        logger.error(f"RAG system error: {e}")
+        print(f"An error occurred: {e}")
+    except Exception as e:
+        logger.error(f"Unexpected error: {e}")
+        print(f"An unexpected error occurred: {e}")
+    finally:
+        if 'rag_system' in locals():
+            rag_system.close()
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+####################################################################################
+# async:
+
+# import os
+# import asyncio
+# from typing import List, Tuple, Callable, Optional
+# import aiosqlite
+# import numpy as np
+# from sentence_transformers import SentenceTransformer
+# from sklearn.metrics.pairwise import cosine_similarity
+# import logging
+# from dotenv import load_dotenv
+#
+# load_dotenv()
+#
+# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# logger = logging.getLogger(__name__)
+#
+#
+# class RAGException(Exception):
+#     """Custom exception class for RAG-related errors"""
+#     pass
+#
+#
+# class BaseRAGSystem:
+#     def __init__(self, db_path: str, model_name: Optional[str] = None):
+#         """
+#         Initialize the RAG system.
+#
+#         :param db_path: Path to the SQLite database
+#         :param model_name: Name of the SentenceTransformer model to use
+#         """
+#         self.db_path = db_path
+#         self.model_name = model_name or os.getenv('DEFAULT_MODEL_NAME', 'all-MiniLM-L6-v2')
+#         try:
+#             self.model = SentenceTransformer(self.model_name)
+#             logger.info(f"Initialized SentenceTransformer with model: {self.model_name}")
+#         except Exception as e:
+#             logger.error(f"Failed to initialize SentenceTransformer: {e}")
+#             raise RAGException(f"Model initialization failed: {e}")
+#
+#     async def init_db(self):
+#         try:
+#             async with aiosqlite.connect(self.db_path) as db:
+#                 await db.execute('''
+#                 CREATE TABLE IF NOT EXISTS documents (
+#                     id INTEGER PRIMARY KEY,
+#                     title TEXT,
+#                     content TEXT,
+#                     embedding BLOB
+#                 )
+#                 ''')
+#                 await db.commit()
+#             logger.info("Initialized database schema")
+#         except aiosqlite.Error as e:
+#             logger.error(f"Failed to initialize database schema: {e}")
+#             raise RAGException(f"Database schema initialization failed: {e}")
+#
+#     async def add_documents(self, documents: List[Tuple[str, str]]):
+#         try:
+#             embeddings = self.model.encode([content for _, content in documents])
+#             async with aiosqlite.connect(self.db_path) as db:
+#                 await db.executemany(
+#                     'INSERT INTO documents (title, content, embedding) VALUES (?, ?, ?)',
+#                     [(title, content, embedding.tobytes()) for (title, content), embedding in
+#                      zip(documents, embeddings)]
+#                 )
+#                 await db.commit()
+#             logger.info(f"Added {len(documents)} documents in batch")
+#         except Exception as e:
+#             logger.error(f"Failed to add documents in batch: {e}")
+#             raise RAGException(f"Batch document addition failed: {e}")
+#
+# async def get_documents(self) -> List[Tuple[int, str, str, np.ndarray, str]]:
+#     try:
+#         async with aiosqlite.connect(self.db_path) as db:
+#             async with db.execute('SELECT id, title, content, embedding, source FROM documents') as cursor:
+#                 documents = [
+#                     (id, title, content, np.frombuffer(embedding, dtype=np.float32), source)
+#                     async for id, title, content, embedding, source in cursor
+#                 ]
+#         logger.info(f"Retrieved {len(documents)} documents")
+#         return documents
+#     except aiosqlite.Error as e:
+#         logger.error(f"Failed to retrieve documents: {e}")
+#         raise RAGException(f"Document retrieval failed: {e}")
+#
+#
+# class AsyncStandardRAGSystem(BaseRAGSystem):
+#     async def get_relevant_documents(self, query: str, top_k: int = 3) -> List[Tuple[int, str, str, float]]:
+#         try:
+#             query_embedding = self.model.encode([query])[0]
+#             documents = await self.get_documents()
+#             similarities = [
+#                 (id, title, content, cosine_similarity([query_embedding], [doc_embedding])[0][0])
+#                 for id, title, content, doc_embedding in documents
+#             ]
+#             similarities.sort(key=lambda x: x[3], reverse=True)
+#             logger.info(f"Retrieved top {top_k} relevant documents for query")
+#             return similarities[:top_k]
+#         except Exception as e:
+#             logger.error(f"Error in getting relevant documents: {e}")
+#             raise RAGException(f"Retrieval of relevant documents failed: {e}")
+#
+#     async def rag_query(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> str:
+#         try:
+#             relevant_docs = await self.get_relevant_documents(query, top_k)
+#             context = "\n\n".join([f"Title: {title}\nContent: {content}\nSource: {source}" for _, title, content, _, source in relevant_docs])
+#
+#             llm_prompt = f"Based on the following context, please answer the query. Include citations in your response using [Source] format:\n\nContext:\n{context}\n\nQuery: {query}"
+#
+#             response = llm_function(llm_prompt)
+#             logger.info("Generated response for query")
+#             return response
+#         except Exception as e:
+#             logger.error(f"Error in RAG query: {e}")
+#             raise RAGException(f"RAG query failed: {e}")
+#
+#
+# class AsyncHyDERAGSystem(BaseRAGSystem):
+#     async def generate_hypothetical_document(self, query: str, llm_function: Callable[[str], str]) -> str:
+#         try:
+#             prompt = f"Given the question '{query}', write a short paragraph that would answer this question. Do not include the question itself in your response."
+#             hypothetical_doc = llm_function(prompt)
+#             logger.info("Generated hypothetical document")
+#             return hypothetical_doc
+#         except Exception as e:
+#             logger.error(f"Error generating hypothetical document: {e}")
+#             raise RAGException(f"Hypothetical document generation failed: {e}")
+#
+#     async def get_relevant_documents(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> List[
+#         Tuple[int, str, str, float]]:
+#         try:
+#             hypothetical_doc = await self.generate_hypothetical_document(query, llm_function)
+#             hyde_embedding = self.model.encode([hypothetical_doc])[0]
+#
+#             documents = await self.get_documents()
+#             similarities = [
+#                 (id, title, content, cosine_similarity([hyde_embedding], [doc_embedding])[0][0])
+#                 for id, title, content, doc_embedding in documents
+#             ]
+#             similarities.sort(key=lambda x: x[3], reverse=True)
+#             logger.info(f"Retrieved top {top_k} relevant documents using HyDE")
+#             return similarities[:top_k]
+#         except Exception as e:
+#             logger.error(f"Error in getting relevant documents with HyDE: {e}")
+#             raise RAGException(f"HyDE retrieval of relevant documents failed: {e}")
+#
+#     async def rag_query(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> str:
+#         try:
+#             relevant_docs = await self.get_relevant_documents(query, llm_function, top_k)
+#             context = "\n\n".join([f"Title: {title}\nContent: {content}" for _, title, content, _ in relevant_docs])
+#
+#             llm_prompt = f"Based on the following context, please answer the query:\n\nContext:\n{context}\n\nQuery: {query}"
+#
+#             response = llm_function(llm_prompt)
+#             logger.info("Generated response for query using HyDE")
+#             return response
+#         except Exception as e:
+#             logger.error(f"Error in HyDE RAG query: {e}")
+#             raise RAGException(f"HyDE RAG query failed: {e}")
+#
+#
+# # Example usage with error handling
+# def mock_llm(prompt: str) -> str:
+#     if "write a short paragraph" in prompt:
+#         return "Paris, the capital of France, is renowned for its iconic Eiffel Tower and rich cultural heritage."
+#     else:
+#         return f"This is a mock LLM response for the prompt: {prompt}"
+#
+#
+# async def main():
+#     use_hyde = False  # Set this to True when you want to enable HyDE
+#
+#     try:
+#         if use_hyde:
+#             rag_system = AsyncHyDERAGSystem('rag_database.db')
+#             logger.info("Using Async HyDE RAG System")
+#         else:
+#             rag_system = AsyncStandardRAGSystem('rag_database.db')
+#             logger.info("Using Async Standard RAG System")
+#
+#         await rag_system.init_db()
+#
+#         # Add sample documents
+#         sample_docs = [
+#             ("Paris", "Paris is the capital of France and is known for the Eiffel Tower."),
+#             ("London", "London is the capital of the United Kingdom and home to Big Ben."),
+#             ("Tokyo", "Tokyo is the capital of Japan and is famous for its bustling city life.")
+#         ]
+#
+#         await rag_system.add_documents(sample_docs)
+#
+#         query = "What is the capital of France?"
+#         result = await rag_system.rag_query(query, mock_llm)
+#         print(f"Query: {query}")
+#         print(f"Result: {result}")
+#
+#     except RAGException as e:
+#         logger.error(f"RAG system error: {e}")
+#         print(f"An error occurred: {e}")
+#     except Exception as e:
+#         logger.error(f"Unexpected error: {e}")
+#         print(f"An unexpected error occurred: {e}")
+#
+#
+# if __name__ == "__main__":
+#     asyncio.run(main())
+
+
+
+#
+# from fastapi import FastAPI, HTTPException
+#
+# app = FastAPI()
+# rag_system = AsyncStandardRAGSystem('rag_database.db')
+#
+# @app.on_event("startup")
+# async def startup_event():
+#     await rag_system.init_db()
+#
+# @app.get("/query")
+# async def query(q: str):
+#     try:
+#         result = await rag_system.rag_query(q, mock_llm)
+#         return {"query": q, "result": result}
+#     except RAGException as e:
+#         raise HTTPException(status_code=500, detail=str(e))
+#
+
+
+############################################################################################
+# Using FAISS
+#
+#
+#
+# Update DB
+# async def init_db(self):
+#     try:
+#         async with aiosqlite.connect(self.db_path) as db:
+#             await db.execute('''
+#             CREATE TABLE IF NOT EXISTS documents (
+#                 id INTEGER PRIMARY KEY,
+#                 title TEXT,
+#                 content TEXT,
+#                 embedding BLOB,
+#                 source TEXT
+#             )
+#             ''')
+#             await db.commit()
+#         logger.info("Initialized database schema")
+#     except aiosqlite.Error as e:
+#         logger.error(f"Failed to initialize database schema: {e}")
+#         raise RAGException(f"Database schema initialization failed: {e}")
+#
+#
+
+# import os
+# import asyncio
+# from typing import List, Tuple, Callable, Optional
+# import aiosqlite
+# import numpy as np
+# from sentence_transformers import SentenceTransformer
+# import faiss
+# import logging
+# from dotenv import load_dotenv
+#
+# load_dotenv()
+#
+# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# logger = logging.getLogger(__name__)
+#
+#
+# class RAGException(Exception):
+#     """Custom exception class for RAG-related errors"""
+#     pass
+#
+#
+# class AsyncFAISSRAGSystem:
+#     def __init__(self, db_path: str, model_name: Optional[str] = None):
+#         self.db_path = db_path
+#         self.model_name = model_name or os.getenv('DEFAULT_MODEL_NAME', 'all-MiniLM-L6-v2')
+#         try:
+#             self.model = SentenceTransformer(self.model_name)
+#             logger.info(f"Initialized SentenceTransformer with model: {self.model_name}")
+#         except Exception as e:
+#             logger.error(f"Failed to initialize SentenceTransformer: {e}")
+#             raise RAGException(f"Model initialization failed: {e}")
+#
+#         self.index = None
+#         self.document_lookup = {}
+#
+#     async def init_db(self):
+#         try:
+#             async with aiosqlite.connect(self.db_path) as db:
+#                 await db.execute('''
+#                 CREATE TABLE IF NOT EXISTS documents (
+#                     id INTEGER PRIMARY KEY,
+#                     title TEXT,
+#                     content TEXT
+#                 )
+#                 ''')
+#                 await db.commit()
+#             logger.info("Initialized database schema")
+#         except aiosqlite.Error as e:
+#             logger.error(f"Failed to initialize database schema: {e}")
+#             raise RAGException(f"Database schema initialization failed: {e}")
+#
+# async def add_documents(self, documents: List[Tuple[str, str, str]]):
+#     try:
+#         embeddings = self.model.encode([content for _, content, _ in documents])
+#         async with aiosqlite.connect(self.db_path) as db:
+#             await db.executemany(
+#                 'INSERT INTO documents (title, content, embedding, source) VALUES (?, ?, ?, ?)',
+#                 [(title, content, embedding.tobytes(), source) for (title, content, source), embedding in
+#                  zip(documents, embeddings)]
+#             )
+#             await db.commit()
+#         logger.info(f"Added {len(documents)} documents in batch")
+#     except Exception as e:
+#         logger.error(f"Failed to add documents in batch: {e}")
+#         raise RAGException(f"Batch document addition failed: {e}")
+#
+#     async def get_relevant_documents(self, query: str, top_k: int = 3) -> List[Tuple[int, str, str, float, str]]:
+#         try:
+#             query_embedding = self.model.encode([query])[0]
+#             documents = await self.get_documents()
+#             similarities = [
+#                 (id, title, content, cosine_similarity([query_embedding], [doc_embedding])[0][0], source)
+#                 for id, title, content, doc_embedding, source in documents
+#             ]
+#             similarities.sort(key=lambda x: x[3], reverse=True)
+#             logger.info(f"Retrieved top {top_k} relevant documents for query")
+#             return similarities[:top_k]
+#         except Exception as e:
+#             logger.error(f"Error in getting relevant documents: {e}")
+#             raise RAGException(f"Retrieval of relevant documents failed: {e}")
+#
+#     async def rag_query(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> str:
+#         try:
+#             relevant_docs = await self.get_relevant_documents(query, top_k)
+#             context = "\n\n".join([f"Title: {title}\nContent: {content}" for _, title, content, _ in relevant_docs])
+#
+#             llm_prompt = f"Based on the following context, please answer the query:\n\nContext:\n{context}\n\nQuery: {query}"
+#
+#             response = llm_function(llm_prompt)
+#             logger.info("Generated response for query")
+#             return response
+#         except Exception as e:
+#             logger.error(f"Error in RAG query: {e}")
+#             raise RAGException(f"RAG query failed: {e}")
+#
+#
+# class AsyncFAISSHyDERAGSystem(AsyncFAISSRAGSystem):
+#     async def generate_hypothetical_document(self, query: str, llm_function: Callable[[str], str]) -> str:
+#         try:
+#             prompt = f"Given the question '{query}', write a short paragraph that would answer this question. Do not include the question itself in your response."
+#             hypothetical_doc = llm_function(prompt)
+#             logger.info("Generated hypothetical document")
+#             return hypothetical_doc
+#         except Exception as e:
+#             logger.error(f"Error generating hypothetical document: {e}")
+#             raise RAGException(f"Hypothetical document generation failed: {e}")
+#
+#     async def get_relevant_documents(self, query: str, llm_function: Callable[[str], str], top_k: int = 3) -> List[
+#         Tuple[int, str, str, float]]:
+#         try:
+#             hypothetical_doc = await self.generate_hypothetical_document(query, llm_function)
+#             hyde_embedding = self.model.encode([hypothetical_doc])[0]
+#
+#             distances, indices = self.index.search(np.array([hyde_embedding]), top_k)
+#
+#             results = []
+#             for i, idx in enumerate(indices[0]):
+#                 doc_id = list(self.document_lookup.keys())[idx]
+#                 title, content = self.document_lookup[doc_id]
+#                 results.append((doc_id, title, content, distances[0][i]))
+#
+#             logger.info(f"Retrieved top {top_k} relevant documents using HyDE")
+#             return results
+#         except Exception as e:
+#             logger.error(f"Error in getting relevant documents with HyDE: {e}")
+#             raise RAGException(f"HyDE retrieval of relevant documents failed: {e}")
+#
+#
+# # Example usage
+# def mock_llm(prompt: str) -> str:
+#     if "write a short paragraph" in prompt:
+#         return "Paris, the capital of France, is renowned for its iconic Eiffel Tower and rich cultural heritage."
+#     else:
+#         return f"This is a mock LLM response for the prompt: {prompt}"
+#
+#
+# async def main():
+#     use_hyde = False  # Set this to True when you want to enable HyDE
+#
+#     try:
+#         if use_hyde:
+#             rag_system = AsyncFAISSHyDERAGSystem('rag_database.db')
+#             logger.info("Using Async FAISS HyDE RAG System")
+#         else:
+#             rag_system = AsyncFAISSRAGSystem('rag_database.db')
+#             logger.info("Using Async FAISS RAG System")
+#
+#         await rag_system.init_db()
+#
+#         # Add sample documents
+#         sample_docs = [
+#             ("Paris", "Paris is the capital of France and is known for the Eiffel Tower."),
+#             ("London", "London is the capital of the United Kingdom and home to Big Ben."),
+#             ("Tokyo", "Tokyo is the capital of Japan and is famous for its bustling city life.")
+#         ]
+#
+#         await rag_system.add_documents(sample_docs)
+#
+#         query = "What is the capital of France?"
+#         result = await rag_system.rag_query(query, mock_llm)
+#         print(f"Query: {query}")
+#         print(f"Result: {result}")
+#
+#     except RAGException as e:
+#         logger.error(f"RAG system error: {e}")
+#         print(f"An error occurred: {e}")
+#     except Exception as e:
+#         logger.error(f"Unexpected error: {e}")
+#         print(f"An unexpected error occurred: {e}")
+#
+#
+# if __name__ == "__main__":
+#     asyncio.run(main())
+
+
+"""
+Key changes in this FAISS-integrated version:
+
+We've replaced the cosine similarity search with FAISS indexing and search.
+The add_documents method now adds embeddings to the FAISS index as well as storing documents in the SQLite database.
+We maintain a document_lookup dictionary to quickly retrieve document content based on FAISS search results.
+The get_relevant_documents method now uses FAISS for similarity search instead of computing cosine similarities manually.
+We've kept the asynchronous structure for database operations, while FAISS operations remain synchronous (as FAISS doesn't have built-in async support).
+
+Benefits of using FAISS:
+
+Scalability: FAISS can handle millions of vectors efficiently, making it suitable for large document collections.
+Speed: FAISS is optimized for fast similarity search, which can significantly improve query times as your dataset grows.
+Memory Efficiency: FAISS provides various index types that can trade off between search accuracy and memory usage, allowing you to optimize for your specific use case.
+
+Considerations:
+
+This implementation uses a simple IndexFlatL2 FAISS index, which performs exact search. For larger datasets, you might want to consider approximate search methods like IndexIVFFlat for better scalability.
+The current implementation keeps all document content in memory (in the document_lookup dictionary). For very large datasets, you might want to modify this to fetch document content from the database as needed.
+If you're dealing with a very large number of documents, you might want to implement batch processing for adding documents to the FAISS index.
+
+This FAISS-integrated version should provide better performance for similarity search, especially as your document collection grows larger
+"""
+
+
+###############################################################################################################
+# Web Search
+# Output from Sonnet 3.5 regarding how to add web searches to the RAG system
+# Integrating web search into your RAG system can significantly enhance its capabilities by providing up-to-date information. Here's how you can modify your RAG system to include web search:
+#
+# First, you'll need to choose a web search API. Some popular options include:
+#
+# Google Custom Search API
+# Bing Web Search API
+# DuckDuckGo API
+# SerpAPI (which can interface with multiple search engines)
+#
+#
+#
+# For this example, let's use the DuckDuckGo API, as it's free and doesn't require authentication.
+#
+# Install the required library:
+# `pip install duckduckgo-search`
+#
+# Add a new method to your RAG system for web search:
+# ```
+# from duckduckgo_search import ddg
+#
+# class AsyncRAGSystem:
+#     # ... (existing code) ...
+#
+#     async def web_search(self, query: str, num_results: int = 3) -> List[Dict[str, str]]:
+#         try:
+#             results = ddg(query, max_results=num_results)
+#             return [{'title': r['title'], 'content': r['body'], 'source': r['href']} for r in results]
+#         except Exception as e:
+#             logger.error(f"Error in web search: {e}")
+#             raise RAGException(f"Web search failed: {e}")
+#
+#     async def add_web_results_to_db(self, results: List[Dict[str, str]]):
+#         try:
+#             documents = [(r['title'], r['content'], r['source']) for r in results]
+#             await self.add_documents(documents)
+#             logger.info(f"Added {len(documents)} web search results to the database")
+#         except Exception as e:
+#             logger.error(f"Error adding web search results to database: {e}")
+#             raise RAGException(f"Adding web search results failed: {e}")
+#
+#     async def rag_query_with_web_search(self, query: str, llm_function: Callable[[str], str], top_k: int = 3,
+#                                         use_web_search: bool = True, num_web_results: int = 3) -> str:
+#         try:
+#             if use_web_search:
+#                 web_results = await self.web_search(query, num_web_results)
+#                 await self.add_web_results_to_db(web_results)
+#
+#             relevant_docs = await self.get_relevant_documents(query, top_k)
+#             context = "\n\n".join([f"Title: {title}\nContent: {content}\nSource: {source}"
+#                                    for _, title, content, _, source in relevant_docs])
+#
+#             llm_prompt = f"Based on the following context, please answer the query. Include citations in your response using [Source] format:\n\nContext:\n{context}\n\nQuery: {query}"
+#
+#             response = llm_function(llm_prompt)
+#             logger.info("Generated response for query with web search")
+#             return response
+#         except Exception as e:
+#             logger.error(f"Error in RAG query with web search: {e}")
+#             raise RAGException(f"RAG query with web search failed: {e}")
+# ```
+#
+# Update your main function to use the new web search capability:
+# ```
+# async def main():
+#     use_hyde = False  # Set this to True when you want to enable HyDE
+#     use_web_search = True  # Set this to False if you don't want to use web search
+#
+#     try:
+#         if use_hyde:
+#             rag_system = AsyncHyDERAGSystem('rag_database.db')
+#             logger.info("Using Async HyDE RAG System")
+#         else:
+#             rag_system = AsyncStandardRAGSystem('rag_database.db')
+#             logger.info("Using Async Standard RAG System")
+#
+#         await rag_system.init_db()
+#
+#         # Add sample documents
+#         sample_docs = [
+#             ("Paris", "Paris is the capital of France and is known for the Eiffel Tower.", "Local Database"),
+#             ("London", "London is the capital of the United Kingdom and home to Big Ben.", "Local Database"),
+#             ("Tokyo", "Tokyo is the capital of Japan and is famous for its bustling city life.", "Local Database")
+#         ]
+#
+#         await rag_system.add_documents(sample_docs)
+#
+#         query = "What is the capital of France?"
+#         result = await rag_system.rag_query_with_web_search(query, mock_llm, use_web_search=use_web_search)
+#         print(f"Query: {query}")
+#         print(f"Result: {result}")
+#
+#     except RAGException as e:
+#         logger.error(f"RAG system error: {e}")
+#         print(f"An error occurred: {e}")
+#     except Exception as e:
+#         logger.error(f"Unexpected error: {e}")
+#         print(f"An unexpected error occurred: {e}")
+# ```
+#
+#
+# This implementation does the following:
+#
+# It adds a web_search method that uses the DuckDuckGo API to perform web searches.
+# It adds an add_web_results_to_db method that adds the web search results to your existing database.
+# It modifies the rag_query method (now called rag_query_with_web_search) to optionally perform a web search before retrieving relevant documents.
+#
+# When use_web_search is set to True, the system will:
+#
+# Perform a web search for the given query.
+# Add the web search results to the database.
+# Retrieve relevant documents (which now may include the newly added web search results).
+# Use these documents to generate a response.
+#
+# This approach allows your RAG system to combine information from your existing database with fresh information from the web, potentially providing more up-to-date and comprehensive answers.
+# Remember to handle rate limiting and respect the terms of service of the web search API you choose to use. Also, be aware that adding web search results to your database will increase its size over time, so you may need to implement a strategy to manage this growth (e.g., removing old web search results periodically).
+
+

App_Function_Libraries/SQLite_DB.py

@@ -0,0 +1,973 @@
+# SQLite_DB.py
+#########################################
+# SQLite_DB Library
+# This library is used to perform any/all DB operations related to SQLite.
+#
+####
+
+####################
+# Function List
+# FIXME - UPDATE Function Arguments
+# 1. get_connection(self)
+# 2. execute_query(self, query: str, params: Tuple = ())
+# 3. create_tables()
+# 4. add_keyword(keyword: str)
+# 5. delete_keyword(keyword: str)
+# 6. add_media_with_keywords(url, title, media_type, content, keywords, prompt, summary, transcription_model, author, ingestion_date)
+# 7. fetch_all_keywords()
+# 8. keywords_browser_interface()
+# 9. display_keywords()
+# 10. export_keywords_to_csv()
+# 11. browse_items(search_query, search_type)
+# 12. fetch_item_details(media_id: int)
+# 13. add_media_version(media_id: int, prompt: str, summary: str)
+# 14. search_db(search_query: str, search_fields: List[str], keywords: str, page: int = 1, results_per_page: int = 10)
+# 15. search_and_display(search_query, search_fields, keywords, page)
+# 16. display_details(index, results)
+# 17. get_details(index, dataframe)
+# 18. format_results(results)
+# 19. export_to_csv(search_query: str, search_fields: List[str], keyword: str, page: int = 1, results_per_file: int = 1000)
+# 20. is_valid_url(url: str) -> bool
+# 21. is_valid_date(date_string: str) -> bool
+# 22. add_media_to_database(url, info_dict, segments, summary, keywords, custom_prompt_input, whisper_model)
+# 23. create_prompts_db()
+# 24. add_prompt(name, details, system, user=None)
+# 25. fetch_prompt_details(name)
+# 26. list_prompts()
+# 27. insert_prompt_to_db(title, description, system_prompt, user_prompt)
+# 28. update_media_content(media_id: int, content: str, prompt: str, summary: str)
+# 29. search_media_database(query: str) -> List[Tuple[int, str, str]]
+# 30. load_media_content(media_id: int)
+# 31.
+# 32.
+#
+#
+#####################
+#
+# Import necessary libraries
+import csv
+import logging
+import os
+import re
+import sqlite3
+import time
+from contextlib import contextmanager
+from datetime import datetime
+from typing import List, Tuple
+# Third-Party Libraries
+import gradio as gr
+import pandas as pd
+# Import Local Libraries
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+# Set up logging
+#logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+#logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+# Custom exceptions
+class DatabaseError(Exception):
+    pass
+
+
+class InputError(Exception):
+    pass
+
+
+# Database connection function with connection pooling
+class Database:
+    def __init__(self, db_name=None):
+        self.db_name = db_name or os.getenv('DB_NAME', 'media_summary.db')
+        self.pool = []
+        self.pool_size = 10
+
+    @contextmanager
+    def get_connection(self):
+        retry_count = 5
+        retry_delay = 1
+        conn = None
+        while retry_count > 0:
+            try:
+                conn = self.pool.pop() if self.pool else sqlite3.connect(self.db_name, check_same_thread=False)
+                yield conn
+                self.pool.append(conn)
+                return
+            except sqlite3.OperationalError as e:
+                if 'database is locked' in str(e):
+                    logging.warning(f"Database is locked, retrying in {retry_delay} seconds...")
+                    retry_count -= 1
+                    time.sleep(retry_delay)
+                else:
+                    raise DatabaseError(f"Database error: {e}")
+            except Exception as e:
+                raise DatabaseError(f"Unexpected error: {e}")
+            finally:
+                # Ensure the connection is returned to the pool even on failure
+                if conn:
+                    self.pool.append(conn)
+        raise DatabaseError("Database is locked and retries have been exhausted")
+
+    def execute_query(self, query: str, params: Tuple = ()) -> None:
+        with self.get_connection() as conn:
+            try:
+                cursor = conn.cursor()
+                cursor.execute(query, params)
+                conn.commit()
+            except sqlite3.Error as e:
+                raise DatabaseError(f"Database error: {e}, Query: {query}")
+
+db = Database()
+
+
+# Function to create tables with the new media schema
+def create_tables() -> None:
+    table_queries = [
+        '''
+        CREATE TABLE IF NOT EXISTS Media (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            url TEXT,
+            title TEXT NOT NULL,
+            type TEXT NOT NULL,
+            content TEXT,
+            author TEXT,
+            ingestion_date TEXT,
+            prompt TEXT,
+            summary TEXT,
+            transcription_model TEXT
+        )
+        ''',
+        '''
+        CREATE TABLE IF NOT EXISTS Keywords (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            keyword TEXT NOT NULL UNIQUE
+        )
+        ''',
+        '''
+        CREATE TABLE IF NOT EXISTS MediaKeywords (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            media_id INTEGER NOT NULL,
+            keyword_id INTEGER NOT NULL,
+            FOREIGN KEY (media_id) REFERENCES Media(id),
+            FOREIGN KEY (keyword_id) REFERENCES Keywords(id)
+        )
+        ''',
+        '''
+        CREATE TABLE IF NOT EXISTS MediaVersion (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            media_id INTEGER NOT NULL,
+            version INTEGER NOT NULL,
+            prompt TEXT,
+            summary TEXT,
+            created_at TEXT NOT NULL,
+            FOREIGN KEY (media_id) REFERENCES Media(id)
+        )
+        ''',
+        '''
+        CREATE TABLE IF NOT EXISTS MediaModifications (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            media_id INTEGER NOT NULL,
+            prompt TEXT,
+            summary TEXT,
+            modification_date TEXT,
+            FOREIGN KEY (media_id) REFERENCES Media(id)
+        )
+        ''',
+        '''
+        CREATE VIRTUAL TABLE IF NOT EXISTS media_fts USING fts5(title, content);
+        ''',
+        '''
+        CREATE VIRTUAL TABLE IF NOT EXISTS keyword_fts USING fts5(keyword);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_media_title ON Media(title);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_media_type ON Media(type);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_media_author ON Media(author);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_media_ingestion_date ON Media(ingestion_date);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_keywords_keyword ON Keywords(keyword);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_mediakeywords_media_id ON MediaKeywords(media_id);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_mediakeywords_keyword_id ON MediaKeywords(keyword_id);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_media_version_media_id ON MediaVersion(media_id);
+        ''',
+        '''
+        CREATE INDEX IF NOT EXISTS idx_mediamodifications_media_id ON MediaModifications(media_id);
+        ''',
+        '''
+        CREATE UNIQUE INDEX IF NOT EXISTS idx_unique_media_url ON Media(url);
+        ''',
+        '''
+        CREATE UNIQUE INDEX IF NOT EXISTS idx_unique_media_keyword ON MediaKeywords(media_id, keyword_id);
+        '''
+    ]
+    for query in table_queries:
+        db.execute_query(query)
+
+    logging.info("All tables and indexes created successfully.")
+
+create_tables()
+
+
+#######################################################################################################################
+# Keyword-related Functions
+#
+
+# Function to add a keyword
+def add_keyword(keyword: str) -> int:
+    keyword = keyword.strip().lower()
+    with db.get_connection() as conn:
+        cursor = conn.cursor()
+        try:
+            cursor.execute('INSERT OR IGNORE INTO Keywords (keyword) VALUES (?)', (keyword,))
+            cursor.execute('SELECT id FROM Keywords WHERE keyword = ?', (keyword,))
+            keyword_id = cursor.fetchone()[0]
+            cursor.execute('INSERT OR IGNORE INTO keyword_fts (rowid, keyword) VALUES (?, ?)', (keyword_id, keyword))
+            logging.info(f"Keyword '{keyword}' added to keyword_fts with ID: {keyword_id}")
+            conn.commit()
+            return keyword_id
+        except sqlite3.IntegrityError as e:
+            logging.error(f"Integrity error adding keyword: {e}")
+            raise DatabaseError(f"Integrity error adding keyword: {e}")
+        except sqlite3.Error as e:
+            logging.error(f"Error adding keyword: {e}")
+            raise DatabaseError(f"Error adding keyword: {e}")
+
+
+# Function to delete a keyword
+def delete_keyword(keyword: str) -> str:
+    keyword = keyword.strip().lower()
+    with db.get_connection() as conn:
+        cursor = conn.cursor()
+        try:
+            cursor.execute('SELECT id FROM Keywords WHERE keyword = ?', (keyword,))
+            keyword_id = cursor.fetchone()
+            if keyword_id:
+                cursor.execute('DELETE FROM Keywords WHERE keyword = ?', (keyword,))
+                cursor.execute('DELETE FROM keyword_fts WHERE rowid = ?', (keyword_id[0],))
+                conn.commit()
+                return f"Keyword '{keyword}' deleted successfully."
+            else:
+                return f"Keyword '{keyword}' not found."
+        except sqlite3.Error as e:
+            raise DatabaseError(f"Error deleting keyword: {e}")
+
+
+
+# Function to add media with keywords
+def add_media_with_keywords(url, title, media_type, content, keywords, prompt, summary, transcription_model, author,
+                            ingestion_date):
+    # Set default values for missing fields
+    url = url or 'Unknown'
+    title = title or 'Untitled'
+    media_type = media_type or 'Unknown'
+    content = content or 'No content available'
+    keywords = keywords or 'default'
+    prompt = prompt or 'No prompt available'
+    summary = summary or 'No summary available'
+    transcription_model = transcription_model or 'Unknown'
+    author = author or 'Unknown'
+    ingestion_date = ingestion_date or datetime.now().strftime('%Y-%m-%d')
+
+    # Ensure URL is valid
+    if not is_valid_url(url):
+        url = 'localhost'
+
+    if media_type not in ['article', 'audio', 'document', 'obsidian_note', 'podcast', 'text', 'video', 'unknown']:
+        raise InputError("Invalid media type. Allowed types: article, audio file, document, obsidian_note podcast, text, video, unknown.")
+
+    if ingestion_date and not is_valid_date(ingestion_date):
+        raise InputError("Invalid ingestion date format. Use YYYY-MM-DD.")
+
+    # Handle keywords as either string or list
+    if isinstance(keywords, str):
+        keyword_list = [keyword.strip().lower() for keyword in keywords.split(',')]
+    elif isinstance(keywords, list):
+        keyword_list = [keyword.strip().lower() for keyword in keywords]
+    else:
+        keyword_list = ['default']
+
+    logging.info(f"Adding/updating media: URL={url}, Title={title}, Type={media_type}")
+    logging.debug(f"Content (first 500 chars): {content[:500]}...")
+    logging.debug(f"Keywords: {keyword_list}")
+    logging.info(f"Prompt: {prompt}")
+    logging.info(f"Summary: {summary}")
+    logging.info(f"Author: {author}")
+    logging.info(f"Ingestion Date: {ingestion_date}")
+    logging.info(f"Transcription Model: {transcription_model}")
+
+    try:
+        with db.get_connection() as conn:
+            conn.execute("BEGIN TRANSACTION")
+            cursor = conn.cursor()
+
+            # Check if media already exists
+            cursor.execute('SELECT id FROM Media WHERE url = ?', (url,))
+            existing_media = cursor.fetchone()
+
+            if existing_media:
+                media_id = existing_media[0]
+                logging.info(f"Updating existing media with ID: {media_id}")
+
+                cursor.execute('''
+                UPDATE Media 
+                SET content = ?, transcription_model = ?, title = ?, type = ?, author = ?, ingestion_date = ?
+                WHERE id = ?
+                ''', (content, transcription_model, title, media_type, author, ingestion_date, media_id))
+            else:
+                logging.info("Creating new media entry")
+
+                cursor.execute('''
+                INSERT INTO Media (url, title, type, content, author, ingestion_date, transcription_model)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
+                ''', (url, title, media_type, content, author, ingestion_date, transcription_model))
+                media_id = cursor.lastrowid
+
+            logging.info(f"Adding new modification to MediaModifications for media ID: {media_id}")
+            cursor.execute('''
+            INSERT INTO MediaModifications (media_id, prompt, summary, modification_date)
+            VALUES (?, ?, ?, ?)
+            ''', (media_id, prompt, summary, ingestion_date))
+            logger.info("New modification added to MediaModifications")
+
+            # Insert keywords and associate with media item
+            logging.info("Processing keywords")
+            for keyword in keyword_list:
+                keyword = keyword.strip().lower()
+                cursor.execute('INSERT OR IGNORE INTO Keywords (keyword) VALUES (?)', (keyword,))
+                cursor.execute('SELECT id FROM Keywords WHERE keyword = ?', (keyword,))
+                keyword_id = cursor.fetchone()[0]
+                cursor.execute('INSERT OR IGNORE INTO MediaKeywords (media_id, keyword_id) VALUES (?, ?)',
+                               (media_id, keyword_id))
+
+            # Update full-text search index
+            logging.info("Updating full-text search index")
+            cursor.execute('INSERT OR REPLACE INTO media_fts (rowid, title, content) VALUES (?, ?, ?)',
+                           (media_id, title, content))
+
+            logging.info("Adding new media version")
+            add_media_version(media_id, prompt, summary)
+
+            conn.commit()
+            logging.info(f"Media '{title}' successfully added/updated with ID: {media_id}")
+
+            return f"Media '{title}' added/updated successfully with keywords: {', '.join(keyword_list)}"
+
+    except sqlite3.Error as e:
+        conn.rollback()
+        logging.error(f"SQL Error: {e}")
+        raise DatabaseError(f"Error adding media with keywords: {e}")
+    except Exception as e:
+        conn.rollback()
+        logging.error(f"Unexpected Error: {e}")
+        raise DatabaseError(f"Unexpected error: {e}")
+
+
+def fetch_all_keywords() -> List[str]:
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute('SELECT keyword FROM Keywords')
+            keywords = [row[0] for row in cursor.fetchall()]
+            return keywords
+    except sqlite3.Error as e:
+        raise DatabaseError(f"Error fetching keywords: {e}")
+
+def keywords_browser_interface():
+    keywords = fetch_all_keywords()
+    return gr.Markdown("\n".join(f"- {keyword}" for keyword in keywords))
+
+def display_keywords():
+    try:
+        keywords = fetch_all_keywords()
+        return "\n".join(keywords) if keywords else "No keywords found."
+    except DatabaseError as e:
+        return str(e)
+
+
+def export_keywords_to_csv():
+    try:
+        keywords = fetch_all_keywords()
+        if not keywords:
+            return None, "No keywords found in the database."
+
+        filename = "keywords.csv"
+        with open(filename, 'w', newline='', encoding='utf-8') as file:
+            writer = csv.writer(file)
+            writer.writerow(["Keyword"])
+            for keyword in keywords:
+                writer.writerow([keyword])
+
+        return filename, f"Keywords exported to {filename}"
+    except Exception as e:
+        logger.error(f"Error exporting keywords to CSV: {e}")
+        return None, f"Error exporting keywords: {e}"
+
+
+# Function to fetch items based on search query and type
+def browse_items(search_query, search_type):
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            if search_type == 'Title':
+                cursor.execute("SELECT id, title, url FROM Media WHERE title LIKE ?", (f'%{search_query}%',))
+            elif search_type == 'URL':
+                cursor.execute("SELECT id, title, url FROM Media WHERE url LIKE ?", (f'%{search_query}%',))
+            results = cursor.fetchall()
+            return results
+    except sqlite3.Error as e:
+        raise Exception(f"Error fetching items by {search_type}: {e}")
+
+
+# Function to fetch item details
+def fetch_item_details(media_id: int):
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT prompt, summary 
+                FROM MediaModifications 
+                WHERE media_id = ? 
+                ORDER BY modification_date DESC 
+                LIMIT 1
+            """, (media_id,))
+            prompt_summary_result = cursor.fetchone()
+            cursor.execute("SELECT content FROM Media WHERE id = ?", (media_id,))
+            content_result = cursor.fetchone()
+
+            prompt = prompt_summary_result[0] if prompt_summary_result else ""
+            summary = prompt_summary_result[1] if prompt_summary_result else ""
+            content = content_result[0] if content_result else ""
+
+            return content, prompt, summary
+    except sqlite3.Error as e:
+        logging.error(f"Error fetching item details: {e}")
+        return "", "", ""  # Return empty strings if there's an error
+
+#
+#
+#######################################################################################################################
+
+
+
+
+# Function to add a version of a prompt and summary
+def add_media_version(media_id: int, prompt: str, summary: str) -> None:
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+
+            # Get the current version number
+            cursor.execute('SELECT MAX(version) FROM MediaVersion WHERE media_id = ?', (media_id,))
+            current_version = cursor.fetchone()[0] or 0
+
+            # Insert the new version
+            cursor.execute('''
+            INSERT INTO MediaVersion (media_id, version, prompt, summary, created_at)
+            VALUES (?, ?, ?, ?, ?)
+            ''', (media_id, current_version + 1, prompt, summary, datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+            conn.commit()
+    except sqlite3.Error as e:
+        raise DatabaseError(f"Error adding media version: {e}")
+
+
+# Function to search the database with advanced options, including keyword search and full-text search
+def search_db(search_query: str, search_fields: List[str], keywords: str, page: int = 1, results_per_page: int = 10):
+    if page < 1:
+        raise ValueError("Page number must be 1 or greater.")
+
+    # Prepare keywords by splitting and trimming
+    keywords = [keyword.strip().lower() for keyword in keywords.split(',') if keyword.strip()]
+
+    with db.get_connection() as conn:
+        cursor = conn.cursor()
+        offset = (page - 1) * results_per_page
+
+        # Prepare the search conditions for general fields
+        search_conditions = []
+        params = []
+
+        for field in search_fields:
+            if search_query:  # Ensure there's a search query before adding this condition
+                search_conditions.append(f"Media.{field} LIKE ?")
+                params.append(f'%{search_query}%')
+
+        # Prepare the conditions for keywords filtering
+        keyword_conditions = []
+        for keyword in keywords:
+            keyword_conditions.append(
+                f"EXISTS (SELECT 1 FROM MediaKeywords mk JOIN Keywords k ON mk.keyword_id = k.id WHERE mk.media_id = Media.id AND k.keyword LIKE ?)")
+            params.append(f'%{keyword}%')
+
+        # Combine all conditions
+        where_clause = " AND ".join(
+            search_conditions + keyword_conditions) if search_conditions or keyword_conditions else "1=1"
+
+        # Complete the query
+        query = f'''
+        SELECT DISTINCT Media.id, Media.url, Media.title, Media.type, Media.content, Media.author, Media.ingestion_date, 
+               MediaModifications.prompt, MediaModifications.summary
+        FROM Media
+        LEFT JOIN MediaModifications ON Media.id = MediaModifications.media_id
+        WHERE {where_clause}
+        ORDER BY Media.ingestion_date DESC
+        LIMIT ? OFFSET ?
+        '''
+        params.extend([results_per_page, offset])
+
+        cursor.execute(query, params)
+        results = cursor.fetchall()
+
+        return results
+
+
+# Gradio function to handle user input and display results with pagination, with better feedback
+def search_and_display(search_query, search_fields, keywords, page):
+    results = search_db(search_query, search_fields, keywords, page)
+
+    if isinstance(results, pd.DataFrame):
+        # Convert DataFrame to a list of tuples or lists
+        processed_results = results.values.tolist()  # This converts DataFrame rows to lists
+    elif isinstance(results, list):
+        # Ensure that each element in the list is itself a list or tuple (not a dictionary)
+        processed_results = [list(item.values()) if isinstance(item, dict) else item for item in results]
+    else:
+        raise TypeError("Unsupported data type for results")
+
+    return processed_results
+
+
+def display_details(index, results):
+    if index is None or results is None:
+        return "Please select a result to view details."
+
+    try:
+        # Ensure the index is an integer and access the row properly
+        index = int(index)
+        if isinstance(results, pd.DataFrame):
+            if index >= len(results):
+                return "Index out of range. Please select a valid index."
+            selected_row = results.iloc[index]
+        else:
+            # If results is not a DataFrame, but a list (assuming list of dicts)
+            selected_row = results[index]
+    except ValueError:
+        return "Index must be an integer."
+    except IndexError:
+        return "Index out of range. Please select a valid index."
+
+    # Build HTML output safely
+    details_html = f"""
+    <h3>{selected_row.get('Title', 'No Title')}</h3>
+    <p><strong>URL:</strong> {selected_row.get('URL', 'No URL')}</p>
+    <p><strong>Type:</strong> {selected_row.get('Type', 'No Type')}</p>
+    <p><strong>Author:</strong> {selected_row.get('Author', 'No Author')}</p>
+    <p><strong>Ingestion Date:</strong> {selected_row.get('Ingestion Date', 'No Date')}</p>
+    <p><strong>Prompt:</strong> {selected_row.get('Prompt', 'No Prompt')}</p>
+    <p><strong>Summary:</strong> {selected_row.get('Summary', 'No Summary')}</p>
+    <p><strong>Content:</strong> {selected_row.get('Content', 'No Content')}</p>
+    """
+    return details_html
+
+
+def get_details(index, dataframe):
+    if index is None or dataframe is None or index >= len(dataframe):
+        return "Please select a result to view details."
+    row = dataframe.iloc[index]
+    details = f"""
+    <h3>{row['Title']}</h3>
+    <p><strong>URL:</strong> {row['URL']}</p>
+    <p><strong>Type:</strong> {row['Type']}</p>
+    <p><strong>Author:</strong> {row['Author']}</p>
+    <p><strong>Ingestion Date:</strong> {row['Ingestion Date']}</p>
+    <p><strong>Prompt:</strong> {row['Prompt']}</p>
+    <p><strong>Summary:</strong> {row['Summary']}</p>
+    <p><strong>Content:</strong></p>
+    <pre>{row['Content']}</pre>
+    """
+    return details
+
+
+def format_results(results):
+    if not results:
+        return pd.DataFrame(columns=['URL', 'Title', 'Type', 'Content', 'Author', 'Ingestion Date', 'Prompt', 'Summary'])
+
+    df = pd.DataFrame(results, columns=['URL', 'Title', 'Type', 'Content', 'Author', 'Ingestion Date', 'Prompt', 'Summary'])
+    logging.debug(f"Formatted DataFrame: {df}")
+
+    return df
+
+
+# Function to export search results to CSV or markdown with pagination
+def export_to_file(search_query: str, search_fields: List[str], keyword: str, page: int = 1, results_per_file: int = 1000, export_format: str = 'csv'):
+    try:
+        results = search_db(search_query, search_fields, keyword, page, results_per_file)
+        if not results:
+            return "No results found to export."
+
+        # Create an 'exports' directory if it doesn't exist
+        if not os.path.exists('exports'):
+            os.makedirs('exports')
+
+        if export_format == 'csv':
+            filename = f'exports/search_results_page_{page}.csv'
+            with open(filename, 'w', newline='', encoding='utf-8') as file:
+                writer = csv.writer(file)
+                writer.writerow(['URL', 'Title', 'Type', 'Content', 'Author', 'Ingestion Date', 'Prompt', 'Summary'])
+                for row in results:
+                    writer.writerow(row)
+        elif export_format == 'markdown':
+            filename = f'exports/search_results_page_{page}.md'
+            with open(filename, 'w', encoding='utf-8') as file:
+                for item in results:
+                    markdown_content = convert_to_markdown({
+                        'title': item[1],
+                        'url': item[0],
+                        'type': item[2],
+                        'content': item[3],
+                        'author': item[4],
+                        'ingestion_date': item[5],
+                        'summary': item[7],
+                        'keywords': item[8].split(',') if item[8] else []
+                    })
+                    file.write(markdown_content)
+                    file.write("\n---\n\n")  # Separator between items
+        else:
+            return f"Unsupported export format: {export_format}"
+
+        return f"Results exported to {filename}"
+    except (DatabaseError, InputError) as e:
+        return str(e)
+
+
+# Helper function to validate URL format
+def is_valid_url(url: str) -> bool:
+    regex = re.compile(
+        r'^(?:http|ftp)s?://'  # http:// or https://
+        r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|'  # domain...
+        r'localhost|'  # localhost...
+        r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}|'  # ...or ipv4
+        r'\[?[A-F0-9]*:[A-F0-9:]+\]?)'  # ...or ipv6
+        r'(?::\d+)?'  # optional port
+        r'(?:/?|[/?]\S+)$', re.IGNORECASE)
+    return re.match(regex, url) is not None
+
+
+# Helper function to validate date format
+def is_valid_date(date_string: str) -> bool:
+    try:
+        datetime.strptime(date_string, '%Y-%m-%d')
+        return True
+    except ValueError:
+        return False
+
+
+# Add ingested media to DB
+def add_media_to_database(url, info_dict, segments, summary, keywords, custom_prompt_input, whisper_model, media_type='video'):
+    try:
+        # Extract content from segments
+        if isinstance(segments, list):
+            content = ' '.join([segment.get('Text', '') for segment in segments if 'Text' in segment])
+        elif isinstance(segments, dict):
+            content = segments.get('text', '') or segments.get('content', '')
+        else:
+            content = str(segments)
+
+        logging.debug(f"Extracted content (first 500 chars): {content[:500]}")
+
+        # Set default custom prompt if not provided
+        if custom_prompt_input is None:
+            custom_prompt_input = """
+            You are a bulleted notes specialist. ```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.
+            **Bulleted Note Creation Guidelines**
+
+            **Headings**:
+            - Based on referenced topics, not categories like quotes or terms
+            - Surrounded by **bold** formatting 
+            - Not listed as bullet points
+            - No space between headings and list items underneath
+
+            **Emphasis**:
+            - **Important terms** set in bold font
+            - **Text ending in a colon**: also bolded
+
+            **Review**:
+            - Ensure adherence to specified format
+            - Do not reference these instructions in your response.</s>[INST] {{ .Prompt }} [/INST]"""
+
+        logging.info(f"Adding media to database: URL={url}, Title={info_dict.get('title', 'Untitled')}, Type={media_type}")
+
+        result = add_media_with_keywords(
+            url=url,
+            title=info_dict.get('title', 'Untitled'),
+            media_type=media_type,
+            content=content,
+            keywords=','.join(keywords) if isinstance(keywords, list) else keywords,
+            prompt=custom_prompt_input or 'No prompt provided',
+            summary=summary or 'No summary provided',
+            transcription_model=whisper_model,
+            author=info_dict.get('uploader', 'Unknown'),
+            ingestion_date=datetime.now().strftime('%Y-%m-%d')
+        )
+
+        logging.info(f"Media added successfully: {result}")
+        return result
+
+    except Exception as e:
+        logging.error(f"Error in add_media_to_database: {str(e)}")
+        raise
+
+
+#
+#
+#######################################################################################################################
+
+
+
+
+#######################################################################################################################
+# Functions to manage prompts DB
+#
+
+def create_prompts_db():
+    conn = sqlite3.connect('prompts.db')
+    cursor = conn.cursor()
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS Prompts (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT NOT NULL UNIQUE,
+            details TEXT,
+            system TEXT,
+            user TEXT
+        )
+    ''')
+    conn.commit()
+    conn.close()
+
+create_prompts_db()
+
+
+def add_prompt(name, details, system, user=None):
+    try:
+        conn = sqlite3.connect('prompts.db')
+        cursor = conn.cursor()
+        cursor.execute('''
+            INSERT INTO Prompts (name, details, system, user)
+            VALUES (?, ?, ?, ?)
+        ''', (name, details, system, user))
+        conn.commit()
+        conn.close()
+        return "Prompt added successfully."
+    except sqlite3.IntegrityError:
+        return "Prompt with this name already exists."
+    except sqlite3.Error as e:
+        return f"Database error: {e}"
+
+def fetch_prompt_details(name):
+    conn = sqlite3.connect('prompts.db')
+    cursor = conn.cursor()
+    cursor.execute('''
+        SELECT name, details, system, user
+        FROM Prompts
+        WHERE name = ?
+    ''', (name,))
+    result = cursor.fetchone()
+    conn.close()
+    return result
+
+def list_prompts():
+    conn = sqlite3.connect('prompts.db')
+    cursor = conn.cursor()
+    cursor.execute('''
+        SELECT name
+        FROM Prompts
+    ''')
+    results = cursor.fetchall()
+    conn.close()
+    return [row[0] for row in results]
+
+def insert_prompt_to_db(title, description, system_prompt, user_prompt):
+    result = add_prompt(title, description, system_prompt, user_prompt)
+    return result
+
+
+
+
+#
+#
+#######################################################################################################################
+
+
+def update_media_content(selected_item, item_mapping, content_input, prompt_input, summary_input):
+    try:
+        if selected_item and item_mapping and selected_item in item_mapping:
+            media_id = item_mapping[selected_item]
+
+            with db.get_connection() as conn:
+                cursor = conn.cursor()
+
+                # Update the main content in the Media table
+                cursor.execute("UPDATE Media SET content = ? WHERE id = ?", (content_input, media_id))
+
+                # Check if a row already exists in MediaModifications for this media_id
+                cursor.execute("SELECT COUNT(*) FROM MediaModifications WHERE media_id = ?", (media_id,))
+                exists = cursor.fetchone()[0] > 0
+
+                if exists:
+                    # Update existing row
+                    cursor.execute("""
+                        UPDATE MediaModifications
+                        SET prompt = ?, summary = ?, modification_date = CURRENT_TIMESTAMP
+                        WHERE media_id = ?
+                    """, (prompt_input, summary_input, media_id))
+                else:
+                    # Insert new row
+                    cursor.execute("""
+                        INSERT INTO MediaModifications (media_id, prompt, summary, modification_date)
+                        VALUES (?, ?, ?, CURRENT_TIMESTAMP)
+                    """, (media_id, prompt_input, summary_input))
+
+                conn.commit()
+
+            return f"Content updated successfully for media ID: {media_id}"
+        else:
+            return "No item selected or invalid selection"
+    except Exception as e:
+        logging.error(f"Error updating media content: {e}")
+        return f"Error updating content: {str(e)}"
+
+def search_media_database(query: str) -> List[Tuple[int, str, str]]:
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("SELECT id, title, url FROM Media WHERE title LIKE ?", (f'%{query}%',))
+            results = cursor.fetchall()
+        return results
+    except sqlite3.Error as e:
+        raise Exception(f"Error searching media database: {e}")
+
+def load_media_content(media_id: int) -> dict:
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("SELECT content, prompt, summary FROM Media WHERE id = ?", (media_id,))
+            result = cursor.fetchone()
+            if result:
+                return {
+                    "content": result[0],
+                    "prompt": result[1],
+                    "summary": result[2]
+                }
+            return {"content": "", "prompt": "", "summary": ""}
+    except sqlite3.Error as e:
+        raise Exception(f"Error loading media content: {e}")
+
+def insert_prompt_to_db(title, description, system_prompt, user_prompt):
+    try:
+        conn = sqlite3.connect('prompts.db')
+        cursor = conn.cursor()
+        cursor.execute(
+            "INSERT INTO Prompts (name, details, system, user) VALUES (?, ?, ?, ?)",
+            (title, description, system_prompt, user_prompt)
+        )
+        conn.commit()
+        conn.close()
+        return "Prompt added successfully!"
+    except sqlite3.Error as e:
+        return f"Error adding prompt: {e}"
+
+
+def fetch_items_by_title_or_url(search_query: str, search_type: str):
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            if search_type == 'Title':
+                cursor.execute("SELECT id, title, url FROM Media WHERE title LIKE ?", (f'%{search_query}%',))
+            elif search_type == 'URL':
+                cursor.execute("SELECT id, title, url FROM Media WHERE url LIKE ?", (f'%{search_query}%',))
+            results = cursor.fetchall()
+            return results
+    except sqlite3.Error as e:
+        raise DatabaseError(f"Error fetching items by {search_type}: {e}")
+
+
+def fetch_items_by_keyword(search_query: str):
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT m.id, m.title, m.url
+                FROM Media m
+                JOIN MediaKeywords mk ON m.id = mk.media_id
+                JOIN Keywords k ON mk.keyword_id = k.id
+                WHERE k.keyword LIKE ?
+            """, (f'%{search_query}%',))
+            results = cursor.fetchall()
+            return results
+    except sqlite3.Error as e:
+        raise DatabaseError(f"Error fetching items by keyword: {e}")
+
+
+def fetch_items_by_content(search_query: str):
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("SELECT id, title, url FROM Media WHERE content LIKE ?", (f'%{search_query}%',))
+            results = cursor.fetchall()
+            return results
+    except sqlite3.Error as e:
+        raise DatabaseError(f"Error fetching items by content: {e}")
+
+
+def fetch_item_details_single(media_id: int):
+    try:
+        with db.get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT prompt, summary 
+                FROM MediaModifications 
+                WHERE media_id = ? 
+                ORDER BY modification_date DESC 
+                LIMIT 1
+            """, (media_id,))
+            prompt_summary_result = cursor.fetchone()
+            cursor.execute("SELECT content FROM Media WHERE id = ?", (media_id,))
+            content_result = cursor.fetchone()
+
+            prompt = prompt_summary_result[0] if prompt_summary_result else ""
+            summary = prompt_summary_result[1] if prompt_summary_result else ""
+            content = content_result[0] if content_result else ""
+
+            return prompt, summary, content
+    except sqlite3.Error as e:
+        raise Exception(f"Error fetching item details: {e}")
+
+
+
+def convert_to_markdown(item):
+    markdown = f"# {item['title']}\n\n"
+    markdown += f"**URL:** {item['url']}\n\n"
+    markdown += f"**Author:** {item['author']}\n\n"
+    markdown += f"**Ingestion Date:** {item['ingestion_date']}\n\n"
+    markdown += f"**Type:** {item['type']}\n\n"
+    markdown += f"**Keywords:** {', '.join(item['keywords'])}\n\n"
+    markdown += "## Summary\n\n"
+    markdown += f"{item['summary']}\n\n"
+    markdown += "## Content\n\n"
+    markdown += f"{item['content']}\n\n"
+    return markdown

App_Function_Libraries/Summarization_General_Lib.py

@@ -0,0 +1,1388 @@
+# Summarization_General_Lib.py
+#########################################
+# General Summarization Library
+# This library is used to perform summarization.
+#
+####
+import configparser
+####################
+# Function List
+#
+# 1. extract_text_from_segments(segments: List[Dict]) -> str
+# 2. summarize_with_openai(api_key, file_path, custom_prompt_arg)
+# 3. summarize_with_anthropic(api_key, file_path, model, custom_prompt_arg, max_retries=3, retry_delay=5)
+# 4. summarize_with_cohere(api_key, file_path, model, custom_prompt_arg)
+# 5. summarize_with_groq(api_key, file_path, model, custom_prompt_arg)
+#
+#
+####################
+# Import necessary libraries
+import os
+import logging
+import time
+import requests
+import json
+from requests import RequestException
+
+from App_Function_Libraries.Audio_Transcription_Lib import convert_to_wav, speech_to_text
+from App_Function_Libraries.Chunk_Lib import semantic_chunking, rolling_summarize, recursive_summarize_chunks, \
+    improved_chunking_process
+from App_Function_Libraries.Diarization_Lib import combine_transcription_and_diarization
+from App_Function_Libraries.Local_Summarization_Lib import summarize_with_llama, summarize_with_kobold, \
+    summarize_with_oobabooga, summarize_with_tabbyapi, summarize_with_vllm, summarize_with_local_llm
+from App_Function_Libraries.SQLite_DB import is_valid_url, add_media_to_database
+# Import Local
+from App_Function_Libraries.Utils import load_and_log_configs, load_comprehensive_config, sanitize_filename, \
+    clean_youtube_url, extract_video_info, create_download_directory
+from App_Function_Libraries.Video_DL_Ingestion_Lib import download_video
+
+#
+#######################################################################################################################
+# Function Definitions
+#
+config = load_comprehensive_config()
+openai_api_key = config.get('API', 'openai_api_key', fallback=None)
+
+def extract_text_from_segments(segments):
+    logging.debug(f"Segments received: {segments}")
+    logging.debug(f"Type of segments: {type(segments)}")
+
+    text = ""
+
+    if isinstance(segments, list):
+        for segment in segments:
+            logging.debug(f"Current segment: {segment}")
+            logging.debug(f"Type of segment: {type(segment)}")
+            if 'Text' in segment:
+                text += segment['Text'] + " "
+            else:
+                logging.warning(f"Skipping segment due to missing 'Text' key: {segment}")
+    else:
+        logging.warning(f"Unexpected type of 'segments': {type(segments)}")
+
+    return text.strip()
+
+
+def summarize_with_openai(api_key, input_data, custom_prompt_arg):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("OpenAI: API key not provided as parameter")
+            logging.info("OpenAI: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['openai']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("OpenAI: API key not found or is empty")
+            return "OpenAI: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"OpenAI: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        # Input data handling
+        logging.debug(f"OpenAI: Raw input data type: {type(input_data)}")
+        logging.debug(f"OpenAI: Raw input data (first 500 chars): {str(input_data)[:500]}...")
+
+        if isinstance(input_data, str):
+            if input_data.strip().startswith('{'):
+                # It's likely a JSON string
+                logging.debug("OpenAI: Parsing provided JSON string data for summarization")
+                try:
+                    data = json.loads(input_data)
+                except json.JSONDecodeError as e:
+                    logging.error(f"OpenAI: Error parsing JSON string: {str(e)}")
+                    return f"OpenAI: Error parsing JSON input: {str(e)}"
+            elif os.path.isfile(input_data):
+                logging.debug("OpenAI: Loading JSON data from file for summarization")
+                with open(input_data, 'r') as file:
+                    data = json.load(file)
+            else:
+                logging.debug("OpenAI: Using provided string data for summarization")
+                data = input_data
+        else:
+            data = input_data
+
+        logging.debug(f"OpenAI: Processed data type: {type(data)}")
+        logging.debug(f"OpenAI: Processed data (first 500 chars): {str(data)[:500]}...")
+
+        # Text extraction
+        if isinstance(data, dict):
+            if 'summary' in data:
+                logging.debug("OpenAI: Summary already exists in the loaded data")
+                return data['summary']
+            elif 'segments' in data:
+                text = extract_text_from_segments(data['segments'])
+            else:
+                text = json.dumps(data)  # Convert dict to string if no specific format
+        elif isinstance(data, list):
+            text = extract_text_from_segments(data)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError(f"OpenAI: Invalid input data format: {type(data)}")
+
+        openai_model = loaded_config_data['models']['openai'] or "gpt-4o"
+        logging.debug(f"OpenAI: Extracted text (first 500 chars): {text[:500]}...")
+        logging.debug(f"OpenAI: Custom prompt: {custom_prompt_arg}")
+
+        openai_model = loaded_config_data['models']['openai'] or "gpt-4o"
+        logging.debug(f"OpenAI: Using model: {openai_model}")
+
+        headers = {
+            'Authorization': f'Bearer {openai_api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug(
+            f"OpenAI API Key: {openai_api_key[:5]}...{openai_api_key[-5:] if openai_api_key else None}")
+        logging.debug("openai: Preparing data + prompt for submittal")
+        openai_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        data = {
+            "model": openai_model,
+            "messages": [
+                {"role": "system", "content": "You are a professional summarizer."},
+                {"role": "user", "content": openai_prompt}
+            ],
+            "max_tokens": 4096,
+            "temperature": 0.1
+        }
+
+        logging.debug("OpenAI: Posting request")
+        response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("OpenAI: Summarization successful")
+                logging.debug(f"OpenAI: Summary (first 500 chars): {summary[:500]}...")
+                return summary
+            else:
+                logging.warning("OpenAI: Summary not found in the response data")
+                return "OpenAI: Summary not available"
+        else:
+            logging.error(f"OpenAI: Summarization failed with status code {response.status_code}")
+            logging.error(f"OpenAI: Error response: {response.text}")
+            return f"OpenAI: Failed to process summary. Status code: {response.status_code}"
+    except json.JSONDecodeError as e:
+        logging.error(f"OpenAI: Error decoding JSON: {str(e)}", exc_info=True)
+        return f"OpenAI: Error decoding JSON input: {str(e)}"
+    except requests.RequestException as e:
+        logging.error(f"OpenAI: Error making API request: {str(e)}", exc_info=True)
+        return f"OpenAI: Error making API request: {str(e)}"
+    except Exception as e:
+        logging.error(f"OpenAI: Unexpected error: {str(e)}", exc_info=True)
+        return f"OpenAI: Unexpected error occurred: {str(e)}"
+
+
+def summarize_with_anthropic(api_key, input_data, custom_prompt_arg, max_retries=3, retry_delay=5):
+    try:
+        loaded_config_data = load_and_log_configs()
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("Anthropic: API key not provided as parameter")
+            logging.info("Anthropic: Attempting to use API key from config file")
+            anthropic_api_key = loaded_config_data['api_keys']['anthropic']
+
+        # Sanity check to ensure API key is not empty in the config file
+        if api_key is None or api_key.strip() == "":
+            logging.error("Anthropic: API key not found or is empty")
+            return "Anthropic: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"Anthropic: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("AnthropicAI: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("AnthropicAI: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"AnthropicAI: Loaded data: {data}")
+        logging.debug(f"AnthropicAI: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Anthropic: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Anthropic: Invalid input data format")
+
+        anthropic_model = loaded_config_data['models']['anthropic']
+
+        headers = {
+            'x-api-key': anthropic_api_key,
+            'anthropic-version': '2023-06-01',
+            'Content-Type': 'application/json'
+        }
+
+        anthropic_prompt = custom_prompt_arg
+        logging.debug(f"Anthropic: Prompt is {anthropic_prompt}")
+        user_message = {
+            "role": "user",
+            "content": f"{text} \n\n\n\n{anthropic_prompt}"
+        }
+
+        model = loaded_config_data['models']['anthropic']
+
+        data = {
+            "model": model,
+            "max_tokens": 4096,  # max _possible_ tokens to return
+            "messages": [user_message],
+            "stop_sequences": ["\n\nHuman:"],
+            "temperature": 0.1,
+            "top_k": 0,
+            "top_p": 1.0,
+            "metadata": {
+                "user_id": "example_user_id",
+            },
+            "stream": False,
+            "system": "You are a professional summarizer."
+        }
+
+        for attempt in range(max_retries):
+            try:
+                logging.debug("anthropic: Posting request to API")
+                response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data)
+
+                # Check if the status code indicates success
+                if response.status_code == 200:
+                    logging.debug("anthropic: Post submittal successful")
+                    response_data = response.json()
+                    try:
+                        summary = response_data['content'][0]['text'].strip()
+                        logging.debug("anthropic: Summarization successful")
+                        print("Summary processed successfully.")
+                        return summary
+                    except (IndexError, KeyError) as e:
+                        logging.debug("anthropic: Unexpected data in response")
+                        print("Unexpected response format from Anthropic API:", response.text)
+                        return None
+                elif response.status_code == 500:  # Handle internal server error specifically
+                    logging.debug("anthropic: Internal server error")
+                    print("Internal server error from API. Retrying may be necessary.")
+                    time.sleep(retry_delay)
+                else:
+                    logging.debug(
+                        f"anthropic: Failed to summarize, status code {response.status_code}: {response.text}")
+                    print(f"Failed to process summary, status code {response.status_code}: {response.text}")
+                    return None
+
+            except RequestException as e:
+                logging.error(f"anthropic: Network error during attempt {attempt + 1}/{max_retries}: {str(e)}")
+                if attempt < max_retries - 1:
+                    time.sleep(retry_delay)
+                else:
+                    return f"anthropic: Network error: {str(e)}"
+    except FileNotFoundError as e:
+        logging.error(f"anthropic: File not found: {input_data}")
+        return f"anthropic: File not found: {input_data}"
+    except json.JSONDecodeError as e:
+        logging.error(f"anthropic: Invalid JSON format in file: {input_data}")
+        return f"anthropic: Invalid JSON format in file: {input_data}"
+    except Exception as e:
+        logging.error(f"anthropic: Error in processing: {str(e)}")
+        return f"anthropic: Error occurred while processing summary with Anthropic: {str(e)}"
+
+
+# Summarize with Cohere
+def summarize_with_cohere(api_key, input_data, custom_prompt_arg):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("Cohere: API key not provided as parameter")
+            logging.info("Cohere: Attempting to use API key from config file")
+            cohere_api_key = loaded_config_data['api_keys']['cohere']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("Cohere: API key not found or is empty")
+            return "Cohere: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"Cohere: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("Cohere: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("Cohere: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"Cohere: Loaded data: {data}")
+        logging.debug(f"Cohere: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Cohere: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Invalid input data format")
+
+        cohere_model = loaded_config_data['models']['cohere']
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+            'Authorization': f'Bearer {cohere_api_key}'
+        }
+
+        cohere_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        logging.debug("cohere: Prompt being sent is {cohere_prompt}")
+
+        model = loaded_config_data['models']['anthropic']
+
+        data = {
+            "chat_history": [
+                {"role": "USER", "message": cohere_prompt}
+            ],
+            "message": "Please provide a summary.",
+            "model": model,
+            "connectors": [{"id": "web-search"}]
+        }
+
+        logging.debug("cohere: Submitting request to API endpoint")
+        print("cohere: Submitting request to API endpoint")
+        response = requests.post('https://api.cohere.ai/v1/chat', headers=headers, json=data)
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'text' in response_data:
+                summary = response_data['text'].strip()
+                logging.debug("cohere: Summarization successful")
+                print("Summary processed successfully.")
+                return summary
+            else:
+                logging.error("Expected data not found in API response.")
+                return "Expected data not found in API response."
+        else:
+            logging.error(f"cohere: API request failed with status code {response.status_code}: {response.text}")
+            print(f"Failed to process summary, status code {response.status_code}: {response.text}")
+            return f"cohere: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("cohere: Error in processing: %s", str(e))
+        return f"cohere: Error occurred while processing summary with Cohere: {str(e)}"
+
+
+# https://console.groq.com/docs/quickstart
+def summarize_with_groq(api_key, input_data, custom_prompt_arg):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("Groq: API key not provided as parameter")
+            logging.info("Groq: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['groq']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("Groq: API key not found or is empty")
+            return "Groq: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"Groq: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        # Transcript data handling & Validation
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("Groq: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("Groq: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"Groq: Loaded data: {data}")
+        logging.debug(f"Groq: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Groq: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Groq: Invalid input data format")
+
+        # Set the model to be used
+        groq_model = loaded_config_data['models']['groq']
+
+        headers = {
+            'Authorization': f'Bearer {api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        groq_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        logging.debug("groq: Prompt being sent is {groq_prompt}")
+
+        data = {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": groq_prompt
+                }
+            ],
+            "model": groq_model
+        }
+
+        logging.debug("groq: Submitting request to API endpoint")
+        print("groq: Submitting request to API endpoint")
+        response = requests.post('https://api.groq.com/openai/v1/chat/completions', headers=headers, json=data)
+
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("groq: Summarization successful")
+                print("Summarization successful.")
+                return summary
+            else:
+                logging.error("Expected data not found in API response.")
+                return "Expected data not found in API response."
+        else:
+            logging.error(f"groq: API request failed with status code {response.status_code}: {response.text}")
+            return f"groq: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("groq: Error in processing: %s", str(e))
+        return f"groq: Error occurred while processing summary with groq: {str(e)}"
+
+
+def summarize_with_openrouter(api_key, input_data, custom_prompt_arg):
+    loaded_config_data = load_and_log_configs()
+    import requests
+    import json
+    global openrouter_model, openrouter_api_key
+    # API key validation
+    if api_key is None or api_key.strip() == "":
+        logging.info("OpenRouter: API key not provided as parameter")
+        logging.info("OpenRouter: Attempting to use API key from config file")
+        openrouter_api_key = loaded_config_data['api_keys']['openrouter']
+
+    if api_key is None or api_key.strip() == "":
+        logging.error("OpenRouter: API key not found or is empty")
+        return "OpenRouter: API Key Not Provided/Found in Config file or is empty"
+
+    # Model Selection validation
+    if openrouter_model is None or openrouter_model.strip() == "":
+        logging.info("OpenRouter: model not provided as parameter")
+        logging.info("OpenRouter: Attempting to use model from config file")
+        openrouter_model = loaded_config_data['api_keys']['openrouter_model']
+
+    if api_key is None or api_key.strip() == "":
+        logging.error("OpenAI: API key not found or is empty")
+        return "OpenAI: API Key Not Provided/Found in Config file or is empty"
+
+    logging.debug(f"OpenAI: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+    logging.debug(f"openai: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+    if isinstance(input_data, str) and os.path.isfile(input_data):
+        logging.debug("openrouter: Loading json data for summarization")
+        with open(input_data, 'r') as file:
+            data = json.load(file)
+    else:
+        logging.debug("openrouter: Using provided string data for summarization")
+        data = input_data
+
+    logging.debug(f"openrouter: Loaded data: {data}")
+    logging.debug(f"openrouter: Type of data: {type(data)}")
+
+    if isinstance(data, dict) and 'summary' in data:
+        # If the loaded data is a dictionary and already contains a summary, return it
+        logging.debug("openrouter: Summary already exists in the loaded data")
+        return data['summary']
+
+    # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+    if isinstance(data, list):
+        segments = data
+        text = extract_text_from_segments(segments)
+    elif isinstance(data, str):
+        text = data
+    else:
+        raise ValueError("Invalid input data format")
+
+    config = configparser.ConfigParser()
+    file_path = 'config.txt'
+
+    # Check if the file exists in the specified path
+    if os.path.exists(file_path):
+        config.read(file_path)
+    elif os.path.exists('config.txt'):  # Check in the current directory
+        config.read('../config.txt')
+    else:
+        print("config.txt not found in the specified path or current directory.")
+
+    openrouter_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
+
+    try:
+        logging.debug("openrouter: Submitting request to API endpoint")
+        print("openrouter: Submitting request to API endpoint")
+        response = requests.post(
+            url="https://openrouter.ai/api/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {openrouter_api_key}",
+            },
+            data=json.dumps({
+                "model": f"{openrouter_model}",
+                "messages": [
+                    {"role": "user", "content": openrouter_prompt}
+                ]
+            })
+        )
+
+        response_data = response.json()
+        logging.debug("API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("openrouter: Summarization successful")
+                print("openrouter: Summarization successful.")
+                return summary
+            else:
+                logging.error("openrouter: Expected data not found in API response.")
+                return "openrouter: Expected data not found in API response."
+        else:
+            logging.error(f"openrouter:  API request failed with status code {response.status_code}: {response.text}")
+            return f"openrouter: API request failed: {response.text}"
+    except Exception as e:
+        logging.error("openrouter: Error in processing: %s", str(e))
+        return f"openrouter: Error occurred while processing summary with openrouter: {str(e)}"
+
+def summarize_with_huggingface(api_key, input_data, custom_prompt_arg):
+    loaded_config_data = load_and_log_configs()
+    global huggingface_api_key
+    logging.debug(f"huggingface: Summarization process starting...")
+    try:
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("HuggingFace: API key not provided as parameter")
+            logging.info("HuggingFace: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['huggingface']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("HuggingFace: API key not found or is empty")
+            return "HuggingFace: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"HuggingFace: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("HuggingFace: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("HuggingFace: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"HuggingFace: Loaded data: {data}")
+        logging.debug(f"HuggingFace: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("HuggingFace: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("HuggingFace: Invalid input data format")
+
+        print(f"HuggingFace: lets make sure the HF api key exists...\n\t {api_key}")
+        headers = {
+            "Authorization": f"Bearer {api_key}"
+        }
+
+        huggingface_model = loaded_config_data['models']['huggingface']
+        API_URL = f"https://api-inference.huggingface.co/models/{huggingface_model}"
+
+        huggingface_prompt = f"{text}\n\n\n\n{custom_prompt_arg}"
+        logging.debug("huggingface: Prompt being sent is {huggingface_prompt}")
+        data = {
+            "inputs": text,
+            "parameters": {"max_length": 512, "min_length": 100}  # You can adjust max_length and min_length as needed
+        }
+
+        print(f"huggingface: lets make sure the HF api key is the same..\n\t {huggingface_api_key}")
+
+        logging.debug("huggingface: Submitting request...")
+
+        response = requests.post(API_URL, headers=headers, json=data)
+
+        if response.status_code == 200:
+            summary = response.json()[0]['summary_text']
+            logging.debug("huggingface: Summarization successful")
+            print("Summarization successful.")
+            return summary
+        else:
+            logging.error(f"huggingface: Summarization failed with status code {response.status_code}: {response.text}")
+            return f"Failed to process summary, status code {response.status_code}: {response.text}"
+    except Exception as e:
+        logging.error("huggingface: Error in processing: %s", str(e))
+        print(f"Error occurred while processing summary with huggingface: {str(e)}")
+        return None
+
+
+def summarize_with_deepseek(api_key, input_data, custom_prompt_arg):
+    loaded_config_data = load_and_log_configs()
+    try:
+        # API key validation
+        if api_key is None or api_key.strip() == "":
+            logging.info("DeepSeek: API key not provided as parameter")
+            logging.info("DeepSeek: Attempting to use API key from config file")
+            api_key = loaded_config_data['api_keys']['deepseek']
+
+        if api_key is None or api_key.strip() == "":
+            logging.error("DeepSeek: API key not found or is empty")
+            return "DeepSeek: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"DeepSeek: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        # Input data handling
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("DeepSeek: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("DeepSeek: Using provided string data for summarization")
+            data = input_data
+
+        logging.debug(f"DeepSeek: Loaded data: {data}")
+        logging.debug(f"DeepSeek: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("DeepSeek: Summary already exists in the loaded data")
+            return data['summary']
+
+        # Text extraction
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("DeepSeek: Invalid input data format")
+
+        deepseek_model = loaded_config_data['models']['deepseek'] or "deepseek-chat"
+
+        headers = {
+            'Authorization': f'Bearer {api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug(
+            f"Deepseek API Key: {api_key[:5]}...{api_key[-5:] if api_key else None}")
+        logging.debug("openai: Preparing data + prompt for submittal")
+        deepseek_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        data = {
+            "model": deepseek_model,
+            "messages": [
+                {"role": "system", "content": "You are a professional summarizer."},
+                {"role": "user", "content": deepseek_prompt}
+            ],
+            "stream": False,
+            "temperature": 0.8
+        }
+
+        logging.debug("DeepSeek: Posting request")
+        response = requests.post('https://api.deepseek.com/chat/completions', headers=headers, json=data)
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("DeepSeek: Summarization successful")
+                return summary
+            else:
+                logging.warning("DeepSeek: Summary not found in the response data")
+                return "DeepSeek: Summary not available"
+        else:
+            logging.error(f"DeepSeek: Summarization failed with status code {response.status_code}")
+            logging.error(f"DeepSeek: Error response: {response.text}")
+            return f"DeepSeek: Failed to process summary. Status code: {response.status_code}"
+    except Exception as e:
+        logging.error(f"DeepSeek: Error in processing: {str(e)}", exc_info=True)
+        return f"DeepSeek: Error occurred while processing summary: {str(e)}"
+
+
+#
+#
+#######################################################################################################################
+#
+#
+# Gradio File Processing
+
+
+# Handle multiple videos as input
+def process_video_urls(url_list, num_speakers, whisper_model, custom_prompt_input, offset, api_name, api_key, vad_filter,
+                       download_video_flag, download_audio, rolling_summarization, detail_level, question_box,
+                       keywords, chunk_text_by_words, max_words, chunk_text_by_sentences, max_sentences,
+                       chunk_text_by_paragraphs, max_paragraphs, chunk_text_by_tokens, max_tokens,  chunk_by_semantic,
+                       semantic_chunk_size, semantic_chunk_overlap, recursive_summarization):
+    global current_progress
+    progress = []  # This must always be a list
+    status = []  # This must always be a list
+
+    if custom_prompt_input is None:
+        custom_prompt_input = """
+            You are a bulleted notes specialist. ```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.
+    **Bulleted Note Creation Guidelines**
+
+    **Headings**:
+    - Based on referenced topics, not categories like quotes or terms
+    - Surrounded by **bold** formatting 
+    - Not listed as bullet points
+    - No space between headings and list items underneath
+
+    **Emphasis**:
+    - **Important terms** set in bold font
+    - **Text ending in a colon**: also bolded
+
+    **Review**:
+    - Ensure adherence to specified format
+    - Do not reference these instructions in your response.</s>[INST] {{ .Prompt }} [/INST]"""
+
+    def update_progress(index, url, message):
+        progress.append(f"Processing {index + 1}/{len(url_list)}: {url}")  # Append to list
+        status.append(message)  # Append to list
+        return "\n".join(progress), "\n".join(status)  # Return strings for display
+
+
+    for index, url in enumerate(url_list):
+        try:
+            transcription, summary, json_file_path, summary_file_path, _, _ = process_url(
+                url=url,
+                num_speakers=num_speakers,
+                whisper_model=whisper_model,
+                custom_prompt_input=custom_prompt_input,
+                offset=offset,
+                api_name=api_name,
+                api_key=api_key,
+                vad_filter=vad_filter,
+                download_video_flag=download_video_flag,
+                download_audio=download_audio,
+                rolling_summarization=rolling_summarization,
+                detail_level=detail_level,
+                question_box=question_box,
+                keywords=keywords,
+                chunk_text_by_words=chunk_text_by_words,
+                max_words=max_words,
+                chunk_text_by_sentences=chunk_text_by_sentences,
+                max_sentences=max_sentences,
+                chunk_text_by_paragraphs=chunk_text_by_paragraphs,
+                max_paragraphs=max_paragraphs,
+                chunk_text_by_tokens=chunk_text_by_tokens,
+                max_tokens=max_tokens,
+                chunk_by_semantic=chunk_by_semantic,
+                semantic_chunk_size=semantic_chunk_size,
+                semantic_chunk_overlap=semantic_chunk_overlap,
+                recursive_summarization=recursive_summarization
+            )
+            # Update progress and transcription properly
+            current_progress, current_status = update_progress(index, url, "Video processed and ingested into the database.")
+        except Exception as e:
+            current_progress, current_status = update_progress(index, url, f"Error: {str(e)}")
+
+    success_message = "All videos have been transcribed, summarized, and ingested into the database successfully."
+    return current_progress, success_message, None, None, None, None
+
+
+# stuff
+def perform_transcription(video_path, offset, whisper_model, vad_filter, diarize=False):
+    global segments_json_path
+    audio_file_path = convert_to_wav(video_path, offset)
+    segments_json_path = audio_file_path.replace('.wav', '.segments.json')
+
+    if diarize:
+        diarized_json_path = audio_file_path.replace('.wav', '.diarized.json')
+
+        # Check if diarized JSON already exists
+        if os.path.exists(diarized_json_path):
+            logging.info(f"Diarized file already exists: {diarized_json_path}")
+            try:
+                with open(diarized_json_path, 'r') as file:
+                    diarized_segments = json.load(file)
+                if not diarized_segments:
+                    logging.warning(f"Diarized JSON file is empty, re-generating: {diarized_json_path}")
+                    raise ValueError("Empty diarized JSON file")
+                logging.debug(f"Loaded diarized segments from {diarized_json_path}")
+                return audio_file_path, diarized_segments
+            except (json.JSONDecodeError, ValueError) as e:
+                logging.error(f"Failed to read or parse the diarized JSON file: {e}")
+                os.remove(diarized_json_path)
+
+        # If diarized file doesn't exist or was corrupted, generate new diarized transcription
+        logging.info(f"Generating diarized transcription for {audio_file_path}")
+        diarized_segments = combine_transcription_and_diarization(audio_file_path)
+
+        # Save diarized segments
+        with open(diarized_json_path, 'w') as file:
+            json.dump(diarized_segments, file, indent=2)
+
+        return audio_file_path, diarized_segments
+
+    # Non-diarized transcription (existing functionality)
+    if os.path.exists(segments_json_path):
+        logging.info(f"Segments file already exists: {segments_json_path}")
+        try:
+            with open(segments_json_path, 'r') as file:
+                segments = json.load(file)
+            if not segments:
+                logging.warning(f"Segments JSON file is empty, re-generating: {segments_json_path}")
+                raise ValueError("Empty segments JSON file")
+            logging.debug(f"Loaded segments from {segments_json_path}")
+        except (json.JSONDecodeError, ValueError) as e:
+            logging.error(f"Failed to read or parse the segments JSON file: {e}")
+            os.remove(segments_json_path)
+            logging.info(f"Re-generating transcription for {audio_file_path}")
+            audio_file, segments = re_generate_transcription(audio_file_path, whisper_model, vad_filter)
+            if segments is None:
+                return None, None
+    else:
+        audio_file, segments = re_generate_transcription(audio_file_path, whisper_model, vad_filter)
+
+    return audio_file_path, segments
+
+
+def re_generate_transcription(audio_file_path, whisper_model, vad_filter):
+    try:
+        segments = speech_to_text(audio_file_path, whisper_model=whisper_model, vad_filter=vad_filter)
+        # Save segments to JSON
+        with open(segments_json_path, 'w') as file:
+            json.dump(segments, file, indent=2)
+        logging.debug(f"Transcription segments saved to {segments_json_path}")
+        return audio_file_path, segments
+    except Exception as e:
+        logging.error(f"Error in re-generating transcription: {str(e)}")
+        return None, None
+
+
+def save_transcription_and_summary(transcription_text, summary_text, download_path, info_dict):
+    try:
+        video_title = sanitize_filename(info_dict.get('title', 'Untitled'))
+
+        # Save transcription
+        transcription_file_path = os.path.join(download_path, f"{video_title}_transcription.txt")
+        with open(transcription_file_path, 'w', encoding='utf-8') as f:
+            f.write(transcription_text)
+
+        # Save summary if available
+        summary_file_path = None
+        if summary_text:
+            summary_file_path = os.path.join(download_path, f"{video_title}_summary.txt")
+            with open(summary_file_path, 'w', encoding='utf-8') as f:
+                f.write(summary_text)
+
+        return transcription_file_path, summary_file_path
+    except Exception as e:
+        logging.error(f"Error in save_transcription_and_summary: {str(e)}", exc_info=True)
+        return None, None
+
+
+def summarize_chunk(api_name, text, custom_prompt_input, api_key):
+    try:
+        if api_name.lower() == 'openai':
+            return summarize_with_openai(api_key, text, custom_prompt_input)
+        elif api_name.lower() == "anthropic":
+            return summarize_with_anthropic(api_key, text, custom_prompt_input)
+        elif api_name.lower() == "cohere":
+            return summarize_with_cohere(api_key, text, custom_prompt_input)
+        elif api_name.lower() == "groq":
+            return summarize_with_groq(api_key, text, custom_prompt_input)
+        elif api_name.lower() == "openrouter":
+            return summarize_with_openrouter(api_key, text, custom_prompt_input)
+        elif api_name.lower() == "deepseek":
+            return summarize_with_deepseek(api_key, text, custom_prompt_input)
+        elif api_name.lower() == "llama.cpp":
+            return summarize_with_llama(text, custom_prompt_input)
+        elif api_name.lower() == "kobold":
+            return summarize_with_kobold(text, api_key, custom_prompt_input)
+        elif api_name.lower() == "ooba":
+            return summarize_with_oobabooga(text, api_key, custom_prompt_input)
+        elif api_name.lower() == "tabbyapi":
+            return summarize_with_tabbyapi(text, custom_prompt_input)
+        elif api_name.lower() == "vllm":
+            return summarize_with_vllm(text, custom_prompt_input)
+        elif api_name.lower() == "local-llm":
+            return summarize_with_local_llm(text, custom_prompt_input)
+        elif api_name.lower() == "huggingface":
+            return summarize_with_huggingface(api_key, text, custom_prompt_input)
+        else:
+            logging.warning(f"Unsupported API: {api_name}")
+            return None
+    except Exception as e:
+        logging.error(f"Error in summarize_chunk with {api_name}: {str(e)}")
+        return None
+
+
+def extract_metadata_and_content(input_data):
+    metadata = {}
+    content = ""
+
+    if isinstance(input_data, str):
+        if os.path.exists(input_data):
+            with open(input_data, 'r', encoding='utf-8') as file:
+                data = json.load(file)
+        else:
+            try:
+                data = json.loads(input_data)
+            except json.JSONDecodeError:
+                return {}, input_data
+    elif isinstance(input_data, dict):
+        data = input_data
+    else:
+        return {}, str(input_data)
+
+    # Extract metadata
+    metadata['title'] = data.get('title', 'No title available')
+    metadata['author'] = data.get('author', 'Unknown author')
+
+    # Extract content
+    if 'transcription' in data:
+        content = extract_text_from_segments(data['transcription'])
+    elif 'segments' in data:
+        content = extract_text_from_segments(data['segments'])
+    elif 'content' in data:
+        content = data['content']
+    else:
+        content = json.dumps(data)
+
+    return metadata, content
+
+def extract_text_from_segments(segments):
+    if isinstance(segments, list):
+        return ' '.join([seg.get('Text', '') for seg in segments if 'Text' in seg])
+    return str(segments)
+
+def format_input_with_metadata(metadata, content):
+    formatted_input = f"Title: {metadata.get('title', 'No title available')}\n"
+    formatted_input += f"Author: {metadata.get('author', 'Unknown author')}\n\n"
+    formatted_input += content
+    return formatted_input
+
+def perform_summarization(api_name, input_data, custom_prompt_input, api_key, recursive_summarization=False):
+    loaded_config_data = load_and_log_configs()
+
+    if custom_prompt_input is None:
+        custom_prompt_input = """
+        You are a bulleted notes specialist. ```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.
+**Bulleted Note Creation Guidelines**
+
+**Headings**:
+- Based on referenced topics, not categories like quotes or terms
+- Surrounded by **bold** formatting 
+- Not listed as bullet points
+- No space between headings and list items underneath
+
+**Emphasis**:
+- **Important terms** set in bold font
+- **Text ending in a colon**: also bolded
+
+**Review**:
+- Ensure adherence to specified format
+- Do not reference these instructions in your response.</s>[INST] {{ .Prompt }} [/INST]"""
+
+    try:
+        logging.debug(f"Input data type: {type(input_data)}")
+        logging.debug(f"Input data (first 500 chars): {str(input_data)[:500]}...")
+
+        # Extract metadata and content
+        metadata, content = extract_metadata_and_content(input_data)
+
+        logging.debug(f"Extracted metadata: {metadata}")
+        logging.debug(f"Extracted content (first 500 chars): {content[:500]}...")
+
+        # Prepare a structured input for summarization
+        structured_input = format_input_with_metadata(metadata, content)
+
+        # Perform summarization on the structured input
+        if recursive_summarization:
+            chunk_options = {
+                'method': 'words',  # or 'sentences', 'paragraphs', 'tokens' based on your preference
+                'max_size': 1000,  # adjust as needed
+                'overlap': 100,  # adjust as needed
+                'adaptive': False,
+                'multi_level': False,
+                'language': 'english'
+            }
+            chunks = improved_chunking_process(structured_input, chunk_options)
+            summary = recursive_summarize_chunks([chunk['text'] for chunk in chunks],
+                                                 lambda x: summarize_chunk(api_name, x, custom_prompt_input, api_key),
+                                                 custom_prompt_input)
+        else:
+            summary = summarize_chunk(api_name, structured_input, custom_prompt_input, api_key)
+
+        if summary:
+            logging.info(f"Summary generated using {api_name} API")
+            if isinstance(input_data, str) and os.path.exists(input_data):
+                summary_file_path = input_data.replace('.json', '_summary.txt')
+                with open(summary_file_path, 'w', encoding='utf-8') as file:
+                    file.write(summary)
+        else:
+            logging.warning(f"Failed to generate summary using {api_name} API")
+
+        return summary
+
+    except requests.exceptions.ConnectionError:
+        logging.error("Connection error while summarizing")
+    except Exception as e:
+        logging.error(f"Error summarizing with {api_name}: {str(e)}", exc_info=True)
+        return f"An error occurred during summarization: {str(e)}"
+    return None
+
+def extract_text_from_input(input_data):
+    if isinstance(input_data, str):
+        try:
+            # Try to parse as JSON
+            data = json.loads(input_data)
+        except json.JSONDecodeError:
+            # If not valid JSON, treat as plain text
+            return input_data
+    elif isinstance(input_data, dict):
+        data = input_data
+    else:
+        return str(input_data)
+
+    # Extract relevant fields from the JSON object
+    text_parts = []
+    if 'title' in data:
+        text_parts.append(f"Title: {data['title']}")
+    if 'description' in data:
+        text_parts.append(f"Description: {data['description']}")
+    if 'transcription' in data:
+        if isinstance(data['transcription'], list):
+            transcription_text = ' '.join([segment.get('Text', '') for segment in data['transcription']])
+        elif isinstance(data['transcription'], str):
+            transcription_text = data['transcription']
+        else:
+            transcription_text = str(data['transcription'])
+        text_parts.append(f"Transcription: {transcription_text}")
+    elif 'segments' in data:
+        segments_text = extract_text_from_segments(data['segments'])
+        text_parts.append(f"Segments: {segments_text}")
+
+    return '\n\n'.join(text_parts)
+
+
+
+def process_url(
+        url,
+        num_speakers,
+        whisper_model,
+        custom_prompt_input,
+        offset,
+        api_name,
+        api_key,
+        vad_filter,
+        download_video_flag,
+        download_audio,
+        rolling_summarization,
+        detail_level,
+        # It's for the asking a question about a returned prompt - needs to be removed #FIXME
+        question_box,
+        keywords,
+        chunk_text_by_words,
+        max_words,
+        chunk_text_by_sentences,
+        max_sentences,
+        chunk_text_by_paragraphs,
+        max_paragraphs,
+        chunk_text_by_tokens,
+        max_tokens,
+        chunk_by_semantic,
+        semantic_chunk_size,
+        semantic_chunk_overlap,
+        local_file_path=None,
+        diarize=False,
+        recursive_summarization=False
+):
+    # Handle the chunk summarization options
+    set_chunk_txt_by_words = chunk_text_by_words
+    set_max_txt_chunk_words = max_words
+    set_chunk_txt_by_sentences = chunk_text_by_sentences
+    set_max_txt_chunk_sentences = max_sentences
+    set_chunk_txt_by_paragraphs = chunk_text_by_paragraphs
+    set_max_txt_chunk_paragraphs = max_paragraphs
+    set_chunk_txt_by_tokens = chunk_text_by_tokens
+    set_max_txt_chunk_tokens = max_tokens
+    set_chunk_txt_by_semantic = chunk_by_semantic
+    set_semantic_chunk_size = semantic_chunk_size
+    set_semantic_chunk_overlap = semantic_chunk_overlap
+
+    progress = []
+    success_message = "All videos processed successfully. Transcriptions and summaries have been ingested into the database."
+
+    if custom_prompt_input is None:
+        custom_prompt_input = """
+            You are a bulleted notes specialist. ```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.
+    **Bulleted Note Creation Guidelines**
+
+    **Headings**:
+    - Based on referenced topics, not categories like quotes or terms
+    - Surrounded by **bold** formatting 
+    - Not listed as bullet points
+    - No space between headings and list items underneath
+
+    **Emphasis**:
+    - **Important terms** set in bold font
+    - **Text ending in a colon**: also bolded
+
+    **Review**:
+    - Ensure adherence to specified format
+    - Do not reference these instructions in your response.</s>[INST] {{ .Prompt }} [/INST]"""
+
+    # Validate input
+    if not url and not local_file_path:
+        return "Process_URL: No URL provided.", "No URL provided.", None, None, None, None, None, None
+
+    # FIXME - Chatgpt again?
+    if isinstance(url, str):
+        urls = url.strip().split('\n')
+        if len(urls) > 1:
+            return process_video_urls(urls, num_speakers, whisper_model, custom_prompt_input, offset, api_name, api_key, vad_filter,
+                                      download_video_flag, download_audio, rolling_summarization, detail_level, question_box,
+                                      keywords, chunk_text_by_words, max_words, chunk_text_by_sentences, max_sentences,
+                                      chunk_text_by_paragraphs, max_paragraphs, chunk_text_by_tokens, max_tokens, chunk_by_semantic, semantic_chunk_size, semantic_chunk_overlap)
+        else:
+            urls = [url]
+
+    if url and not is_valid_url(url):
+        return "Process_URL: Invalid URL format.", "Invalid URL format.", None, None, None, None, None, None
+
+    if url:
+        # Clean the URL to remove playlist parameters if any
+        url = clean_youtube_url(url)
+        logging.info(f"Process_URL: Processing URL: {url}")
+
+    if api_name:
+        print("Process_URL: API Name received:", api_name)  # Debugging line
+
+    video_file_path = None
+    global info_dict
+
+    # FIXME - need to handle local audio file processing
+    # If Local audio file is provided
+    if local_file_path:
+        try:
+            pass
+            # # insert code to process local audio file
+            # # Need to be able to add a title/author/etc for ingestion into the database
+            # # Also want to be able to optionally _just_ ingest it, and not ingest.
+            # # FIXME
+            # #download_path = create_download_directory(title)
+            # #audio_path = download_video(url, download_path, info_dict, download_video_flag)
+            #
+            # audio_file_path = local_file_path
+            # global segments
+            # audio_file_path, segments = perform_transcription(audio_file_path, offset, whisper_model, vad_filter)
+            #
+            # if audio_file_path is None or segments is None:
+            #     logging.error("Process_URL: Transcription failed or segments not available.")
+            #     return "Process_URL: Transcription failed.", "Transcription failed.", None, None, None, None
+            #
+            # logging.debug(f"Process_URL: Transcription audio_file: {audio_file_path}")
+            # logging.debug(f"Process_URL: Transcription segments: {segments}")
+            #
+            # transcription_text = {'audio_file': audio_file_path, 'transcription': segments}
+            # logging.debug(f"Process_URL: Transcription text: {transcription_text}")
+
+            # Rolling Summarization Processing
+            # if rolling_summarization:
+            #     text = extract_text_from_segments(segments)
+            #     summary_text = rolling_summarize_function(
+            #         transcription_text,
+            #         detail=detail_level,
+            #         api_name=api_name,
+            #         api_key=api_key,
+            #         custom_prompt=custom_prompt,
+            #         chunk_by_words=chunk_text_by_words,
+            #         max_words=max_words,
+            #         chunk_by_sentences=chunk_text_by_sentences,
+            #         max_sentences=max_sentences,
+            #         chunk_by_paragraphs=chunk_text_by_paragraphs,
+            #         max_paragraphs=max_paragraphs,
+            #         chunk_by_tokens=chunk_text_by_tokens,
+            #         max_tokens=max_tokens
+            #     )
+            # if api_name:
+            #     summary_text = perform_summarization(api_name, segments_json_path, custom_prompt, api_key, config)
+            #     if summary_text is None:
+            #         logging.error("Summary text is None. Check summarization function.")
+            #         summary_file_path = None  # Set summary_file_path to None if summary is not generated
+            # else:
+            #     summary_text = 'Summary not available'
+            #     summary_file_path = None  # Set summary_file_path to None if summary is not generated
+            #
+            # json_file_path, summary_file_path = save_transcription_and_summary(transcription_text, summary_text, download_path)
+            #
+            # add_media_to_database(url, info_dict, segments, summary_text, keywords, custom_prompt, whisper_model)
+            #
+            # return transcription_text, summary_text, json_file_path, summary_file_path, None, None
+
+        except Exception as e:
+            logging.error(f": {e}")
+            return str(e), 'process_url: Error processing the request.', None, None, None, None
+
+
+    # If URL/Local video file is provided
+    try:
+        info_dict, title = extract_video_info(url)
+        download_path = create_download_directory(title)
+        video_path = download_video(url, download_path, info_dict, download_video_flag)
+        global segments
+        audio_file_path, segments = perform_transcription(video_path, offset, whisper_model, vad_filter)
+
+        if diarize:
+            transcription_text = combine_transcription_and_diarization(audio_file_path)
+        else:
+            audio_file, segments = perform_transcription(video_path, offset, whisper_model, vad_filter)
+            transcription_text = {'audio_file': audio_file, 'transcription': segments}
+
+
+        if audio_file_path is None or segments is None:
+            logging.error("Process_URL: Transcription failed or segments not available.")
+            return "Process_URL: Transcription failed.", "Transcription failed.", None, None, None, None
+
+        logging.debug(f"Process_URL: Transcription audio_file: {audio_file_path}")
+        logging.debug(f"Process_URL: Transcription segments: {segments}")
+
+        logging.debug(f"Process_URL: Transcription text: {transcription_text}")
+
+        # FIXME - Implement chunking calls here
+        # Implement chunking calls here
+        chunked_transcriptions = []
+        if chunk_text_by_words:
+            chunked_transcriptions = chunk_text_by_words(transcription_text['transcription'], max_words)
+        elif chunk_text_by_sentences:
+            chunked_transcriptions = chunk_text_by_sentences(transcription_text['transcription'], max_sentences)
+        elif chunk_text_by_paragraphs:
+            chunked_transcriptions = chunk_text_by_paragraphs(transcription_text['transcription'], max_paragraphs)
+        elif chunk_text_by_tokens:
+            chunked_transcriptions = chunk_text_by_tokens(transcription_text['transcription'], max_tokens)
+        elif chunk_by_semantic:
+            chunked_transcriptions = semantic_chunking(transcription_text['transcription'], semantic_chunk_size, 'tokens')
+
+        # If we did chunking, we now have the chunked transcripts in 'chunked_transcriptions'
+        elif rolling_summarization:
+        # FIXME - rolling summarization
+        #     text = extract_text_from_segments(segments)
+        #     summary_text = rolling_summarize_function(
+        #         transcription_text,
+        #         detail=detail_level,
+        #         api_name=api_name,
+        #         api_key=api_key,
+        #         custom_prompt_input=custom_prompt_input,
+        #         chunk_by_words=chunk_text_by_words,
+        #         max_words=max_words,
+        #         chunk_by_sentences=chunk_text_by_sentences,
+        #         max_sentences=max_sentences,
+        #         chunk_by_paragraphs=chunk_text_by_paragraphs,
+        #         max_paragraphs=max_paragraphs,
+        #         chunk_by_tokens=chunk_text_by_tokens,
+        #         max_tokens=max_tokens
+        #     )
+            pass
+        else:
+            pass
+
+        summarized_chunk_transcriptions = []
+
+        if chunk_text_by_words or chunk_text_by_sentences or chunk_text_by_paragraphs or chunk_text_by_tokens or chunk_by_semantic and api_name:
+            # Perform summarization based on chunks
+            for chunk in chunked_transcriptions:
+                summarized_chunks = []
+                if api_name == "anthropic":
+                    summary = summarize_with_anthropic(api_key, chunk, custom_prompt_input)
+                elif api_name == "cohere":
+                    summary = summarize_with_cohere(api_key, chunk, custom_prompt_input)
+                elif api_name == "openai":
+                    summary = summarize_with_openai(api_key, chunk, custom_prompt_input)
+                elif api_name == "Groq":
+                    summary = summarize_with_groq(api_key, chunk, custom_prompt_input)
+                elif api_name == "DeepSeek":
+                    summary = summarize_with_deepseek(api_key, chunk, custom_prompt_input)
+                elif api_name == "OpenRouter":
+                    summary = summarize_with_openrouter(api_key, chunk, custom_prompt_input)
+                elif api_name == "Llama.cpp":
+                    summary = summarize_with_llama(chunk, custom_prompt_input)
+                elif api_name == "Kobold":
+                    summary = summarize_with_kobold(chunk, custom_prompt_input)
+                elif api_name == "Ooba":
+                    summary = summarize_with_oobabooga(chunk, custom_prompt_input)
+                elif api_name == "Tabbyapi":
+                    summary = summarize_with_tabbyapi(chunk, custom_prompt_input)
+                elif api_name == "VLLM":
+                    summary = summarize_with_vllm(chunk, custom_prompt_input)
+                summarized_chunk_transcriptions.append(summary)
+
+        # Combine chunked transcriptions into a single file
+        combined_transcription_text = '\n\n'.join(chunked_transcriptions)
+        combined_transcription_file_path = os.path.join(download_path, 'combined_transcription.txt')
+        with open(combined_transcription_file_path, 'w') as f:
+            f.write(combined_transcription_text)
+
+        # Combine summarized chunk transcriptions into a single file
+        combined_summary_text = '\n\n'.join(summarized_chunk_transcriptions)
+        combined_summary_file_path = os.path.join(download_path, 'combined_summary.txt')
+        with open(combined_summary_file_path, 'w') as f:
+            f.write(combined_summary_text)
+
+        # Handle rolling summarization
+        if rolling_summarization:
+            summary_text = rolling_summarize(
+                text=extract_text_from_segments(segments),
+                detail=detail_level,
+                model='gpt-4-turbo',
+                additional_instructions=custom_prompt_input,
+                summarize_recursively=recursive_summarization
+            )
+        elif api_name:
+            summary_text = perform_summarization(api_name, segments_json_path, custom_prompt_input, api_key,
+                                                 recursive_summarization)
+        else:
+            summary_text = 'Summary not available'
+
+        # Check to see if chunking was performed, and if so, return that instead
+        if chunk_text_by_words or chunk_text_by_sentences or chunk_text_by_paragraphs or chunk_text_by_tokens or chunk_by_semantic:
+            # Combine chunked transcriptions into a single file
+            # FIXME - validate this works....
+            json_file_path, summary_file_path = save_transcription_and_summary(combined_transcription_file_path, combined_summary_file_path, download_path)
+            add_media_to_database(url, info_dict, segments, summary_text, keywords, custom_prompt_input, whisper_model)
+            return transcription_text, summary_text, json_file_path, summary_file_path, None, None
+        else:
+            json_file_path, summary_file_path = save_transcription_and_summary(transcription_text, summary_text, download_path)
+            add_media_to_database(url, info_dict, segments, summary_text, keywords, custom_prompt_input, whisper_model)
+            return transcription_text, summary_text, json_file_path, summary_file_path, None, None
+
+    except Exception as e:
+        logging.error(f": {e}")
+        return str(e), 'process_url: Error processing the request.', None, None, None, None
+
+#
+#
+############################################################################################################################################

App_Function_Libraries/System_Checks_Lib.py

@@ -0,0 +1,184 @@
+# System_Checks_Lib.py
+#########################################
+# System Checks Library
+# This library is used to check the system for the necessary dependencies to run the script.
+# It checks for the OS, the availability of the GPU, and the availability of the ffmpeg executable.
+# If the GPU is available, it asks the user if they would like to use it for processing.
+# If ffmpeg is not found, it asks the user if they would like to download it.
+# The script will exit if the user chooses not to download ffmpeg.
+####
+
+####################
+# Function List
+#
+# 1. platform_check()
+# 2. cuda_check()
+# 3. decide_cpugpu()
+# 4. check_ffmpeg()
+# 5. download_ffmpeg()
+#
+####################
+
+
+
+
+# Import necessary libraries
+import logging
+import os
+import platform
+import requests
+import shutil
+import subprocess
+import zipfile
+# Import Local Libraries
+#from App_Function_Libraries import
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+def platform_check():
+    global userOS
+    if platform.system() == "Linux":
+        print("Linux OS detected \n Running Linux appropriate commands")
+        userOS = "Linux"
+    elif platform.system() == "Windows":
+        print("Windows OS detected \n Running Windows appropriate commands")
+        userOS = "Windows"
+    else:
+        print("Other OS detected \n Maybe try running things manually?")
+        exit()
+
+
+# Check for NVIDIA GPU and CUDA availability
+def cuda_check():
+    global processing_choice
+    try:
+        # Run nvidia-smi to capture its output
+        nvidia_smi_output = subprocess.check_output("nvidia-smi", shell=True).decode()
+
+        # Look for CUDA version in the output
+        if "CUDA Version" in nvidia_smi_output:
+            cuda_version = next(
+                (line.split(":")[-1].strip() for line in nvidia_smi_output.splitlines() if "CUDA Version" in line),
+                "Not found")
+            print(f"NVIDIA GPU with CUDA Version {cuda_version} is available.")
+            processing_choice = "cuda"
+        else:
+            print("CUDA is not installed or configured correctly.")
+            processing_choice = "cpu"
+
+    except subprocess.CalledProcessError as e:
+        print(f"Failed to run 'nvidia-smi': {str(e)}")
+        processing_choice = "cpu"
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+        processing_choice = "cpu"
+
+    # Optionally, check for the CUDA_VISIBLE_DEVICES env variable as an additional check
+    if "CUDA_VISIBLE_DEVICES" in os.environ:
+        print("CUDA_VISIBLE_DEVICES is set:", os.environ["CUDA_VISIBLE_DEVICES"])
+    else:
+        print("CUDA_VISIBLE_DEVICES not set.")
+
+
+# Ask user if they would like to use either their GPU or their CPU for transcription
+def decide_cpugpu():
+    global processing_choice
+    processing_input = input("Would you like to use your GPU or CPU for transcription? (1/cuda)GPU/(2/cpu)CPU): ")
+    if processing_choice == "cuda" and (processing_input.lower() == "cuda" or processing_input == "1"):
+        print("You've chosen to use the GPU.")
+        logging.debug("GPU is being used for processing")
+        processing_choice = "cuda"
+    elif processing_input.lower() == "cpu" or processing_input == "2":
+        print("You've chosen to use the CPU.")
+        logging.debug("CPU is being used for processing")
+        processing_choice = "cpu"
+    else:
+        print("Invalid choice. Please select either GPU or CPU.")
+
+
+# check for existence of ffmpeg
+def check_ffmpeg():
+    if shutil.which("ffmpeg") or (os.path.exists("Bin") and os.path.isfile(".\\Bin\\ffmpeg.exe")):
+        logging.debug("ffmpeg found installed on the local system, in the local PATH, or in the './Bin' folder")
+        pass
+    else:
+        logging.debug("ffmpeg not installed on the local system/in local PATH")
+        print(
+            "ffmpeg is not installed.\n\n You can either install it manually, or through your package manager of "
+            "choice.\n Windows users, builds are here: https://www.gyan.dev/ffmpeg/builds/")
+        if userOS == "Windows":
+            download_ffmpeg()
+        elif userOS == "Linux":
+            print(
+                "You should install ffmpeg using your platform's appropriate package manager, 'apt install ffmpeg',"
+                "'dnf install ffmpeg' or 'pacman', etc.")
+        else:
+            logging.debug("running an unsupported OS")
+            print("You're running an unspported/Un-tested OS")
+            exit_script = input("Let's exit the script, unless you're feeling lucky? (y/n)")
+            if exit_script == "y" or "yes" or "1":
+                exit()
+
+
+# Download ffmpeg
+def download_ffmpeg():
+    user_choice = input("Do you want to download ffmpeg? (y)Yes/(n)No: ")
+    if user_choice.lower() in ['yes', 'y', '1']:
+        print("Downloading ffmpeg")
+        url = "https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-essentials.zip"
+        response = requests.get(url)
+
+        if response.status_code == 200:
+            print("Saving ffmpeg zip file")
+            logging.debug("Saving ffmpeg zip file")
+            zip_path = "ffmpeg-release-essentials.zip"
+            with open(zip_path, 'wb') as file:
+                file.write(response.content)
+
+            logging.debug("Extracting the 'ffmpeg.exe' file from the zip")
+            print("Extracting ffmpeg.exe from zip file to '/Bin' folder")
+            with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                # Find the ffmpeg.exe file within the zip
+                ffmpeg_path = None
+                for file_info in zip_ref.infolist():
+                    if file_info.filename.endswith("ffmpeg.exe"):
+                        ffmpeg_path = file_info.filename
+                        break
+
+                if ffmpeg_path is None:
+                    logging.error("ffmpeg.exe not found in the zip file.")
+                    print("ffmpeg.exe not found in the zip file.")
+                    return
+
+                logging.debug("checking if the './Bin' folder exists, creating if not")
+                bin_folder = "Bin"
+                if not os.path.exists(bin_folder):
+                    logging.debug("Creating a folder for './Bin', it didn't previously exist")
+                    os.makedirs(bin_folder)
+
+                logging.debug("Extracting 'ffmpeg.exe' to the './Bin' folder")
+                zip_ref.extract(ffmpeg_path, path=bin_folder)
+
+                logging.debug("Moving 'ffmpeg.exe' to the './Bin' folder")
+                src_path = os.path.join(bin_folder, ffmpeg_path)
+                dst_path = os.path.join(bin_folder, "ffmpeg.exe")
+                shutil.move(src_path, dst_path)
+
+            logging.debug("Removing ffmpeg zip file")
+            print("Deleting zip file (we've already extracted ffmpeg.exe, no worries)")
+            os.remove(zip_path)
+
+            logging.debug("ffmpeg.exe has been downloaded and extracted to the './Bin' folder.")
+            print("ffmpeg.exe has been successfully downloaded and extracted to the './Bin' folder.")
+        else:
+            logging.error("Failed to download the zip file.")
+            print("Failed to download the zip file.")
+    else:
+        logging.debug("User chose to not download ffmpeg")
+        print("ffmpeg will not be downloaded.")
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/Tokenization_Methods_Lib.py

@@ -0,0 +1,30 @@
+# Tokenization_Methods_Lib.py
+#########################################
+# Tokenization Methods Library
+# This library is used to handle tokenization of text for summarization.
+#
+####
+import tiktoken
+
+# Import Local
+from typing import List
+
+####################
+# Function List
+#
+# 1. openai_tokenize(text: str) -> List[str]
+#
+####################
+
+
+#######################################################################################################################
+# Function Definitions
+#
+
+def openai_tokenize(text: str) -> List[str]:
+    encoding = tiktoken.encoding_for_model('gpt-4-turbo')
+    return encoding.encode(text)
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/Tone-Changer.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import json
+from transformers import pipeline
+
+# Initialize the text generation pipeline
+generator = pipeline('text-generation', model='gpt2')
+
+
+def adjust_tone(text, concise, casual):
+    tones = [
+        {"tone": "concise", "weight": concise},
+        {"tone": "casual", "weight": casual},
+        {"tone": "professional", "weight": 1 - casual},
+        {"tone": "expanded", "weight": 1 - concise}
+    ]
+    tones = sorted(tones, key=lambda x: x['weight'], reverse=True)[:2]
+
+    tone_prompt = " and ".join([f"{t['tone']} (weight: {t['weight']:.2f})" for t in tones])
+
+    prompt = f"Rewrite the following text to match these tones: {tone_prompt}. Text: {text}"
+
+    result = generator(prompt, max_length=100, num_return_sequences=1)
+    return result[0]['generated_text']
+
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Tone Adjuster")
+
+    input_text = gr.Textbox(label="Input Text")
+
+    with gr.Row():
+        concise_slider = gr.Slider(minimum=0, maximum=1, value=0.5, label="Concise vs Expanded")
+        casual_slider = gr.Slider(minimum=0, maximum=1, value=0.5, label="Casual vs Professional")
+
+    output_text = gr.Textbox(label="Adjusted Text")
+
+    adjust_btn = gr.Button("Adjust Tone")
+
+    adjust_btn.click(
+        adjust_tone,
+        inputs=[input_text, concise_slider, casual_slider],
+        outputs=output_text
+    )
+
+demo.launch()

App_Function_Libraries/Utils.py

@@ -0,0 +1,440 @@
+# Utils.py
+#########################################
+# General Utilities Library
+# This library is used to hold random utilities used by various other libraries.
+#
+####
+####################
+# Function List
+#
+# 1. extract_text_from_segments(segments: List[Dict]) -> str
+# 2. download_file(url, dest_path, expected_checksum=None, max_retries=3, delay=5)
+# 3. verify_checksum(file_path, expected_checksum)
+# 4. create_download_directory(title)
+# 5. sanitize_filename(filename)
+# 6. normalize_title(title)
+# 7.
+#
+#
+#
+####################
+# Import necessary libraries
+import configparser
+import hashlib
+import json
+import logging
+from datetime import timedelta
+from urllib.parse import urlparse, parse_qs, urlencode, urlunparse
+
+import requests
+import time
+from tqdm import tqdm
+import os
+import re
+import unicodedata
+
+from App_Function_Libraries.Video_DL_Ingestion_Lib import get_youtube
+
+
+#######################################################################################################################
+# Function Definitions
+#
+
+def extract_text_from_segments(segments):
+    logging.debug(f"Segments received: {segments}")
+    logging.debug(f"Type of segments: {type(segments)}")
+
+    def extract_text_recursive(data):
+        if isinstance(data, dict):
+            for key, value in data.items():
+                if key == 'Text':
+                    return value
+                elif isinstance(value, (dict, list)):
+                    result = extract_text_recursive(value)
+                    if result:
+                        return result
+        elif isinstance(data, list):
+            return ' '.join(filter(None, [extract_text_recursive(item) for item in data]))
+        return None
+
+    text = extract_text_recursive(segments)
+
+    if text:
+        return text.strip()
+    else:
+        logging.error(f"Unable to extract text from segments: {segments}")
+        return "Error: Unable to extract transcription"
+
+
+def download_file(url, dest_path, expected_checksum=None, max_retries=3, delay=5):
+    temp_path = dest_path + '.tmp'
+
+    for attempt in range(max_retries):
+        try:
+            # Check if a partial download exists and get its size
+            resume_header = {}
+            if os.path.exists(temp_path):
+                resume_header = {'Range': f'bytes={os.path.getsize(temp_path)}-'}
+
+            response = requests.get(url, stream=True, headers=resume_header)
+            response.raise_for_status()
+
+            # Get the total file size from headers
+            total_size = int(response.headers.get('content-length', 0))
+            initial_pos = os.path.getsize(temp_path) if os.path.exists(temp_path) else 0
+
+            mode = 'ab' if 'Range' in response.headers else 'wb'
+            with open(temp_path, mode) as temp_file, tqdm(
+                total=total_size, unit='B', unit_scale=True, desc=dest_path, initial=initial_pos, ascii=True
+            ) as pbar:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:  # filter out keep-alive new chunks
+                        temp_file.write(chunk)
+                        pbar.update(len(chunk))
+
+            # Verify the checksum if provided
+            if expected_checksum:
+                if not verify_checksum(temp_path, expected_checksum):
+                    os.remove(temp_path)
+                    raise ValueError("Downloaded file's checksum does not match the expected checksum")
+
+            # Move the file to the final destination
+            os.rename(temp_path, dest_path)
+            print("Download complete and verified!")
+            return dest_path
+
+        except Exception as e:
+            print(f"Attempt {attempt + 1} failed: {e}")
+            if attempt < max_retries - 1:
+                print(f"Retrying in {delay} seconds...")
+                time.sleep(delay)
+            else:
+                print("Max retries reached. Download failed.")
+                raise
+
+
+def verify_checksum(file_path, expected_checksum):
+    sha256_hash = hashlib.sha256()
+    with open(file_path, 'rb') as f:
+        for byte_block in iter(lambda: f.read(4096), b''):
+            sha256_hash.update(byte_block)
+    return sha256_hash.hexdigest() == expected_checksum
+
+
+def create_download_directory(title):
+    base_dir = "Results"
+    # Remove characters that are illegal in Windows filenames and normalize
+    safe_title = normalize_title(title)
+    logging.debug(f"{title} successfully normalized")
+    session_path = os.path.join(base_dir, safe_title)
+    if not os.path.exists(session_path):
+        os.makedirs(session_path, exist_ok=True)
+        logging.debug(f"Created directory for downloaded video: {session_path}")
+    else:
+        logging.debug(f"Directory already exists for downloaded video: {session_path}")
+    return session_path
+
+
+def sanitize_filename(filename):
+    # Remove invalid characters and replace spaces with underscores
+    sanitized = re.sub(r'[<>:"/\\|?*]', '', filename)
+    sanitized = re.sub(r'\s+', ' ', sanitized).strip()
+    return sanitized
+
+
+def normalize_title(title):
+    # Normalize the string to 'NFKD' form and encode to 'ascii' ignoring non-ascii characters
+    title = unicodedata.normalize('NFKD', title).encode('ascii', 'ignore').decode('ascii')
+    title = title.replace('/', '_').replace('\\', '_').replace(':', '_').replace('"', '').replace('*', '').replace('?',
+                                                                                                                   '').replace(
+        '<', '').replace('>', '').replace('|', '')
+    return title
+
+
+
+
+def clean_youtube_url(url):
+    parsed_url = urlparse(url)
+    query_params = parse_qs(parsed_url.query)
+    if 'list' in query_params:
+        query_params.pop('list')
+    cleaned_query = urlencode(query_params, doseq=True)
+    cleaned_url = urlunparse(parsed_url._replace(query=cleaned_query))
+    return cleaned_url
+
+
+def extract_video_info(url):
+    info_dict = get_youtube(url)
+    title = info_dict.get('title', 'Untitled')
+    return info_dict, title
+
+
+def clean_youtube_url(url):
+    parsed_url = urlparse(url)
+    query_params = parse_qs(parsed_url.query)
+    if 'list' in query_params:
+        query_params.pop('list')
+    cleaned_query = urlencode(query_params, doseq=True)
+    cleaned_url = urlunparse(parsed_url._replace(query=cleaned_query))
+    return cleaned_url
+
+def extract_video_info(url):
+    info_dict = get_youtube(url)
+    title = info_dict.get('title', 'Untitled')
+    return info_dict, title
+
+def import_data(file):
+    # Implement this function to import data from a file
+    pass
+
+
+
+
+#######################
+# Config loading
+#
+
+def load_comprehensive_config():
+    # Get the directory of the current script
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    # Go up one level to the project root directory
+    project_root = os.path.dirname(current_dir)
+    # Construct the path to the config file in the project root directory
+    config_path = os.path.join(project_root, 'config.txt')
+    # Create a ConfigParser object
+    config = configparser.ConfigParser()
+    # Read the configuration file
+    files_read = config.read(config_path)
+    if not files_read:
+        raise FileNotFoundError(f"Config file not found at {config_path}")
+    return config
+
+
+def load_and_log_configs():
+    try:
+        config = load_comprehensive_config()
+        if config is None:
+            logging.error("Config is None, cannot proceed")
+            return None
+        # API Keys
+        anthropic_api_key = config.get('API', 'anthropic_api_key', fallback=None)
+        logging.debug(
+            f"Loaded Anthropic API Key: {anthropic_api_key[:5]}...{anthropic_api_key[-5:] if anthropic_api_key else None}")
+
+        cohere_api_key = config.get('API', 'cohere_api_key', fallback=None)
+        logging.debug(
+            f"Loaded Cohere API Key: {cohere_api_key[:5]}...{cohere_api_key[-5:] if cohere_api_key else None}")
+
+        groq_api_key = config.get('API', 'groq_api_key', fallback=None)
+        logging.debug(f"Loaded Groq API Key: {groq_api_key[:5]}...{groq_api_key[-5:] if groq_api_key else None}")
+
+        openai_api_key = config.get('API', 'openai_api_key', fallback=None)
+        logging.debug(
+            f"Loaded OpenAI API Key: {openai_api_key[:5]}...{openai_api_key[-5:] if openai_api_key else None}")
+
+        huggingface_api_key = config.get('API', 'huggingface_api_key', fallback=None)
+        logging.debug(
+            f"Loaded HuggingFace API Key: {huggingface_api_key[:5]}...{huggingface_api_key[-5:] if huggingface_api_key else None}")
+
+        openrouter_api_key = config.get('API', 'openrouter_api_key', fallback=None)
+        logging.debug(
+            f"Loaded OpenRouter API Key: {openrouter_api_key[:5]}...{openrouter_api_key[-5:] if openrouter_api_key else None}")
+
+        deepseek_api_key = config.get('API', 'deepseek_api_key', fallback=None)
+        logging.debug(
+            f"Loaded DeepSeek API Key: {deepseek_api_key[:5]}...{deepseek_api_key[-5:] if deepseek_api_key else None}")
+
+        # Models
+        anthropic_model = config.get('API', 'anthropic_model', fallback='claude-3-sonnet-20240229')
+        cohere_model = config.get('API', 'cohere_model', fallback='command-r-plus')
+        groq_model = config.get('API', 'groq_model', fallback='llama3-70b-8192')
+        openai_model = config.get('API', 'openai_model', fallback='gpt-4-turbo')
+        huggingface_model = config.get('API', 'huggingface_model', fallback='CohereForAI/c4ai-command-r-plus')
+        openrouter_model = config.get('API', 'openrouter_model', fallback='microsoft/wizardlm-2-8x22b')
+        deepseek_model = config.get('API', 'deepseek_model', fallback='deepseek-chat')
+
+        logging.debug(f"Loaded Anthropic Model: {anthropic_model}")
+        logging.debug(f"Loaded Cohere Model: {cohere_model}")
+        logging.debug(f"Loaded Groq Model: {groq_model}")
+        logging.debug(f"Loaded OpenAI Model: {openai_model}")
+        logging.debug(f"Loaded HuggingFace Model: {huggingface_model}")
+        logging.debug(f"Loaded OpenRouter Model: {openrouter_model}")
+
+        # Local-Models
+        kobold_api_IP = config.get('Local-API', 'kobold_api_IP', fallback='http://127.0.0.1:5000/api/v1/generate')
+        kobold_api_key = config.get('Local-API', 'kobold_api_key', fallback='')
+
+        llama_api_IP = config.get('Local-API', 'llama_api_IP', fallback='http://127.0.0.1:8080/v1/chat/completions')
+        llama_api_key = config.get('Local-API', 'llama_api_key', fallback='')
+
+        ooba_api_IP = config.get('Local-API', 'ooba_api_IP', fallback='http://127.0.0.1:5000/v1/chat/completions')
+        ooba_api_key = config.get('Local-API', 'ooba_api_key', fallback='')
+
+        tabby_api_IP = config.get('Local-API', 'tabby_api_IP', fallback='http://127.0.0.1:5000/api/v1/generate')
+        tabby_api_key = config.get('Local-API', 'tabby_api_key', fallback=None)
+
+        vllm_api_url = config.get('Local-API', 'vllm_api_IP', fallback='http://127.0.0.1:500/api/v1/chat/completions')
+        vllm_api_key = config.get('Local-API', 'vllm_api_key', fallback=None)
+
+        logging.debug(f"Loaded Kobold API IP: {kobold_api_IP}")
+        logging.debug(f"Loaded Llama API IP: {llama_api_IP}")
+        logging.debug(f"Loaded Ooba API IP: {ooba_api_IP}")
+        logging.debug(f"Loaded Tabby API IP: {tabby_api_IP}")
+        logging.debug(f"Loaded VLLM API URL: {vllm_api_url}")
+
+        # Retrieve output paths from the configuration file
+        output_path = config.get('Paths', 'output_path', fallback='results')
+        logging.debug(f"Output path set to: {output_path}")
+
+        # Retrieve processing choice from the configuration file
+        processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')
+        logging.debug(f"Processing choice set to: {processing_choice}")
+
+        # Prompts - FIXME
+        prompt_path = config.get('Prompts', 'prompt_path', fallback='prompts.db')
+
+        return {
+            'api_keys': {
+                'anthropic': anthropic_api_key,
+                'cohere': cohere_api_key,
+                'groq': groq_api_key,
+                'openai': openai_api_key,
+                'huggingface': huggingface_api_key,
+                'openrouter': openrouter_api_key,
+                'deepseek': deepseek_api_key
+            },
+            'models': {
+                'anthropic': anthropic_model,
+                'cohere': cohere_model,
+                'groq': groq_model,
+                'openai': openai_model,
+                'huggingface': huggingface_model,
+                'openrouter': openrouter_model,
+                'deepseek': deepseek_model
+            },
+            'local_apis': {
+                'kobold': {'ip': kobold_api_IP, 'key': kobold_api_key},
+                'llama': {'ip': llama_api_IP, 'key': llama_api_key},
+                'ooba': {'ip': ooba_api_IP, 'key': ooba_api_key},
+                'tabby': {'ip': tabby_api_IP, 'key': tabby_api_key},
+                'vllm': {'ip': vllm_api_url, 'key': vllm_api_key}
+            },
+            'output_path': output_path,
+            'processing_choice': processing_choice
+        }
+
+    except Exception as e:
+        logging.error(f"Error loading config: {str(e)}")
+        return None
+
+
+
+# Log file
+# logging.basicConfig(filename='debug-runtime.log', encoding='utf-8', level=logging.DEBUG)
+
+
+
+
+
+
+
+def format_metadata_as_text(metadata):
+    if not metadata:
+        return "No metadata available"
+
+    formatted_text = "Video Metadata:\n"
+    for key, value in metadata.items():
+        if value is not None:
+            if isinstance(value, list):
+                # Join list items with commas
+                formatted_value = ", ".join(str(item) for item in value)
+            elif key == 'upload_date' and len(str(value)) == 8:
+                # Format date as YYYY-MM-DD
+                formatted_value = f"{value[:4]}-{value[4:6]}-{value[6:]}"
+            elif key in ['view_count', 'like_count']:
+                # Format large numbers with commas
+                formatted_value = f"{value:,}"
+            elif key == 'duration':
+                # Convert seconds to HH:MM:SS format
+                hours, remainder = divmod(value, 3600)
+                minutes, seconds = divmod(remainder, 60)
+                formatted_value = f"{hours:02d}:{minutes:02d}:{seconds:02d}"
+            else:
+                formatted_value = str(value)
+
+            formatted_text += f"{key.capitalize()}: {formatted_value}\n"
+    return formatted_text.strip()
+
+# # Example usage:
+# example_metadata = {
+#     'title': 'Sample Video Title',
+#     'uploader': 'Channel Name',
+#     'upload_date': '20230615',
+#     'view_count': 1000000,
+#     'like_count': 50000,
+#     'duration': 3725,  # 1 hour, 2 minutes, 5 seconds
+#     'tags': ['tag1', 'tag2', 'tag3'],
+#     'description': 'This is a sample video description.'
+# }
+#
+# print(format_metadata_as_text(example_metadata))
+
+
+
+def convert_to_seconds(time_str):
+    if not time_str:
+        return 0
+
+    # If it's already a number, assume it's in seconds
+    if time_str.isdigit():
+        return int(time_str)
+
+    # Parse time string in format HH:MM:SS, MM:SS, or SS
+    time_parts = time_str.split(':')
+    if len(time_parts) == 3:
+        return int(timedelta(hours=int(time_parts[0]),
+                             minutes=int(time_parts[1]),
+                             seconds=int(time_parts[2])).total_seconds())
+    elif len(time_parts) == 2:
+        return int(timedelta(minutes=int(time_parts[0]),
+                             seconds=int(time_parts[1])).total_seconds())
+    elif len(time_parts) == 1:
+        return int(time_parts[0])
+    else:
+        raise ValueError(f"Invalid time format: {time_str}")
+
+
+def save_to_file(video_urls, filename):
+    with open(filename, 'w') as file:
+        file.write('\n'.join(video_urls))
+    print(f"Video URLs saved to {filename}")
+
+
+def save_segments_to_json(segments, file_name="transcription_segments.json"):
+    """
+    Save transcription segments to a JSON file.
+
+    Parameters:
+    segments (list): List of transcription segments
+    file_name (str): Name of the JSON file to save (default: "transcription_segments.json")
+
+    Returns:
+    str: Path to the saved JSON file
+    """
+    # Ensure the Results directory exists
+    os.makedirs("Results", exist_ok=True)
+
+    # Full path for the JSON file
+    json_file_path = os.path.join("Results", file_name)
+
+    # Save segments to JSON file
+    with open(json_file_path, 'w', encoding='utf-8') as json_file:
+        json.dump(segments, json_file, ensure_ascii=False, indent=4)
+
+    return json_file_path
+
+
+
+
+
+

App_Function_Libraries/Video_DL_Ingestion_Lib.py

@@ -0,0 +1,315 @@
+# Video_DL_Ingestion_Lib.py
+#########################################
+# Video Downloader and Ingestion Library
+# This library is used to handle downloading videos from YouTube and other platforms.
+# It also handles the ingestion of the videos into the database.
+# It uses yt-dlp to extract video information and download the videos.
+####
+import json
+####################
+# Function List
+#
+# 1. get_video_info(url)
+# 2. create_download_directory(title)
+# 3. sanitize_filename(title)
+# 4. normalize_title(title)
+# 5. get_youtube(video_url)
+# 6. get_playlist_videos(playlist_url)
+# 7. download_video(video_url, download_path, info_dict, download_video_flag)
+# 8. save_to_file(video_urls, filename)
+# 9. save_summary_to_file(summary, file_path)
+# 10. process_url(url, num_speakers, whisper_model, custom_prompt, offset, api_name, api_key, vad_filter, download_video, download_audio, rolling_summarization, detail_level, question_box, keywords, chunk_summarization, chunk_duration_input, words_per_second_input)
+#
+#
+####################
+# Import necessary libraries to run solo for testing
+import logging
+import os
+import re
+import sys
+from urllib.parse import urlparse, parse_qs
+
+import unicodedata
+# 3rd-Party Imports
+import yt_dlp
+# Import Local
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+def normalize_title(title):
+    # Normalize the string to 'NFKD' form and encode to 'ascii' ignoring non-ascii characters
+    title = unicodedata.normalize('NFKD', title).encode('ascii', 'ignore').decode('ascii')
+    title = title.replace('/', '_').replace('\\', '_').replace(':', '_').replace('"', '').replace('*', '').replace('?',
+                                                                                                                   '').replace(
+        '<', '').replace('>', '').replace('|', '')
+    return title
+
+def get_video_info(url: str) -> dict:
+    ydl_opts = {
+        'quiet': True,
+        'no_warnings': True,
+        'skip_download': True,
+    }
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        try:
+            info_dict = ydl.extract_info(url, download=False)
+            return info_dict
+        except Exception as e:
+            logging.error(f"Error extracting video info: {e}")
+            return None
+
+
+def get_youtube(video_url):
+    ydl_opts = {
+        'format': 'bestaudio[ext=m4a]',
+        'noplaylist': False,
+        'quiet': True,
+        'extract_flat': True
+    }
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        logging.debug("About to extract youtube info")
+        info_dict = ydl.extract_info(video_url, download=False)
+        logging.debug("Youtube info successfully extracted")
+    return info_dict
+
+
+def get_playlist_videos(playlist_url):
+    ydl_opts = {
+        'extract_flat': True,
+        'skip_download': True,
+        'quiet': True
+    }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info = ydl.extract_info(playlist_url, download=False)
+
+        if 'entries' in info:
+            video_urls = [entry['url'] for entry in info['entries']]
+            playlist_title = info['title']
+            return video_urls, playlist_title
+        else:
+            print("No videos found in the playlist.")
+            return [], None
+
+
+def download_video(video_url, download_path, info_dict, download_video_flag):
+    global video_file_path, ffmpeg_path
+    global audio_file_path
+
+    # Normalize Video Title name
+    logging.debug("About to normalize downloaded video title")
+    if 'title' not in info_dict or 'ext' not in info_dict:
+        logging.error("info_dict is missing 'title' or 'ext'")
+        return None
+
+    normalized_video_title = normalize_title(info_dict['title'])
+    video_file_path = os.path.join(download_path, f"{normalized_video_title}.{info_dict['ext']}")
+
+    # Check for existence of video file
+    if os.path.exists(video_file_path):
+        logging.info(f"Video file already exists: {video_file_path}")
+        return video_file_path
+
+    # Setup path handling for ffmpeg on different OSs
+    if sys.platform.startswith('win'):
+        ffmpeg_path = os.path.join(os.getcwd(), 'Bin', 'ffmpeg.exe')
+    elif sys.platform.startswith('linux'):
+        ffmpeg_path = 'ffmpeg'
+    elif sys.platform.startswith('darwin'):
+        ffmpeg_path = 'ffmpeg'
+
+    if download_video_flag:
+        video_file_path = os.path.join(download_path, f"{normalized_video_title}.mp4")
+        ydl_opts_video = {
+            'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]',
+            'outtmpl': video_file_path,
+            'ffmpeg_location': ffmpeg_path
+        }
+
+        try:
+            with yt_dlp.YoutubeDL(ydl_opts_video) as ydl:
+                logging.debug("yt_dlp: About to download video with youtube-dl")
+                ydl.download([video_url])
+                logging.debug("yt_dlp: Video successfully downloaded with youtube-dl")
+                if os.path.exists(video_file_path):
+                    return video_file_path
+                else:
+                    logging.error("yt_dlp: Video file not found after download")
+                    return None
+        except Exception as e:
+            logging.error(f"yt_dlp: Error downloading video: {e}")
+            return None
+    elif not download_video_flag:
+        video_file_path = os.path.join(download_path, f"{normalized_video_title}.mp4")
+        # Set options for video and audio
+        ydl_opts = {
+            'format': 'bestaudio[ext=m4a]',
+            'quiet': True,
+            'outtmpl': video_file_path
+        }
+
+        try:
+            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                logging.debug("yt_dlp: About to download video with youtube-dl")
+                ydl.download([video_url])
+                logging.debug("yt_dlp: Video successfully downloaded with youtube-dl")
+                if os.path.exists(video_file_path):
+                    return video_file_path
+                else:
+                    logging.error("yt_dlp: Video file not found after download")
+                    return None
+        except Exception as e:
+            logging.error(f"yt_dlp: Error downloading video: {e}")
+            return None
+
+    else:
+        logging.debug("download_video: Download video flag is set to False and video file path is not found")
+        return None
+
+
+def extract_video_info(url):
+    try:
+        with yt_dlp.YoutubeDL({'quiet': True}) as ydl:
+            info = ydl.extract_info(url, download=False)
+
+            # Log only a subset of the info to avoid overwhelming the logs
+            log_info = {
+                'title': info.get('title'),
+                'duration': info.get('duration'),
+                'upload_date': info.get('upload_date')
+            }
+            logging.debug(f"Extracted info for {url}: {log_info}")
+
+            return info
+    except Exception as e:
+        logging.error(f"Error extracting video info for {url}: {str(e)}", exc_info=True)
+        return None
+
+
+def get_youtube_playlist_urls(playlist_id):
+    ydl_opts = {
+        'extract_flat': True,
+        'quiet': True,
+    }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        result = ydl.extract_info(f'https://www.youtube.com/playlist?list={playlist_id}', download=False)
+        return [entry['url'] for entry in result['entries'] if entry.get('url')]
+
+
+def parse_and_expand_urls(url_input):
+    logging.info(f"Starting parse_and_expand_urls with input: {url_input}")
+    urls = [url.strip() for url in url_input.split('\n') if url.strip()]
+    logging.info(f"Parsed URLs: {urls}")
+    expanded_urls = []
+
+    for url in urls:
+        try:
+            logging.info(f"Processing URL: {url}")
+            parsed_url = urlparse(url)
+            logging.debug(f"Parsed URL components: {parsed_url}")
+
+            # YouTube playlist handling
+            if 'youtube.com' in parsed_url.netloc and 'list' in parsed_url.query:
+                playlist_id = parse_qs(parsed_url.query)['list'][0]
+                logging.info(f"Detected YouTube playlist with ID: {playlist_id}")
+                playlist_urls = get_youtube_playlist_urls(playlist_id)
+                logging.info(f"Expanded playlist URLs: {playlist_urls}")
+                expanded_urls.extend(playlist_urls)
+
+            # YouTube short URL handling
+            elif 'youtu.be' in parsed_url.netloc:
+                video_id = parsed_url.path.lstrip('/')
+                full_url = f'https://www.youtube.com/watch?v={video_id}'
+                logging.info(f"Expanded YouTube short URL to: {full_url}")
+                expanded_urls.append(full_url)
+
+            # Vimeo handling
+            elif 'vimeo.com' in parsed_url.netloc:
+                video_id = parsed_url.path.lstrip('/')
+                full_url = f'https://vimeo.com/{video_id}'
+                logging.info(f"Processed Vimeo URL: {full_url}")
+                expanded_urls.append(full_url)
+
+            # Add more platform-specific handling here
+
+            else:
+                logging.info(f"URL not recognized as special case, adding as-is: {url}")
+                expanded_urls.append(url)
+
+        except Exception as e:
+            logging.error(f"Error processing URL {url}: {str(e)}", exc_info=True)
+            # Optionally, you might want to add the problematic URL to expanded_urls
+            # expanded_urls.append(url)
+
+    logging.info(f"Final expanded URLs: {expanded_urls}")
+    return expanded_urls
+
+
+def extract_metadata(url, use_cookies=False, cookies=None):
+    ydl_opts = {
+        'quiet': True,
+        'no_warnings': True,
+        'extract_flat': True,
+        'skip_download': True,
+    }
+
+    if use_cookies and cookies:
+        try:
+            cookie_dict = json.loads(cookies)
+            ydl_opts['cookiefile'] = cookie_dict
+        except json.JSONDecodeError:
+            logging.warning("Invalid cookie format. Proceeding without cookies.")
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        try:
+            info = ydl.extract_info(url, download=False)
+            metadata = {
+                'title': info.get('title'),
+                'uploader': info.get('uploader'),
+                'upload_date': info.get('upload_date'),
+                'view_count': info.get('view_count'),
+                'like_count': info.get('like_count'),
+                'duration': info.get('duration'),
+                'tags': info.get('tags'),
+                'description': info.get('description')
+            }
+
+            # Create a safe subset of metadata to log
+            safe_metadata = {
+                'title': metadata.get('title', 'No title'),
+                'duration': metadata.get('duration', 'Unknown duration'),
+                'upload_date': metadata.get('upload_date', 'Unknown upload date'),
+                'uploader': metadata.get('uploader', 'Unknown uploader')
+            }
+
+            logging.info(f"Successfully extracted metadata for {url}: {safe_metadata}")
+            return metadata
+        except Exception as e:
+            logging.error(f"Error extracting metadata for {url}: {str(e)}", exc_info=True)
+            return None
+
+
+def generate_timestamped_url(url, hours, minutes, seconds):
+    # Extract video ID from the URL
+    video_id_match = re.search(r'(?:v=|\/)([0-9A-Za-z_-]{11}).*', url)
+    if not video_id_match:
+        return "Invalid YouTube URL"
+
+    video_id = video_id_match.group(1)
+
+    # Calculate total seconds
+    total_seconds = int(hours) * 3600 + int(minutes) * 60 + int(seconds)
+
+    # Generate the new URL
+    new_url = f"https://www.youtube.com/watch?v={video_id}&t={total_seconds}s"
+
+    return new_url
+
+
+
+#
+#
+#######################################################################################################################

App_Function_Libraries/models/config.yaml

@@ -0,0 +1,21 @@
+version: 3.1.0
+
+pipeline:
+  name: pyannote.audio.pipelines.SpeakerDiarization
+  params:
+    clustering: AgglomerativeClustering
+    # embedding: pyannote/wespeaker-voxceleb-resnet34-LM # If you want to use the HF model
+    embedding: pyannote_model_wespeaker-voxceleb-resnet34-LM.bin # If you want to use the local model
+    embedding_batch_size: 32
+    embedding_exclude_overlap: true
+    # segmentation: pyannote/segmentation-3.0 # If you want to use the HF model
+    segmentation: pyannote_model_segmentation-3.0.bin  # If you want to use the local model
+    segmentation_batch_size: 32
+
+params:
+  clustering:
+    method: centroid
+    min_cluster_size: 12
+    threshold: 0.7045654963945799
+  segmentation:
+    min_duration_off: 0.0