Create backup-010924.app.py

backup-010924.app.py

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+# Imports
+import base64
+import glob
+import json
+import math
+import openai
+import os
+import pytz
+import re
+import requests
+import streamlit as st
+import textract
+import time
+import zipfile
+import huggingface_hub
+import dotenv
+from audio_recorder_streamlit import audio_recorder
+from bs4 import BeautifulSoup
+from collections import deque
+from datetime import datetime
+from dotenv import load_dotenv
+from huggingface_hub import InferenceClient
+from io import BytesIO
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from openai import ChatCompletion
+from PyPDF2 import PdfReader
+from templates import bot_template, css, user_template
+from xml.etree import ElementTree as ET
+import streamlit.components.v1 as components  # Import Streamlit Components for HTML5
+
+
+st.set_page_config(page_title="🐪Llama🦙Whisperer", layout="wide")
+st.markdown('(Inference Endpoints)[https://ui.endpoints.huggingface.co/awacke1/endpoints]')
+
+def add_Med_Licensing_Exam_Dataset():
+    import streamlit as st
+    from datasets import load_dataset
+    dataset = load_dataset("augtoma/usmle_step_1")['test']  # Using 'test' split
+    st.title("USMLE Step 1 Dataset Viewer")
+    if len(dataset) == 0:
+        st.write("😢 The dataset is empty.")
+    else:
+        st.write("""
+        🔍 Use the search box to filter questions or use the grid to scroll through the dataset.
+        """)
+    
+        # 👩‍🔬 Search Box
+        search_term = st.text_input("Search for a specific question:", "")
+        
+        # 🎛 Pagination
+        records_per_page = 100
+        num_records = len(dataset)
+        num_pages = max(int(num_records / records_per_page), 1)
+        
+        # Skip generating the slider if num_pages is 1 (i.e., all records fit in one page)
+        if num_pages > 1:
+            page_number = st.select_slider("Select page:", options=list(range(1, num_pages + 1)))
+        else:
+            page_number = 1  # Only one page
+        
+        # 📊 Display Data
+        start_idx = (page_number - 1) * records_per_page
+        end_idx = start_idx + records_per_page
+    
+        # 🧪 Apply the Search Filter
+        filtered_data = []
+        for record in dataset[start_idx:end_idx]:
+            if isinstance(record, dict) and 'text' in record and 'id' in record:
+                if search_term:
+                    if search_term.lower() in record['text'].lower():
+                        st.markdown(record)
+                        filtered_data.append(record)
+                else:
+                    filtered_data.append(record)
+    
+        # 🌐 Render the Grid
+        for record in filtered_data:
+            st.write(f"## Question ID: {record['id']}")
+            st.write(f"### Question:")
+            st.write(f"{record['text']}")
+            st.write(f"### Answer:")
+            st.write(f"{record['answer']}")
+            st.write("---")
+    
+        st.write(f"😊 Total Records: {num_records} | 📄 Displaying {start_idx+1} to {min(end_idx, num_records)}")
+
+# 1. Constants and Top Level UI Variables
+
+# My Inference API Copy
+API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud'  # Dr Llama
+# Original:
+#API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
+API_KEY = os.getenv('API_KEY')
+MODEL1="meta-llama/Llama-2-7b-chat-hf"
+MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"
+HF_KEY = os.getenv('HF_KEY')
+headers = {
+    "Authorization": f"Bearer {HF_KEY}",
+    "Content-Type": "application/json"
+}
+key = os.getenv('OPENAI_API_KEY')
+prompt = f"Write instructions to teach anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface."
+should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")
+
+# 2. Prompt label button demo for LLM
+def add_witty_humor_buttons():
+    with st.expander("Wit and Humor 🤣", expanded=True):
+        # Tip about the Dromedary family
+        st.markdown("🔬 **Fun Fact**: Dromedaries, part of the camel family, have a single hump and are adapted to arid environments. Their 'superpowers' include the ability to survive without water for up to 7 days, thanks to their specialized blood cells and water storage in their hump.")
+        
+        # Define button descriptions
+        descriptions = {
+            "Generate Limericks 😂": "Write ten random adult limericks based on quotes that are tweet length and make you laugh 🎭",
+            "Wise Quotes 🧙": "Generate ten wise quotes that are tweet length 🦉",
+            "Funny Rhymes 🎤": "Create ten funny rhymes that are tweet length 🎶",
+            "Medical Jokes 💉": "Create ten medical jokes that are tweet length 🏥",
+            "Minnesota Humor ❄️": "Create ten jokes about Minnesota that are tweet length 🌨️",
+            "Top Funny Stories 📖": "Create ten funny stories that are tweet length 📚",
+            "More Funny Rhymes 🎙️": "Create ten more funny rhymes that are tweet length 🎵"
+        }
+        
+        # Create columns
+        col1, col2, col3 = st.columns([1, 1, 1], gap="small")
+        
+        # Add buttons to columns
+        if col1.button("Generate Limericks 😂"):
+            StreamLLMChatResponse(descriptions["Generate Limericks 😂"])
+        
+        if col2.button("Wise Quotes 🧙"):
+            StreamLLMChatResponse(descriptions["Wise Quotes 🧙"])
+        
+        if col3.button("Funny Rhymes 🎤"):
+            StreamLLMChatResponse(descriptions["Funny Rhymes 🎤"])
+        
+        col4, col5, col6 = st.columns([1, 1, 1], gap="small")
+        
+        if col4.button("Medical Jokes 💉"):
+            StreamLLMChatResponse(descriptions["Medical Jokes 💉"])
+        
+        if col5.button("Minnesota Humor ❄️"):
+            StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"])
+        
+        if col6.button("Top Funny Stories 📖"):
+            StreamLLMChatResponse(descriptions["Top Funny Stories 📖"])
+        
+        col7 = st.columns(1, gap="small")
+        
+        if col7[0].button("More Funny Rhymes 🎙️"):
+            StreamLLMChatResponse(descriptions["More Funny Rhymes 🎙️"])
+
+def SpeechSynthesis(result):
+    documentHTML5='''
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>Read It Aloud</title>
+        <script type="text/javascript">
+            function readAloud() {
+                const text = document.getElementById("textArea").value;
+                const speech = new SpeechSynthesisUtterance(text);
+                window.speechSynthesis.speak(speech);
+            }
+        </script>
+    </head>
+    <body>
+        <h1>🔊 Read It Aloud</h1>
+        <textarea id="textArea" rows="10" cols="80">
+    '''
+    documentHTML5 = documentHTML5 + result
+    documentHTML5 = documentHTML5 + '''
+        </textarea>
+        <br>
+        <button onclick="readAloud()">🔊 Read Aloud</button>
+    </body>
+    </html>
+    '''
+
+    components.html(documentHTML5, width=1280, height=1024)
+    #return result
+
+
+# 3. Stream Llama Response
+# @st.cache_resource
+def StreamLLMChatResponse(prompt):
+    try:
+        endpoint_url = API_URL
+        hf_token = API_KEY
+        client = InferenceClient(endpoint_url, token=hf_token)
+        gen_kwargs = dict(
+            max_new_tokens=512,
+            top_k=30,
+            top_p=0.9,
+            temperature=0.2,
+            repetition_penalty=1.02,
+            stop_sequences=["\nUser:", "<|endoftext|>", "</s>"],
+        )
+        stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)
+        report=[]
+        res_box = st.empty()
+        collected_chunks=[]
+        collected_messages=[]
+        allresults=''
+        for r in stream:
+            if r.token.special:
+                continue
+            if r.token.text in gen_kwargs["stop_sequences"]:
+                break
+            collected_chunks.append(r.token.text)
+            chunk_message = r.token.text
+            collected_messages.append(chunk_message)
+            try:
+                report.append(r.token.text)
+                if len(r.token.text) > 0:
+                    result="".join(report).strip()
+                    res_box.markdown(f'*{result}*')
+                    
+            except:
+                st.write('Stream llm issue')
+        SpeechSynthesis(result)
+        return result
+    except:
+        st.write('Llama model is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')
+
+# 4. Run query with payload
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    st.markdown(response.json())
+    return response.json()
+def get_output(prompt):
+    return query({"inputs": prompt})
+
+# 5. Auto name generated output files from time and content
+def generate_filename(prompt, file_type):
+    central = pytz.timezone('US/Central')
+    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
+    replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
+    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:45]
+    return f"{safe_date_time}_{safe_prompt}.{file_type}"
+
+# 6. Speech transcription via OpenAI service
+def transcribe_audio(openai_key, file_path, model):
+    openai.api_key = openai_key
+    OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
+    headers = {
+        "Authorization": f"Bearer {openai_key}",
+    }
+    with open(file_path, 'rb') as f:
+        data = {'file': f}
+        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
+    if response.status_code == 200:
+        st.write(response.json())
+        chatResponse = chat_with_model(response.json().get('text'), '') # *************************************
+        transcript = response.json().get('text')
+        filename = generate_filename(transcript, 'txt')
+        response = chatResponse
+        user_prompt = transcript
+        create_file(filename, user_prompt, response, should_save)
+        return transcript
+    else:
+        st.write(response.json())
+        st.error("Error in API call.")
+        return None
+
+# 7. Auto stop on silence audio control for recording WAV files
+def save_and_play_audio(audio_recorder):
+    audio_bytes = audio_recorder(key='audio_recorder')
+    if audio_bytes:
+        filename = generate_filename("Recording", "wav")
+        with open(filename, 'wb') as f:
+            f.write(audio_bytes)
+        st.audio(audio_bytes, format="audio/wav")
+        return filename
+    return None
+
+# 8. File creator that interprets type and creates output file for text, markdown and code
+def create_file(filename, prompt, response, should_save=True):
+    if not should_save:
+        return
+    base_filename, ext = os.path.splitext(filename)
+    if ext in ['.txt', '.htm', '.md']:
+        with open(f"{base_filename}.md", 'w') as file:
+            try:
+                content = prompt.strip() + '\r\n' + response
+                file.write(content)
+            except:
+                st.write('.')
+
+    #has_python_code = re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response)
+    #has_python_code = bool(re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response))
+        #if has_python_code:
+        #    python_code = re.findall(r"```python([\s\S]*?)```", response)[0].strip()
+        #    with open(f"{base_filename}-Code.py", 'w') as file:
+        #        file.write(python_code)
+        #    with open(f"{base_filename}.md", 'w') as file:
+        #        content = prompt.strip() + '\r\n' + response
+        #        file.write(content)
+            
+def truncate_document(document, length):
+    return document[:length]
+def divide_document(document, max_length):
+    return [document[i:i+max_length] for i in range(0, len(document), max_length)]
+
+# 9. Sidebar with UI controls to review and re-run prompts and continue responses
+@st.cache_resource
+def get_table_download_link(file_path):
+    with open(file_path, 'r') as file:
+        data = file.read()
+   
+    b64 = base64.b64encode(data.encode()).decode()  
+    file_name = os.path.basename(file_path)
+    ext = os.path.splitext(file_name)[1]  # get the file extension
+    if ext == '.txt':
+        mime_type = 'text/plain'
+    elif ext == '.py':
+        mime_type = 'text/plain'
+    elif ext == '.xlsx':
+        mime_type = 'text/plain'
+    elif ext == '.csv':
+        mime_type = 'text/plain'
+    elif ext == '.htm':
+        mime_type = 'text/html'
+    elif ext == '.md':
+        mime_type = 'text/markdown'
+    else:
+        mime_type = 'application/octet-stream'  # general binary data type
+    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
+    return href
+
+
+def CompressXML(xml_text):
+    root = ET.fromstring(xml_text)
+    for elem in list(root.iter()):
+        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
+            elem.parent.remove(elem)
+    return ET.tostring(root, encoding='unicode', method="xml")
+
+# 10. Read in and provide UI for past files
+@st.cache_resource
+def read_file_content(file,max_length):
+    if file.type == "application/json":
+        content = json.load(file)
+        return str(content)
+    elif file.type == "text/html" or file.type == "text/htm":
+        content = BeautifulSoup(file, "html.parser")
+        return content.text
+    elif file.type == "application/xml" or file.type == "text/xml":
+        tree = ET.parse(file)
+        root = tree.getroot()
+        xml = CompressXML(ET.tostring(root, encoding='unicode'))
+        return xml
+    elif file.type == "text/markdown" or file.type == "text/md":
+        md = mistune.create_markdown()
+        content = md(file.read().decode())
+        return content
+    elif file.type == "text/plain":
+        return file.getvalue().decode()
+    else:
+        return ""
+
+# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS
+@st.cache_resource
+def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
+    model = model_choice
+    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
+    conversation.append({'role': 'user', 'content': prompt})
+    if len(document_section)>0:
+        conversation.append({'role': 'assistant', 'content': document_section})
+    start_time = time.time()
+    report = []
+    res_box = st.empty()
+    collected_chunks = []
+    collected_messages = []
+    for chunk in openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=conversation, temperature=0.5, stream=True):
+        collected_chunks.append(chunk)  
+        chunk_message = chunk['choices'][0]['delta']  
+        collected_messages.append(chunk_message) 
+        content=chunk["choices"][0].get("delta",{}).get("content")
+        try:
+            report.append(content)
+            if len(content) > 0:
+                result = "".join(report).strip()
+                res_box.markdown(f'*{result}*') 
+        except:
+            st.write(' ')
+    full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
+    st.write("Elapsed time:")
+    st.write(time.time() - start_time)
+    return full_reply_content
+
+# 12. Embedding VectorDB for LLM query of documents to text to compress inputs and prompt together as Chat memory using Langchain
+@st.cache_resource
+def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
+    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
+    conversation.append({'role': 'user', 'content': prompt})
+    if len(file_content)>0:
+        conversation.append({'role': 'assistant', 'content': file_content})
+    response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
+    return response['choices'][0]['message']['content']
+
+def extract_mime_type(file):
+    if isinstance(file, str):
+        pattern = r"type='(.*?)'"
+        match = re.search(pattern, file)
+        if match:
+            return match.group(1)
+        else:
+            raise ValueError(f"Unable to extract MIME type from {file}")
+    elif isinstance(file, streamlit.UploadedFile):
+        return file.type
+    else:
+        raise TypeError("Input should be a string or a streamlit.UploadedFile object")
+
+def extract_file_extension(file):
+    # get the file name directly from the UploadedFile object
+    file_name = file.name
+    pattern = r".*?\.(.*?)$"
+    match = re.search(pattern, file_name)
+    if match:
+        return match.group(1)
+    else:
+        raise ValueError(f"Unable to extract file extension from {file_name}")
+
+# Normalize input as text from PDF and other formats
+@st.cache_resource
+def pdf2txt(docs):
+    text = ""
+    for file in docs:
+        file_extension = extract_file_extension(file)
+        st.write(f"File type extension: {file_extension}")
+        if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:
+            text += file.getvalue().decode('utf-8')
+        elif file_extension.lower() == 'pdf':
+            from PyPDF2 import PdfReader
+            pdf = PdfReader(BytesIO(file.getvalue()))
+            for page in range(len(pdf.pages)):
+                text += pdf.pages[page].extract_text() # new PyPDF2 syntax
+    return text
+
+def txt2chunks(text):
+    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
+    return text_splitter.split_text(text)
+
+# Vector Store using FAISS
+@st.cache_resource
+def vector_store(text_chunks):
+    embeddings = OpenAIEmbeddings(openai_api_key=key)
+    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+
+# Memory and Retrieval chains
+@st.cache_resource
+def get_chain(vectorstore):
+    llm = ChatOpenAI()
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
+
+def process_user_input(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+    for i, message in enumerate(st.session_state.chat_history):
+        template = user_template if i % 2 == 0 else bot_template
+        st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+        filename = generate_filename(user_question, 'txt')
+        response = message.content
+        user_prompt = user_question
+        create_file(filename, user_prompt, response, should_save)       
+
+def divide_prompt(prompt, max_length):
+    words = prompt.split()
+    chunks = []
+    current_chunk = []
+    current_length = 0
+    for word in words:
+        if len(word) + current_length <= max_length:
+            current_length += len(word) + 1 
+            current_chunk.append(word)
+        else:
+            chunks.append(' '.join(current_chunk))
+            current_chunk = [word]
+            current_length = len(word)
+    chunks.append(' '.join(current_chunk))
+    return chunks
+
+    
+# 13. Provide way of saving all and deleting all to give way of reviewing output and saving locally before clearing it
+    
+@st.cache_resource
+def create_zip_of_files(files):
+    zip_name = "all_files.zip"
+    with zipfile.ZipFile(zip_name, 'w') as zipf:
+        for file in files:
+            zipf.write(file)
+    return zip_name
+    
+@st.cache_resource
+def get_zip_download_link(zip_file):
+    with open(zip_file, 'rb') as f:
+        data = f.read()
+    b64 = base64.b64encode(data).decode()
+    href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
+    return href
+
+# 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10
+# My Inference Endpoint
+#API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
+# Original
+#API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en"
+# A10 Inference Endpoint for whisper large tests
+API_URL_IE = "https://hifdvffh2em0wn50.us-east-1.aws.endpoints.huggingface.cloud"
+
+MODEL2 = "openai/whisper-small.en"
+MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"
+#headers = {
+#	"Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
+#	"Content-Type": "audio/wav"
+#}
+HF_KEY = os.getenv('HF_KEY')
+headers = {
+    "Authorization": f"Bearer {HF_KEY}",
+    "Content-Type": "audio/wav"
+}
+
+#@st.cache_resource
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL_IE, headers=headers, data=data)
+    return response.json()
+
+def generate_filename(prompt, file_type):
+    central = pytz.timezone('US/Central')
+    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
+    replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
+    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
+    return f"{safe_date_time}_{safe_prompt}.{file_type}"
+
+# 15. Audio recorder to Wav file 
+def save_and_play_audio(audio_recorder):
+    audio_bytes = audio_recorder()
+    if audio_bytes:
+        filename = generate_filename("Recording", "wav")
+        with open(filename, 'wb') as f:
+            f.write(audio_bytes)
+        st.audio(audio_bytes, format="audio/wav")
+        return filename
+
+# 16. Speech transcription to file output
+def transcribe_audio(filename):
+    output = query(filename)
+    return output
+
+
+def whisper_main():
+    st.title("1🐪Llama🦙Whisperer")
+    st.write("Record your speech and get the text.")
+
+    # Audio, transcribe, GPT:
+    filename = save_and_play_audio(audio_recorder)
+    if filename is not None:
+        transcription = transcribe_audio(filename)
+        #try:
+        
+        transcript = transcription['text']
+        #except:
+        #st.write('Whisper model is asleep. Starting up now on T4 GPU - please give 5 minutes then retry as it scales up from zero to activate running container(s).')
+
+        st.write(transcript)
+        response = StreamLLMChatResponse(transcript)
+        # st.write(response) - redundant with streaming result?
+        filename = generate_filename(transcript, ".txt")
+        create_file(filename, transcript, response, should_save)
+        #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
+
+import streamlit as st
+
+# Sample function to demonstrate a response, replace with your own logic
+def StreamMedChatResponse(topic):
+    st.write(f"Showing resources or questions related to: {topic}")
+
+def add_multi_system_agent_topics():
+    with st.expander("Multi-System Agent AI Topics 🤖", expanded=True):
+        st.markdown("🤖 **Explore Multi-System Agent AI Topics**: This section provides a variety of topics related to multi-system agent AI systems.")
+
+        # Define multi-system agent AI topics and descriptions
+        descriptions = {
+            "Reinforcement Learning 🎮": "Questions related to reinforcement learning algorithms and applications 🕹️",
+            "Natural Language Processing 🗣️": "Questions about natural language processing techniques and chatbot development 🗨️",
+            "Multi-Agent Systems 🤝": "Questions pertaining to multi-agent systems and cooperative AI interactions 🤖",
+            "Conversational AI 🗨️": "Questions on building conversational AI agents and chatbots for various platforms 💬",
+            "Distributed AI Systems 🌐": "Questions about distributed AI systems and their implementation in networked environments 🌐",
+            "AI Ethics and Bias 🤔": "Questions related to ethics and bias considerations in AI systems and decision-making 🧠",
+            "AI in Healthcare 🏥": "Questions about the application of AI in healthcare and medical diagnosis 🩺",
+            "AI in Autonomous Vehicles 🚗": "Questions on the use of AI in autonomous vehicles and self-driving technology 🚗"
+        }
+
+        # Create columns
+        col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small")
+        
+        # Add buttons to columns
+        if col1.button("Reinforcement Learning 🎮"):
+            st.write(descriptions["Reinforcement Learning 🎮"])
+            StreamLLMChatResponse(descriptions["Reinforcement Learning 🎮"])
+
+        if col2.button("Natural Language Processing 🗣️"):
+            st.write(descriptions["Natural Language Processing 🗣️"])
+            StreamLLMChatResponse(descriptions["Natural Language Processing 🗣️"])
+
+        if col3.button("Multi-Agent Systems 🤝"):
+            st.write(descriptions["Multi-Agent Systems 🤝"])
+            StreamLLMChatResponse(descriptions["Multi-Agent Systems 🤝"])
+
+        if col4.button("Conversational AI 🗨️"):
+            st.write(descriptions["Conversational AI 🗨️"])
+            StreamLLMChatResponse(descriptions["Conversational AI 🗨️"])
+
+        col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small")
+
+        if col5.button("Distributed AI Systems 🌐"):
+            st.write(descriptions["Distributed AI Systems 🌐"])
+            StreamLLMChatResponse(descriptions["Distributed AI Systems 🌐"])
+
+        if col6.button("AI Ethics and Bias 🤔"):
+            st.write(descriptions["AI Ethics and Bias 🤔"])
+            StreamLLMChatResponse(descriptions["AI Ethics and Bias 🤔"])
+
+        if col7.button("AI in Healthcare 🏥"):
+            st.write(descriptions["AI in Healthcare 🏥"])
+            StreamLLMChatResponse(descriptions["AI in Healthcare 🏥"])
+
+        if col8.button("AI in Autonomous Vehicles 🚗"):
+            st.write(descriptions["AI in Autonomous Vehicles 🚗"])
+            StreamLLMChatResponse(descriptions["AI in Autonomous Vehicles 🚗"])
+
+
+# 17. Main
+def main():
+
+    st.title("Try Some Topics:")
+    prompt = f"Write ten funny jokes that are tweet length stories that make you laugh.  Show as markdown outline with emojis for each."
+
+    # Add Wit and Humor buttons
+    # add_witty_humor_buttons()
+    # Calling the function to add the multi-system agent AI topics buttons
+    add_multi_system_agent_topics()
+
+    example_input = st.text_input("Enter your example text:", value=prompt, help="Enter text to get a response from DromeLlama.")
+    if st.button("Run Prompt With DromeLlama", help="Click to run the prompt."):
+        try:
+            StreamLLMChatResponse(example_input)
+        except:
+            st.write('DromeLlama is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')
+
+    openai.api_key = os.getenv('OPENAI_KEY')
+    menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
+    choice = st.sidebar.selectbox("Output File Type:", menu)
+    model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))        
+    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
+    collength, colupload = st.columns([2,3])  # adjust the ratio as needed
+    with collength:
+        max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
+    with colupload:
+        uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])
+    document_sections = deque()
+    document_responses = {}
+    if uploaded_file is not None:
+        file_content = read_file_content(uploaded_file, max_length)
+        document_sections.extend(divide_document(file_content, max_length))
+    if len(document_sections) > 0:
+        if st.button("👁️ View Upload"):
+            st.markdown("**Sections of the uploaded file:**")
+            for i, section in enumerate(list(document_sections)):
+                st.markdown(f"**Section {i+1}**\n{section}")
+        st.markdown("**Chat with the model:**")
+        for i, section in enumerate(list(document_sections)):
+            if i in document_responses:
+                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
+            else:
+                if st.button(f"Chat about Section {i+1}"):
+                    st.write('Reasoning with your inputs...')
+                    response = chat_with_model(user_prompt, section, model_choice)
+                    st.write('Response:')
+                    st.write(response)
+                    document_responses[i] = response
+                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
+                    create_file(filename, user_prompt, response, should_save)
+                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
+    if st.button('💬 Chat'):
+        st.write('Reasoning with your inputs...')
+        user_prompt_sections = divide_prompt(user_prompt, max_length)
+        full_response = ''
+        for prompt_section in user_prompt_sections:
+            response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
+            full_response += response + '\n'  # Combine the responses
+        response = full_response
+        st.write('Response:')
+        st.write(response)
+        filename = generate_filename(user_prompt, choice)
+        create_file(filename, user_prompt, response, should_save)
+        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
+
+    # Compose a file sidebar of past encounters
+    all_files = glob.glob("*.*")
+    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
+    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
+    if st.sidebar.button("🗑 Delete All"):
+        for file in all_files:
+            os.remove(file)
+        st.experimental_rerun()
+    if st.sidebar.button("⬇️ Download All"):
+        zip_file = create_zip_of_files(all_files)
+        st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)
+    file_contents=''
+    next_action=''
+    for file in all_files:
+        col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1])  # adjust the ratio as needed
+        with col1:
+            if st.button("🌐", key="md_"+file):  # md emoji button
+                with open(file, 'r') as f:
+                    file_contents = f.read()
+                    next_action='md'
+        with col2:
+            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
+        with col3:
+            if st.button("📂", key="open_"+file):  # open emoji button
+                with open(file, 'r') as f:
+                    file_contents = f.read()
+                    next_action='open'
+        with col4:
+            if st.button("🔍", key="read_"+file):  # search emoji button
+                with open(file, 'r') as f:
+                    file_contents = f.read()
+                    next_action='search'
+        with col5:
+            if st.button("🗑", key="delete_"+file):
+                os.remove(file)
+                st.experimental_rerun()
+
+                
+    if len(file_contents) > 0:
+        if next_action=='open':
+            file_content_area = st.text_area("File Contents:", file_contents, height=500)
+        if next_action=='md':
+            st.markdown(file_contents)
+        if next_action=='search':
+            file_content_area = st.text_area("File Contents:", file_contents, height=500)
+            st.write('Reasoning with your inputs...')
+
+            # new - llama
+            response = StreamLLMChatResponse(file_contents)
+            filename = generate_filename(user_prompt, ".md")
+            create_file(filename, file_contents, response, should_save)
+            SpeechSynthesis(response)
+            
+            # old - gpt
+            #response = chat_with_model(user_prompt, file_contents, model_choice)
+            #filename = generate_filename(file_contents, choice)
+            #create_file(filename, user_prompt, response, should_save)
+            
+            st.experimental_rerun()
+
+    # Feedback
+    # Step: Give User a Way to Upvote or Downvote
+    feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
+    if feedback == "👍 Upvote":
+        st.write("You upvoted 👍. Thank you for your feedback!")
+    else:
+        st.write("You downvoted 👎. Thank you for your feedback!")
+        
+    load_dotenv()
+    st.write(css, unsafe_allow_html=True)
+    st.header("Chat with documents :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        process_user_input(user_question)
+    with st.sidebar:
+        st.subheader("Your documents")
+        docs = st.file_uploader("import documents", accept_multiple_files=True)
+        with st.spinner("Processing"):
+            raw = pdf2txt(docs)
+            if len(raw) > 0:
+                length = str(len(raw))
+                text_chunks = txt2chunks(raw)
+                vectorstore = vector_store(text_chunks)
+                st.session_state.conversation = get_chain(vectorstore)
+                st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
+                filename = generate_filename(raw, 'txt')
+                create_file(filename, raw, '', should_save)
+
+# 18. Run AI Pipeline
+if __name__ == "__main__":
+    whisper_main()
+    main()
+    add_Med_Licensing_Exam_Dataset()