Spaces:
Running
Running
| import streamlit as st | |
| import streamlit.components.v1 as components | |
| import os | |
| import json | |
| import random | |
| import base64 | |
| import glob | |
| import math | |
| import openai | |
| import pytz | |
| import re | |
| import requests | |
| 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 openai import ChatCompletion | |
| from PyPDF2 import PdfReader | |
| from templates import bot_template, css, user_template | |
| from xml.etree import ElementTree as ET | |
| from PIL import Image | |
| from urllib.parse import quote # Ensure this import is included | |
| # Set initial page and app customization and configuration ------------------------- | |
| st.set_page_config( | |
| page_title="📖🔍GraphicNovelAI", | |
| page_icon="🔍📖", | |
| layout="wide", | |
| initial_sidebar_state="expanded", | |
| menu_items={ | |
| 'Get Help': 'https://huggingface.co/awacke1', | |
| 'Report a bug': "https://huggingface.co/spaces/awacke1/GraphicAINovel", | |
| 'About': "# Midjourney: https://discord.com/channels/@me/997514686608191558" | |
| } | |
| ) | |
| # Title and Help/About | |
| st.markdown('''### 📖✨🔍 GraphicNovelAI ''') | |
| with st.expander("Help / About 📚", expanded=False): | |
| st.markdown(''' | |
| - 🚀 **Unlock Plots:** Elevate your vocabulary with AI. Turns plots into thrilling experiences. | |
| - 📚 **Features:** Creates extensive glossaries & exciting challenges. | |
| - 🧙♂️ **Experience:** Become a graphic novel plot wizard, boost your language skills. | |
| - 🔎 **Query Use:** Input `?q=Palindrome` or `?query=Anagram` in URL for new challenges. | |
| ''') | |
| # Aaron's Intelligent Style Guide for AI Graphic Novel Writers | |
| parts_of_speech = [ | |
| {"type": "Noun", "description": "Person, place, thing, or idea", "example": "Hero, city, spaceship, justice"}, | |
| {"type": "Verb", "description": "Action or state of being", "example": "Fight, transform, is, become"}, | |
| {"type": "Adjective", "description": "Describes a noun", "example": "Mysterious, ancient, powerful, dark"}, | |
| {"type": "Adverb", "description": "Modifies verbs, adjectives, or other adverbs", "example": "Mysteriously, very, suddenly, heroically"}, | |
| {"type": "Conjunction", "description": "Connects clauses, sentences, or words", "example": "And, but, or, yet"}, | |
| {"type": "Interjection", "description": "Expresses emotion", "example": "Wow!, Ouch!, Haha!, Shhh!"}, | |
| {"type": "Idiom", "description": "Phrase with a figurative meaning", "example": "Break a leg, Spill the beans, Hit the road"}, | |
| {"type": "Symbolism", "description": "Objects, figures, or colors used to represent ideas or concepts", "example": "A rose for love, a storm for chaos"}, | |
| {"type": "Theme", "description": "Underlying message or main idea", "example": "The quest for identity, the battle between good and evil"}, | |
| {"type": "Motif", "description": "Recurring element that has symbolic significance", "example": "Repeated imagery of masks to signify identity"} | |
| ] | |
| language_structures = [ | |
| {"type": "Glossary", "description": "Vocabulary Reference: List of terms and their definitions", "example": "Villain: The antagonist of the story"}, | |
| {"type": "Dialogue", "description": "Conversational Text: Characters' spoken words", "example": "We must act now! exclaimed the hero"}, | |
| {"type": "Narration", "description": "Storytelling Text: Text that tells the story", "example": "The city had never seen such despair"}, | |
| {"type": "Captions", "description": "Descriptive Text: Describes scene, setting, or action", "example": "New York, 2050. A city in turmoil"}, | |
| {"type": "Sound Effects", "description": "Auditory Text: Words that mimic sounds", "example": "BOOM! The spaceship landed"}, | |
| {"type": "Thought Bubbles", "description": "Internal Monologue Text: Characters' thoughts", "example": "I wonder if they know my secret"}, | |
| {"type": "Panel Transitions", "description": "Visual Storytelling Technique: Movement between scenes or ideas", "example": "Meanwhile, across the galaxy..."}, | |
| {"type": "Character Development", "description": "Evolution of characters throughout the story", "example": "From a timid schoolgirl to a fearless warrior"}, | |
| {"type": "Plot Twists", "description": "Unexpected changes in the story direction", "example": "The hero discovers their enemy is their sibling"}, | |
| {"type": "Backstory", "description": "Historical or background context of characters or setting", "example": "Once a celebrated hero, now a forgotten legend"} | |
| ] | |
| # Assuming 'parts_of_speech' and 'language_structures' are defined as above | |
| def display_elements(elements, title): | |
| st.markdown(f"## {title}") | |
| for element in elements: | |
| st.markdown(f""" | |
| - **Type**: {element['type']} | |
| - **Description**: {element['description']} | |
| - **Example**: {element['example']} | |
| """) | |
| # process sets: | |
| st.title("Graphic Novel Creation Toolkit") | |
| display_elements(parts_of_speech, "Parts of Speech for Dramatic Situations") | |
| display_elements(language_structures, "Language Structures for Dramatic Situations") | |
| # MoE Context Glossary | |
| roleplaying_glossary = { | |
| "👨👩👧👦 Top Graphic Novel Plot Themes": { | |
| "Epic Fantasy": [ | |
| "Ancient prophecies and mystical artifacts", | |
| "Epic battles between good and evil", | |
| "Complex world-building with diverse cultures", | |
| "Journey of a reluctant hero", | |
| "Alliance of unlikely companions", | |
| "Betrayal and redemption arcs", | |
| "Magic systems and mythical creatures", | |
| "Climactic confrontation with a dark lord" | |
| ], | |
| "Superhero Sagas": [ | |
| "Origin stories of heroes and villains", | |
| "Struggle with personal identity and responsibility", | |
| "Formation of superhero teams", | |
| "Epic battles to save the city/world", | |
| "Moral dilemmas and ethical questions", | |
| "Interdimensional threats and cosmic wars", | |
| "Evolution of powers and discovery of new abilities", | |
| "Legacy heroes and passing of the mantle" | |
| ], | |
| "Post-Apocalyptic Survival": [ | |
| "Survival in a world after a global catastrophe", | |
| "Rebuilding society from the ashes", | |
| "Conflict between surviving factions", | |
| "Quests for scarce resources", | |
| "Encounters with mutated creatures", | |
| "Moral ambiguity and survival ethics", | |
| "Exploration of human resilience", | |
| "Discovery of a safe haven or cure" | |
| ], | |
| "Science Fiction and Space Opera": [ | |
| "Exploration of distant galaxies", | |
| "Conflict between alien species", | |
| "Advanced technology and space travel", | |
| "Utopian and dystopian societies", | |
| "Time travel and alternate realities", | |
| "Artificial intelligence and robotics", | |
| "Quests for knowledge and discovery", | |
| "Rebellion against oppressive regimes" | |
| ], | |
| "Horror and Supernatural": [ | |
| "Haunted locations and ghost stories", | |
| "Battles against demonic forces", | |
| "Survival horror and psychological terror", | |
| "Folklore and urban legends", | |
| "Vampires, werewolves, and other monsters", | |
| "Occult practices and dark magic", | |
| "Apocalyptic and Lovecraftian themes", | |
| "Investigations into the unknown" | |
| ], | |
| "Romance and Relationship Dramas": [ | |
| "Complex romantic entanglements", | |
| "Struggles with identity and societal expectations", | |
| "Heartbreak, healing, and growth", | |
| "Forbidden love and star-crossed lovers", | |
| "Contemporary relationship dynamics", | |
| "Cultural and social differences", | |
| "Self-discovery and personal fulfillment", | |
| "Romantic comedies and tragedies" | |
| ] | |
| } | |
| } | |
| # Set initial page and app configs ------------------------------------------ | |
| # Prompts for App, for App Product, and App Product Code | |
| PromptPrefix = 'Create a graphic novel story with streamlit markdown outlines and tables with appropriate emojis for graphic novel rules defining the method steps of play. Use story structure architect rules using plan, structure and top three dramatic situations matching the theme for topic of ' | |
| PromptPrefix2 = 'Create a streamlit python user app with full code listing to create a UI implementing the plans, structure, situations and tables as python functions creating a game which operates like choose your own adventure graphic novel rules and creates a compelling fun story using streamlit to create user interface elements like emoji buttons, sliders, drop downs, and data interfaces like dataframes to show tables, session_state to track inventory, character advancement and experience, locations, file_uploader to allow the user to add images which are saved and referenced shown in gallery, camera_input to take character picture, on_change = function callbacks with continual running plots that change when you change data or click a button, randomness and dice rolls using emojis and st.markdown, st.expander for groupings and clusters of things, st.columns and other UI controls in streamlit as a game. Create inline data tables and list dictionaries for entities implemented as variables for the game rule entities and stats. Design it as a fun data driven game app and show full python code listing for this ruleset and thematic story plot line: ' | |
| PromptPrefix3 = 'Create a HTML5 aframe and javascript app using appropriate libraries to create a simulation and use more advanced libraries like aframe to render 3d scenes creating moving entities that stay within a bounding box but show text and animation in 3d for inventory, components and story entities. Show full code listing. Add a list of new random entities say 3 of a few different types to any list appropriately and use emojis to make things easier and fun to read. Use appropriate emojis in labels. Create the UI to implement storytelling in the style of a dungeon master, with features using three emoji appropriate text plot twists and recurring interesting funny fascinating and complex almost poetic named characters with genius traits and file IO, randomness, ten point choice lists, math distribution tradeoffs, witty humorous dilemnas with emoji , rewards, variables, reusable functions with parameters, and data driven app with python libraries and streamlit components for Javascript and HTML5. Use appropriate emojis for labels to summarize and list parts, function, conditions for topic:' | |
| # Function to display the entire glossary in a grid format with links | |
| def display_glossary_grid(roleplaying_glossary): | |
| search_urls = { | |
| "📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}", | |
| "🔍": lambda k: f"https://www.google.com/search?q={quote(k)}", | |
| "▶️": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}", | |
| "🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}", | |
| "🐦": lambda k: f"https://twitter.com/search?q={quote(k)}", | |
| "🎲": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(k)}", # this url plus query! | |
| "🃏": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix)}{quote(k)}", # this url plus query! | |
| "📚": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix2)}{quote(k)}", # this url plus query! | |
| "📚": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix3)}{quote(k)}", # this url plus query! | |
| } | |
| for category, details in roleplaying_glossary.items(): | |
| st.write(f"### {category}") | |
| cols = st.columns(len(details)) # Create dynamic columns based on the number of games | |
| for idx, (game, terms) in enumerate(details.items()): | |
| with cols[idx]: | |
| st.markdown(f"#### {game}") | |
| for term in terms: | |
| links_md = ' '.join([f"[{emoji}]({url(term)})" for emoji, url in search_urls.items()]) | |
| st.markdown(f"{term} {links_md}", unsafe_allow_html=True) | |
| def display_glossary_entity(k): | |
| search_urls = { | |
| "📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}", | |
| "🔍": lambda k: f"https://www.google.com/search?q={quote(k)}", | |
| "▶️": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}", | |
| "🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}", | |
| "🐦": lambda k: f"https://twitter.com/search?q={quote(k)}", | |
| "🎲": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(k)}", # this url plus query! | |
| "🃏": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix)}{quote(k)}", # this url plus query! | |
| "📚": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix2)}{quote(k)}", # this url plus query! | |
| "📚": lambda k: f"https://huggingface.co/spaces/awacke1/GraphicAINovel?q={quote(PromptPrefix3)}{quote(k)}", # this url plus query! | |
| } | |
| links_md = ' '.join([f"[{emoji}]({url(k)})" for emoji, url in search_urls.items()]) | |
| st.markdown(f"{k} {links_md}", unsafe_allow_html=True) | |
| # HTML5 based Speech Synthesis (Text to Speech in Browser) | |
| 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=300) | |
| # 9. Chat History File Sidebar | |
| 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' | |
| elif ext == '.wav': | |
| mime_type = 'audio/wav' | |
| 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 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 | |
| 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 | |
| def FileSidebar(): | |
| # ----------------------------------------------------- File Sidebar for Jump Gates ------------------------------------------ | |
| # Compose a file sidebar of markdown md files: | |
| all_files = glob.glob("*.md") | |
| all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10] # 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 Text"): | |
| 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) | |
| try: | |
| if st.button("🔍", key="filecontentssearch"): | |
| search_glossary(file_content_area) | |
| except: | |
| st.markdown('GPT is sleeping. Restart ETA 30 seconds.') | |
| if next_action=='md': | |
| st.markdown(file_contents) | |
| buttonlabel = '🔍Run' | |
| if st.button(key='RunWithLlamaandGPT', label = buttonlabel): | |
| user_prompt = file_contents | |
| try: | |
| search_glossary(file_contents) | |
| except: | |
| st.markdown('GPT is sleeping. Restart ETA 30 seconds.') | |
| if next_action=='search': | |
| file_content_area = st.text_area("File Contents:", file_contents, height=500) | |
| user_prompt = file_contents | |
| try: | |
| search_glossary(file_contents) | |
| except: | |
| st.markdown('GPT is sleeping. Restart ETA 30 seconds.') | |
| # ----------------------------------------------------- File Sidebar for Jump Gates ------------------------------------------ | |
| FileSidebar() | |
| # ---- Art Card Sidebar with Random Selection of image: | |
| def get_image_as_base64(url): | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| # Convert the image to base64 | |
| return base64.b64encode(response.content).decode("utf-8") | |
| else: | |
| return None | |
| def create_download_link(filename, base64_str): | |
| href = f'<a href="data:file/png;base64,{base64_str}" download="{filename}">Download Image</a>' | |
| return href | |
| # List of image URLs | |
| image_urls = [ | |
| "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/W1omJItftG3OkW9sj-Ckb.png", | |
| "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/Djx-k4WOxzlXEQPzllP3r.png" | |
| ] | |
| UseSidebarArtCard=False | |
| if UseSidebarArtCard: | |
| # Select a random URL from the list | |
| selected_image_url = random.choice(image_urls) | |
| # Get the base64 encoded string of the selected image | |
| st.write(selected_image_url) | |
| try: | |
| selected_image_base64 = get_image_as_base64(selected_image_url) | |
| if selected_image_base64 is not None: | |
| with st.sidebar: | |
| st.markdown("""### Graphic Novel AI""") | |
| # Display the image | |
| st.markdown(f"") | |
| # Create and display the download link | |
| download_link = create_download_link("downloaded_image.png", selected_image_base64) | |
| st.markdown(download_link, unsafe_allow_html=True) | |
| else: | |
| st.sidebar.write("Failed to load the image.") | |
| except: | |
| st.write('Sidebar Fail - Check your Images') | |
| # ---- Art Card Sidebar with random selection of image. | |
| # Ensure the directory for storing scores exists | |
| score_dir = "scores" | |
| os.makedirs(score_dir, exist_ok=True) | |
| # Function to generate a unique key for each button, including an emoji | |
| def generate_key(label, header, idx): | |
| return f"{header}_{label}_{idx}_key" | |
| # Function to increment and save score | |
| def update_score(key, increment=1): | |
| score_file = os.path.join(score_dir, f"{key}.json") | |
| if os.path.exists(score_file): | |
| with open(score_file, "r") as file: | |
| score_data = json.load(file) | |
| else: | |
| score_data = {"clicks": 0, "score": 0} | |
| score_data["clicks"] += 1 | |
| score_data["score"] += increment | |
| with open(score_file, "w") as file: | |
| json.dump(score_data, file) | |
| return score_data["score"] | |
| # Function to load score | |
| def load_score(key): | |
| score_file = os.path.join(score_dir, f"{key}.json") | |
| if os.path.exists(score_file): | |
| with open(score_file, "r") as file: | |
| score_data = json.load(file) | |
| return score_data["score"] | |
| return 0 | |
| def search_glossary(query): | |
| for category, terms in roleplaying_glossary.items(): | |
| if query.lower() in (term.lower() for term in terms): | |
| st.markdown(f"#### {category}") | |
| st.write(f"- {query}") | |
| all="" | |
| query2 = PromptPrefix + query | |
| response = chat_with_model(query2) | |
| query3 = PromptPrefix2 + query + ' for story outline of method steps: ' + response # Add prompt preface for coding task behavior | |
| response2 = chat_with_model(query3) | |
| query4 = PromptPrefix3 + query + ' using this streamlit python programspecification to define features. Create entities for each variable and generate UI with HTML5 and JS that matches the streamlit program: ' + response2 # Add prompt preface for coding task behavior | |
| response3 = chat_with_model(query4) | |
| all = query + ' ' + response + ' ' + response2 + ' ' + response3 | |
| filename = generate_filename(all, "md") | |
| create_file(filename, query, all, should_save) | |
| SpeechSynthesis(all) | |
| return all | |
| # Function to display the glossary in a structured format | |
| def display_glossary(glossary, area): | |
| if area in glossary: | |
| st.subheader(f"📘 Glossary for {area}") | |
| for game, terms in glossary[area].items(): | |
| st.markdown(f"### {game}") | |
| for idx, term in enumerate(terms, start=1): | |
| st.write(f"{idx}. {term}") | |
| game_emojis = { | |
| "Dungeons and Dragons": "🐉", | |
| "Call of Cthulhu": "🐙", | |
| "GURPS": "🎲", | |
| "Pathfinder": "🗺️", | |
| "Kindred of the East": "🌅", | |
| "Changeling": "🍃", | |
| } | |
| topic_emojis = { | |
| "Core Rulebooks": "📚", | |
| "Maps & Settings": "🗺️", | |
| "Game Mechanics & Tools": "⚙️", | |
| "Monsters & Adversaries": "👹", | |
| "Campaigns & Adventures": "📜", | |
| "Creatives & Assets": "🎨", | |
| "Game Master Resources": "🛠️", | |
| "Lore & Background": "📖", | |
| "Character Development": "🧍", | |
| "Homebrew Content": "🔧", | |
| "General Topics": "🌍", | |
| } | |
| # Adjusted display_buttons_with_scores function | |
| def display_buttons_with_scores(): | |
| for category, games in roleplaying_glossary.items(): | |
| category_emoji = topic_emojis.get(category, "🔍") # Default to search icon if no match | |
| st.markdown(f"## {category_emoji} {category}") | |
| for game, terms in games.items(): | |
| game_emoji = game_emojis.get(game, "🎮") # Default to generic game controller if no match | |
| for term in terms: | |
| key = f"{category}_{game}_{term}".replace(' ', '_').lower() | |
| score = load_score(key) | |
| if st.button(f"{game_emoji} {term} {score}", key=key): | |
| update_score(key) | |
| # Create a dynamic query incorporating emojis and formatting for clarity | |
| query_prefix = f"{category_emoji} {game_emoji} **{game} - {category}:**" | |
| # ---------------------------------------------------------------------------------------------- | |
| #query_body = f"Create a detailed outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements." | |
| query_body = f"Create a streamlit python app.py that produces a detailed markdown outline and emoji laden user interface with labels with the entity name and emojis in all labels with a set of streamlit UI components with drop down lists and dataframes and buttons with expander and sidebar for the app to run the data as default values mostly in text boxes. Feature a 3 point outline sith 3 subpoints each where each line has about six words describing this and also contain appropriate emoji for creating sumamry of all aspeccts of this topic. an outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements." | |
| response = search_glossary(query_prefix + query_body) | |
| def fetch_wikipedia_summary(keyword): | |
| # Placeholder function for fetching Wikipedia summaries | |
| # In a real app, you might use requests to fetch from the Wikipedia API | |
| return f"Summary for {keyword}. For more information, visit Wikipedia." | |
| def create_search_url_youtube(keyword): | |
| base_url = "https://www.youtube.com/results?search_query=" | |
| return base_url + keyword.replace(' ', '+') | |
| def create_search_url_bing(keyword): | |
| base_url = "https://www.bing.com/search?q=" | |
| return base_url + keyword.replace(' ', '+') | |
| def create_search_url_wikipedia(keyword): | |
| base_url = "https://www.wikipedia.org/search-redirect.php?family=wikipedia&language=en&search=" | |
| return base_url + keyword.replace(' ', '+') | |
| def create_search_url_google(keyword): | |
| base_url = "https://www.google.com/search?q=" | |
| return base_url + keyword.replace(' ', '+') | |
| def create_search_url_ai(keyword): | |
| base_url = "https://huggingface.co/spaces/awacke1/GraphicAINovel?q=" | |
| return base_url + keyword.replace(' ', '+') | |
| def display_videos_and_links(): | |
| video_files = [f for f in os.listdir('.') if f.endswith('.mp4')] | |
| if not video_files: | |
| st.write("No MP4 videos found in the current directory.") | |
| return | |
| video_files_sorted = sorted(video_files, key=lambda x: len(x.split('.')[0])) | |
| cols = st.columns(2) # Define 2 columns outside the loop | |
| col_index = 0 # Initialize column index | |
| for video_file in video_files_sorted: | |
| with cols[col_index % 2]: # Use modulo 2 to alternate between the first and second column | |
| # Embedding video with autoplay and loop using HTML | |
| #video_html = ("""<video width="100%" loop autoplay> <source src="{video_file}" type="video/mp4">Your browser does not support the video tag.</video>""") | |
| #st.markdown(video_html, unsafe_allow_html=True) | |
| k = video_file.split('.')[0] # Assumes keyword is the file name without extension | |
| st.video(video_file, format='video/mp4', start_time=0) | |
| display_glossary_entity(k) | |
| col_index += 1 # Increment column index to place the next video in the next column | |
| def display_videos_and_links_old(): | |
| video_files = [f for f in os.listdir('.') if f.endswith('.mp4')] | |
| if not video_files: | |
| st.write("No MP4 videos found in the current directory.") | |
| return | |
| video_files_sorted = sorted(video_files, key=lambda x: len(x.split('.')[0])) | |
| grid_sizes = [len(f.split('.')[0]) for f in video_files_sorted] | |
| col_sizes = ['small' if size <= 4 else 'medium' if size <= 8 else 'large' for size in grid_sizes] | |
| # Create a map for number of columns to use for each size | |
| num_columns_map = {"small": 4, "medium": 3, "large": 2} | |
| current_grid_size = 0 | |
| for video_file, col_size in zip(video_files_sorted, col_sizes): | |
| if current_grid_size != num_columns_map[col_size]: | |
| cols = st.columns(num_columns_map[col_size]) | |
| current_grid_size = num_columns_map[col_size] | |
| col_index = 0 | |
| with cols[col_index % current_grid_size]: | |
| st.video(video_file, format='video/mp4', start_time=0) | |
| k = video_file.split('.')[0] # Assumes keyword is the file name without extension | |
| display_glossary_entity(k) | |
| def display_images_and_wikipedia_summaries(): | |
| image_files = [f for f in os.listdir('.') if f.endswith('.png')] | |
| if not image_files: | |
| st.write("No PNG images found in the current directory.") | |
| return | |
| # Sort image_files based on the length of the keyword to create a visually consistent grid | |
| image_files_sorted = sorted(image_files, key=lambda x: len(x.split('.')[0])) | |
| # Calculate the grid size based on the sorted keywords | |
| grid_sizes = [len(f.split('.')[0]) for f in image_files_sorted] | |
| # Dynamically adjust column size based on keyword length | |
| col_sizes = ['small' if size <= 4 else 'medium' if size <= 8 else 'large' for size in grid_sizes] | |
| # Create a map for number of columns to use for each size | |
| num_columns_map = {"small": 4, "medium": 3, "large": 2} | |
| current_grid_size = 0 | |
| for image_file, col_size in zip(image_files_sorted, col_sizes): | |
| if current_grid_size != num_columns_map[col_size]: | |
| cols = st.columns(num_columns_map[col_size]) | |
| current_grid_size = num_columns_map[col_size] | |
| col_index = 0 | |
| with cols[col_index % current_grid_size]: | |
| image = Image.open(image_file) | |
| st.image(image, caption=image_file, use_column_width=True) | |
| # Display search links | |
| k = image_file.split('.')[0] # Assumes keyword is the file name without extension | |
| display_glossary_entity(k) | |
| #col_index += 1 | |
| def get_all_query_params(key): | |
| return st.query_params().get(key, []) | |
| def clear_query_params(): | |
| st.query_params() | |
| # Function to display content or image based on a query | |
| def display_content_or_image(query): | |
| # Check if the query matches any glossary term | |
| for category, terms in transhuman_glossary.items(): | |
| for term in terms: | |
| if query.lower() in term.lower(): | |
| st.subheader(f"Found in {category}:") | |
| st.write(term) | |
| return True # Return after finding and displaying the first match | |
| # Check for an image match in a predefined directory (adjust path as needed) | |
| image_dir = "images" # Example directory where images are stored | |
| image_path = f"{image_dir}/{query}.png" # Construct image path with query | |
| if os.path.exists(image_path): | |
| st.image(image_path, caption=f"Image for {query}") | |
| return True | |
| # If no content or image is found | |
| st.warning("No matching content or image found.") | |
| return False | |
| # 1. Constants and Top Level UI Variables | |
| # My Inference API Copy | |
| API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' # Dr Llama | |
| # Meta's Original - Chat HF Free Version: | |
| #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 discharge planning along with guidelines and patient education. List entities, features and relationships to CCDA and FHIR objects in boldface." | |
| should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.") | |
| # 3. Stream Llama Response | |
| # @st.cache_resource | |
| def StreamLLMChatResponse(prompt): | |
| try: | |
| endpoint_url = API_URL | |
| hf_token = API_KEY | |
| #st.write('Running client ' + endpoint_url) | |
| 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 == "_")[:255] # 255 is linux max, 260 is windows max | |
| 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} | |
| st.write('STT transcript ' + OPENAI_API_URL) | |
| 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)] | |
| 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 | |
| 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 | |
| def chat_with_model(prompt, document_section='', model_choice='gpt-3.5-turbo'): # gpt-4-0125-preview gpt-3.5-turbo | |
| #def chat_with_model(prompt, document_section='', model_choice='gpt-4-0125-preview'): # gpt-4-0125-preview 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=model_choice, 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 | |
| def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'): # gpt-4-0125-preview gpt-3.5-turbo | |
| #def chat_with_file_contents(prompt, file_content, model_choice='gpt-4-0125-preview'): # gpt-4-0125-preview 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 | |
| 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 | |
| def vector_store(text_chunks): | |
| embeddings = OpenAIEmbeddings(openai_api_key=key) | |
| return FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
| # Memory and Retrieval chains | |
| 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 | |
| # 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10 | |
| API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud' | |
| API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en" | |
| MODEL2 = "openai/whisper-small.en" | |
| MODEL2_URL = "https://huggingface.co/openai/whisper-small.en" | |
| HF_KEY = st.secrets['HF_KEY'] | |
| headers = { | |
| "Authorization": f"Bearer {HF_KEY}", | |
| "Content-Type": "audio/wav" | |
| } | |
| 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("Speech to Text") | |
| #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'] | |
| st.write(transcript) | |
| except: | |
| transcript='' | |
| st.write(transcript) | |
| # Whisper to GPT: New!! --------------------------------------------------------------------- | |
| st.write('Reasoning with your inputs with GPT..') | |
| response = chat_with_model(transcript) | |
| st.write('Response:') | |
| st.write(response) | |
| filename = generate_filename(response, "txt") | |
| create_file(filename, transcript, response, should_save) | |
| # Whisper to GPT: New!! --------------------------------------------------------------------- | |
| # Whisper to Llama: | |
| response = StreamLLMChatResponse(transcript) | |
| filename_txt = generate_filename(transcript, "md") | |
| create_file(filename_txt, transcript, response, should_save) | |
| filename_wav = filename_txt.replace('.txt', '.wav') | |
| import shutil | |
| try: | |
| if os.path.exists(filename): | |
| shutil.copyfile(filename, filename_wav) | |
| except: | |
| st.write('.') | |
| if os.path.exists(filename): | |
| os.remove(filename) | |
| # 17. Main | |
| def main(): | |
| prompt = PromptPrefix2 | |
| with st.expander("Prompts 📚", expanded=False): | |
| example_input = st.text_input("Enter your prompt text for Llama:", value=prompt, help="Enter text to get a response from DromeLlama.") | |
| if st.button("Run Prompt With Llama model", help="Click to run the prompt."): | |
| try: | |
| response=StreamLLMChatResponse(example_input) | |
| create_file(filename, example_input, response, should_save) | |
| except: | |
| st.write('Llama model is asleep. Starting now on A10 GPU. Please wait one minute then retry. KEDA triggered.') | |
| openai.api_key = os.getenv('OPENAI_API_KEY') | |
| if openai.api_key == None: openai.api_key = st.secrets['OPENAI_API_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) | |
| # Function to encode file to base64 | |
| def get_base64_encoded_file(file_path): | |
| with open(file_path, "rb") as file: | |
| return base64.b64encode(file.read()).decode() | |
| # Function to create a download link | |
| def get_audio_download_link(file_path): | |
| base64_file = get_base64_encoded_file(file_path) | |
| return f'<a href="data:file/wav;base64,{base64_file}" download="{os.path.basename(file_path)}">⬇️ Download Audio</a>' | |
| # Compose a file sidebar of past encounters | |
| all_files = glob.glob("*.wav") | |
| all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10] # 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 | |
| filekey = 'delall' | |
| if st.sidebar.button("🗑 Delete All Audio", key=filekey): | |
| for file in all_files: | |
| os.remove(file) | |
| st.experimental_rerun() | |
| for file in all_files: | |
| col1, col2 = st.sidebar.columns([6, 1]) # adjust the ratio as needed | |
| with col1: | |
| st.markdown(file) | |
| if st.button("🎵", key="play_" + file): # play emoji button | |
| audio_file = open(file, 'rb') | |
| audio_bytes = audio_file.read() | |
| st.audio(audio_bytes, format='audio/wav') | |
| #st.markdown(get_audio_download_link(file), unsafe_allow_html=True) | |
| #st.text_input(label="", value=file) | |
| with col2: | |
| if st.button("🗑", key="delete_" + file): | |
| os.remove(file) | |
| st.experimental_rerun() | |
| GiveFeedback=False | |
| if GiveFeedback: | |
| with st.expander("Give your feedback 👍", expanded=False): | |
| 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) | |
| try: | |
| query_params = st.query_params | |
| #query = (query_params.get('q') or query_params.get('query') or [''])[0] | |
| query = (query_params.get('q') or query_params.get('query') or ['']) | |
| #st.markdown('# Running query: ' + query) | |
| if query: search_glossary(query) | |
| except: | |
| st.markdown(' ') | |
| # Display the glossary grid | |
| st.markdown("### 🎲🗺️ Graphic Novel Gallery") | |
| display_videos_and_links() # Video Jump Grid | |
| display_images_and_wikipedia_summaries() # Image Jump Grid | |
| display_glossary_grid(roleplaying_glossary) # Word Glossary Jump Grid | |
| display_buttons_with_scores() # Feedback Jump Grid | |
| if 'action' in st.query_params: | |
| action = st.query_params()['action'][0] # Get the first (or only) 'action' parameter | |
| if action == 'show_message': | |
| st.success("Showing a message because 'action=show_message' was found in the URL.") | |
| elif action == 'clear': | |
| clear_query_params() | |
| st.experimental_rerun() | |
| # Handling repeated keys | |
| if 'multi' in st.query_params: | |
| multi_values = get_all_query_params('multi') | |
| st.write("Values for 'multi':", multi_values) | |
| # Manual entry for demonstration | |
| st.write("Enter query parameters in the URL like this: ?action=show_message&multi=1&multi=2") | |
| if 'query' in st.query_params: | |
| query = st.query_params['query'][0] # Get the query parameter | |
| # Display content or image based on the query | |
| display_content_or_image(query) | |
| # Add a clear query parameters button for convenience | |
| if st.button("Clear Query Parameters", key='ClearQueryParams'): | |
| # This will clear the browser URL's query parameters | |
| st.experimental_set_query_params | |
| st.experimental_rerun() | |
| # 18. Run AI Pipeline | |
| if __name__ == "__main__": | |
| whisper_main() | |
| main() |