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| import streamlit as st | |
| import os | |
| import json | |
| import random | |
| # 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 | |
| import streamlit.components.v1 as components # Import Streamlit Components for HTML5 | |
| 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 | |
| from PIL import Image | |
| from urllib.parse import quote # Ensure this import is included | |
| # Set page configuration with a title and favicon | |
| 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" | |
| } | |
| ) | |
| experimentalSubProgram=""" | |
| import streamlit as st | |
| import random | |
| import numpy as np | |
| """ | |
| 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) | |
| #return result | |
| 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 usable choose your own adventure graphic novel rules and story using streamlit, session_state, file_uploader, camera_input, on_change = funcction callbacks, randomness and dice rolls using emojis and st.markdown, st.expander, st.columns and other UI controls in streamlit as a game interface and create inline data tables for entities implemented as variables with python list dictionaries for the game rule entities and stats. Design it as a fun data driven app and show full python code listing for this ruleset and thematic story plot line: ' | |
| PromptPrefix3 = 'Create a HTML5 aframe and javascript app. Show full code listing. Create a UI implementing storytelling, features using use three emoji appropriate text detailed plot twists and recurring interesting 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:' | |
| 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. | |
| ''') | |
| 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" | |
| ] | |
| } | |
| } | |
| # 9. Sidebar with UI controls to review and re-run prompts and continue responses | |
| 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 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) | |
| if next_action=='md': | |
| st.markdown(file_contents) | |
| buttonlabel = '🔍Run' | |
| if st.button(key='RunWithLlamaandGPT', label = buttonlabel): | |
| user_prompt = file_contents | |
| # gpt | |
| try: | |
| #st.write('🔍Running..') | |
| response2 = chat_with_model(user_prompt, file_contents, model_choice) | |
| filename2 = generate_filename(file_contents, choice) | |
| create_file(filename2, user_prompt, response, should_save) | |
| all=response2 | |
| except: | |
| st.markdown('GPT is sleeping. Restart ETA 30 seconds.') | |
| SpeechSynthesis(all) | |
| if next_action=='search': | |
| file_content_area = st.text_area("File Contents:", file_contents, height=500) | |
| #st.write('🔍Running..') | |
| user_prompt = file_contents | |
| # gpt | |
| try: | |
| #st.write('🔍Running with GPT.') | |
| response2 = chat_with_model(user_prompt, file_contents, model_choice) | |
| filename2 = generate_filename(file_contents, choice) | |
| create_file(filename2, user_prompt, response, should_save) | |
| all=response2 | |
| except: | |
| st.markdown('GPT is sleeping. Restart ETA 30 seconds.') | |
| SpeechSynthesis(all) | |
| # ----------------------------------------------------- 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/KGvIFUpU1N-X2tX8hMRva.png", | |
| "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/KvmVfcpAbowZIxcViGMmd.png", | |
| "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/_ANl0q3ZGDa9CxQqpUmYP.png", | |
| "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/SIGnnyVv7eLu8NnrtxdQP.png", | |
| "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/5u06M9ue8FXK6Dsi2SF-I.png", | |
| "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/gTmhCUdpPaIJcmNXceDi4.png" | |
| ] | |
| # Select a random URL from the list | |
| selected_image_url = random.choice(image_urls) | |
| # Get the base64 encoded string of the selected image | |
| 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.") | |
| # ---- 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 # Add prompt preface for method step task behavior | |
| # st.write('## ' + query2) | |
| #st.write('## 🔍 Running with GPT.') # ------------------------------------------------------------------------------------------------- | |
| response = chat_with_model(query2) | |
| filename = generate_filename(query2 + ' --- ' + response, "md") | |
| create_file(filename, query, response, should_save) | |
| query3 = PromptPrefix2 + query + ' for story outline of method steps: ' + response # Add prompt preface for coding task behavior | |
| # st.write('## ' + query3) | |
| st.write('## 🔍 Coding with GPT.') # ------------------------------------------------------------------------------------------------- | |
| response2 = chat_with_model(query3) | |
| filename_txt = generate_filename(query + ' --- ' + response2, "py") | |
| create_file(filename_txt, query, response2, should_save) | |
| all = '# Query: ' + query + '# Response: ' + response + '# Response2: ' + response2 | |
| filename_txt2 = generate_filename(query + ' --- ' + all, "md") | |
| create_file(filename_txt2, 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}") | |
| # 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://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) | |
| 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 | |
| # Sort video_files based on the length of the keyword to create a visually consistent grid | |
| video_files_sorted = sorted(video_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 video_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 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) | |
| # Display Wikipedia and Google search links | |
| keyword = video_file.split('.')[0] # Assumes keyword is the file name without extension | |
| wikipedia_url = create_search_url_wikipedia(keyword) | |
| google_url = create_search_url_google(keyword) | |
| youtube_url = create_search_url_youtube(keyword) | |
| bing_url = create_search_url_bing(keyword) | |
| ai_url = create_search_url_ai(keyword) | |
| ai_url2 = create_search_url_ai(keyword + ' ' + PromptPrefix) | |
| ai_url3 = create_search_url_ai(keyword + ' ' + PromptPrefix2) | |
| links_md = f""" | |
| [Wikipedia]({wikipedia_url}) | | |
| [Google]({google_url}) | | |
| [YouTube]({youtube_url}) | | |
| [Bing]({bing_url}) | | |
| [AI]({ai_url})| | |
| [AI Novel]({ai_url2} )| | |
| [AI Novel App]({ai_url3}) | |
| """ | |
| st.markdown(links_md) | |
| col_index += 1 | |
| 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 Wikipedia and Google search links | |
| keyword = image_file.split('.')[0] # Assumes keyword is the file name without extension | |
| wikipedia_url = create_search_url_wikipedia(keyword) | |
| google_url = create_search_url_google(keyword) | |
| youtube_url = create_search_url_youtube(keyword) | |
| bing_url = create_search_url_bing(keyword) | |
| ai_url = create_search_url_ai(keyword) | |
| ai_url2 = create_search_url_ai(keyword + ' ' + PromptPrefix) | |
| ai_url3 = create_search_url_ai(keyword + ' ' + PromptPrefix2) | |
| links_md = f""" | |
| [Wikipedia]({wikipedia_url}) | | |
| [Google]({google_url}) | | |
| [YouTube]({youtube_url}) | | |
| [Bing]({bing_url}) | | |
| [AI]({ai_url})| | |
| [AI Novel]({ai_url2} )| | |
| [AI Novel App]({ai_url3}) | |
| """ | |
| st.markdown(links_md) | |
| 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 | |
| # ------------------------------------ | |
| 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 | |
| # 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.") | |
| # 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("Wise 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("Top Ten Funniest Clean Jokes 💉"): | |
| StreamLLMChatResponse(descriptions["Top Ten Funniest Clean Jokes 💉"]) | |
| if col5.button("Minnesota Humor ❄️"): | |
| StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"]) | |
| if col6.button("Origins of Medical Science True Stories"): | |
| StreamLLMChatResponse(descriptions["Origins of Medical Science True Stories"]) | |
| col7 = st.columns(1, gap="small") | |
| if col7[0].button("Top Ten Best Write a streamlit python program prompts to build AI programs. 🎙️"): | |
| StreamLLMChatResponse(descriptions["Top Ten Best Write a streamlit python program prompts to build AI programs. 🎙️"]) | |
| # 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 | |
| #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} | |
| 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'): | |
| 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 = [] | |
| st.write('LLM stream ' + 'gpt-3.5-turbo') | |
| 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 | |
| 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 | |
| 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 | |
| # 13. Provide way of saving all and deleting all to give way of reviewing output and saving locally before clearing it | |
| 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 | |
| # 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" | |
| 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') | |
| HF_KEY = st.secrets['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("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) | |
| #st.experimental_rerun() | |
| #except: | |
| # st.write('Starting Whisper Model on GPU. Please retry in 30 seconds.') | |
| # Sample function to demonstrate a response, replace with your own logic | |
| def StreamMedChatResponse(topic): | |
| st.write(f"Showing resources or questions related to: {topic}") | |
| # 17. Main | |
| def main(): | |
| 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() | |
| # add_medical_exam_buttons() | |
| 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() | |
| # Feedback | |
| # Step: Give User a Way to Upvote or Downvote | |
| 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) | |
| # Relocated! Hope you like your new space - enjoy! | |
| # Display instructions and handle query parameters | |
| #st.markdown("## Glossary Lookup\nEnter a term in the URL query, like `?q=Nanotechnology` or `?query=Martian Syndicate`.") | |
| 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() | |
| display_images_and_wikipedia_summaries() | |
| display_glossary_grid(roleplaying_glossary) | |
| st.markdown("## Explore the vast universe of Graphic Novels including card and board games with fascinating readable content and semantic rules where language is fun!.🌠") | |
| display_buttons_with_scores() | |
| # Assuming the transhuman_glossary and other setup code remains the same | |
| #st.write("Current Query Parameters:", st.query_params) | |
| #st.markdown("### Query Parameters - These Deep Link Map to Remixable Methods, Navigate or Trigger Functionalities") | |
| # Example: Using query parameters to navigate or trigger functionalities | |
| 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() |