import streamlit as st import os import json 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="๐ŸŒŒ๐Ÿš€ Mixable AI - Voice Search", page_icon="๐ŸŒ ", layout="wide", initial_sidebar_state="expanded", menu_items={ 'Get Help': 'https://huggingface.co/awacke1', 'Report a bug': "https://huggingface.co/spaces/awacke1/WebDataDownload", 'About': "# Midjourney: https://discord.com/channels/@me/997514686608191558" } ) # 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 roleplaying_glossary = { "๐ŸŽฒ Core Rulebooks": { "Dungeons and Dragons": ["Player's Handbook", "Dungeon Master's Guide", "Monster Manual"], "Call of Cthulhu": ["Keeper's Rulebook", "Investigator Handbook", "Monster Manual"], "GURPS": ["Basic Set: Characters", "Basic Set: Campaigns", "GURPS Lite"], "Pathfinder": ["Core Rulebook", "Bestiary", "GameMastery Guide"], "Kindred of the East": ["Core Rulebook", "Companion Guide", "Kindred Secrets"], "Changeling": ["The Dreaming Core", "Players Guide", "Storytellers Handbook"], }, "๐Ÿ—บ๏ธ Maps & Settings": { "Dungeons and Dragons": ["The Forgotten Realms", "Eberron", "Ravenloft"], "Gamma World": ["Gamma Terra", "The Wastelands"], "Car Wars": ["Autoduel America", "The Arenas"], "Top Secret": ["World of Espionage", "Mission Modules"], "Kindred of the East": ["The Eastern Realms", "Cities of Darkness", "Ancestral Voices"], "Changeling": ["Freeholds & Hidden Glens", "Isles of the Mighty", "Kingdoms of Will"], }, "๐Ÿ› ๏ธ Game Mechanics & Tools": { "GURPS": ["GURPS Character Sheet", "GURPS Calculator", "Modular System for Custom Campaigns"], "Pathfinder": ["Pathfinder Character Sheet", "Pathfinder Spell Database", "Adventure Path Modules"], "Call of Cthulhu": ["Sanity System", "Investigator's Companion", "Mythos Tomes"], "Kindred of the East": ["Karmic Wheel", "Yin-Yang Balance", "Chi Cultivation"], "Changeling": ["Glamour Harnessing", "Arts and Realms", "Cantrip Casting"], }, "๐ŸงŸ Monsters & Adversaries": { "Dungeons and Dragons": ["Beholder", "Dragon", "Lich"], "Call of Cthulhu": ["Great Old Ones", "Deep Ones", "Shoggoth"], "Pathfinder": ["Goblins", "Dragons", "Vampires"], "Kindred of the East": ["Kuei-jin Ancestors", "Demon Hordes", "Yama Kings"], "Changeling": ["Thallain Nightmares", "Autumn People", "Banality's Minions"], }, "๐Ÿ“œ Campaigns & Adventures": { "Dungeons and Dragons": ["Curse of Strahd", "Tomb of Annihilation", "Waterdeep: Dragon Heist"], "Gamma World": ["Legion of Gold", "Famine in Far-Go"], "Call of Cthulhu": ["The Masks of Nyarlathotep", "The Haunting"], "GURPS": ["Banestorm", "Infinite Worlds"], "Kindred of the East": ["Blood and Silk", "Dharma Book: Bone Flowers"], "Changeling": ["Dreams and Nightmares", "War in Concordia"], }, "๐ŸŽจ Creatives & Assets": { "General RPG": ["Character Artwork", "Map Generators", "Token Creators"], "Digital Tools": ["Roll20 Assets", "Fantasy Grounds Modules", "Tabletop Simulator Packs"], }, "๐Ÿ”ง Game Master Resources": { "General Tips": ["Campaign Planning", "NPC Creation", "World-Building"], "Adventure Hooks": ["Mystery", "Exploration", "Conflict"], "Puzzle & Trap Ideas": ["Logic Puzzles", "Physical Traps", "Social Encounters"], }, "๐Ÿ“š Lore & Background": { "Dungeons and Dragons": ["The Pantheon of Deities", "Historic Timelines", "Cultural Customs"], "Pathfinder": ["Golarion", "Inner Sea Gods", "The Starstone"], "Call of Cthulhu": ["The Cthulhu Mythos", "Historic Campaign Settings", "Cosmic Horrors"], "Kindred of the East": ["Eastern Mythology", "The Five Courts", "Undead Politics"], "Changeling": ["Faerie Lore", "The Dreaming", "Changeling Society"], }, "๐ŸŽ–๏ธ Character Development": { "Skill Systems": ["Class-Based", "Skill-Based", "Level-Less Progression"], "Alignment & Morality": ["Law vs. Chaos", "Good vs. Evil", "Neutrality"], "Character Backstories": ["Origin Stories", "Character Flaws", "Personal Goals"], }, "โš™๏ธ Homebrew Content": { "Rule Modifications": ["House Rules", "Alternate Magic Systems", "Custom Races & Classes"], "Homebrew Campaigns": ["World Building Guides", "Custom Adventures", "NPC Galleries"], "Creative Tools": ["Homebrewery", "D&D Beyond Homebrew", "GMBinder"], }, "๐ŸŒ General Topics": { "Fantasy Cultures": ["High Elven Traditions", "Dwarven Kingdoms", "Nomadic Tribes"], "World Cultures": ["Ancient Egypt Mysteries", "Feudal Japan Lore", "Viking Raiders"], "Magic": ["Arcane Arts", "Divine Blessings", "Ritual Craft"], "Fantasy Races": ["Elves and Dwarves", "Orcs and Goblins", "Fae and Spirits"], "Creatures of Myth and Legend": ["Dragons and Wyverns", "Ghosts and Ghouls", "Titans and Giants"], "Dress and Costume": ["Medieval Attire", "Warrior's Armor", "Ceremonial Robes"], "Arms, Armor, and Armies": ["Sword and Shield", "Siege Warfare", "Cavalry Tactics"], "Anatomy of a Castle": ["Keep and Bailey", "Castle Defenses", "Living Quarters"], }, } # Function to search within the roleplaying glossary and display results def search_glossary(query, roleplaying_glossary): found = False for category, details in roleplaying_glossary.items(): for game, terms in details.items(): if query.lower() in ' '.join(terms).lower(): if not found: st.markdown(f"#### Results for '{query}'") found = True st.markdown(f"**{category} -> {game}:**") for term in terms: if query.lower() in term.lower(): st.write(f"- {term}") if not found: st.write("No results found.") st.write('## ' + query) all="" st.write('## ๐Ÿ” Running with GPT.') # ------------------------------------------------------------------------------------------------- response = chat_with_model(query) #st.write(response) filename = generate_filename(query + ' --- ' + response, "md") create_file(filename, query, response, should_save) st.write('## ๐Ÿ” Running with Llama.') # ------------------------------------------------------------------------------------------------- response2 = StreamLLMChatResponse(query) #st.write(response2) filename_txt = generate_filename(query + ' --- ' + response2, "md") 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"๐Ÿ“˜ 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 = { "๐Ÿ“–": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}", "๐Ÿ”": lambda k: f"https://www.google.com/search?q={quote(k)}", "โ–ถ๏ธ": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}", "๐Ÿ”Ž": lambda k: f"https://www.bing.com/search?q={quote(k)}", "๐ŸŽฒ": lambda k: f"https://huggingface.co/spaces/awacke1/MixableRolePlayAI?q={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_buttons_with_scores_old(): # Assuming generate_key, load_score, update_score, and search_glossary are defined elsewhere for category, games in roleplaying_glossary.items(): st.markdown(f"## {category}") for game, terms in games.items(): for term in terms: # Construct a unique key for each term. This replaces the previous use of .index() # The key now includes both the category and the game for uniqueness key = f"{category}_{game}_{term}".replace(' ', '_').lower() score = load_score(key) # Load the score based on the generated key # Display a button for each term with its score if st.button(f"{term} {score}๐Ÿš€", key=key): # If button is pressed, update score and possibly rerun the search or action update_score(key) # Adjust the search_glossary call as needed to match the function's signature # -------------------------- Call LLMs with the button content: response = search_glossary('Create a three-level markdown outline with 3 subpoints each where each line defines and writes out the descriptions with appropriate emojis for the glossary term: ' + term, roleplaying_glossary) #st.experimental_rerun() game_emojis = { "Dungeons and Dragons": "๐Ÿ‰", "Call of Cthulhu": "๐Ÿ™", "GURPS": "๐ŸŽฒ", "Pathfinder": "๐Ÿ—บ๏ธ", "Kindred of the East": "๐ŸŒ…", "Changeling": "๐Ÿƒ", } topic_emojis = { "Core Rulebooks": "๐Ÿ“š", "Maps & Settings": "๐Ÿ—บ๏ธ", "Game Mechanics & Tools": "โš™๏ธ", "Monsters & Adversaries": "๐Ÿ‘น", "Campaigns & Adventures": "๐Ÿ“œ", "Creatives & Assets": "๐ŸŽจ", "Game Master Resources": "๐Ÿ› ๏ธ", "Lore & Background": "๐Ÿ“–", "Character Development": "๐Ÿง", "Homebrew Content": "๐Ÿ”ง", "General Topics": "๐ŸŒ", } # Adjusted display_buttons_with_scores function def display_buttons_with_scores(): for category, games in roleplaying_glossary.items(): category_emoji = topic_emojis.get(category, "๐Ÿ”") # 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, "๐ŸŽฎ") # 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." response = search_glossary(query_prefix + query_body, roleplaying_glossary) 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 display_images_and_wikipedia_summaries(): st.title('Gallery with Related Stories') 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 for image_file in image_files: image = Image.open(image_file) st.image(image, caption=image_file, use_column_width=True) keyword = image_file.split('.')[0] # Assumes keyword is the file name without extension # Display Wikipedia and Google search links 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) links_md = f""" [Wikipedia]({wikipedia_url}) | [Google]({google_url}) | [YouTube]({youtube_url}) | [Bing]({bing_url}) """ st.markdown(links_md) 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 # Imports import base64 import glob import json import math import openai import os import pytz import re import requests import streamlit as st import textract import time import zipfile import huggingface_hub import dotenv from audio_recorder_streamlit import audio_recorder from bs4 import BeautifulSoup from collections import deque from datetime import datetime from dotenv import load_dotenv from huggingface_hub import InferenceClient from io import BytesIO from langchain.chat_models import ChatOpenAI from langchain.chains import ConversationalRetrievalChain from langchain.embeddings import OpenAIEmbeddings from langchain.memory import ConversationBufferMemory from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import FAISS from openai import ChatCompletion from PyPDF2 import PdfReader from templates import bot_template, css, user_template from xml.etree import ElementTree as ET import streamlit.components.v1 as components # Import Streamlit Components for HTML5 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("๐Ÿ˜ข The dataset is empty.") else: st.write(""" ๐Ÿ” Use the search box to filter questions or use the grid to scroll through the dataset. """) # ๐Ÿ‘ฉโ€๐Ÿ”ฌ Search Box search_term = st.text_input("Search for a specific question:", "") # ๐ŸŽ› 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 # ๐Ÿ“Š Display Data start_idx = (page_number - 1) * records_per_page end_idx = start_idx + records_per_page # ๐Ÿงช 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) # ๐ŸŒ 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"๐Ÿ˜Š Total Records: {num_records} | ๐Ÿ“„ 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("๐Ÿ’พ 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 ๐Ÿคฃ", expanded=True): # Tip about the Dromedary family st.markdown("๐Ÿ”ฌ **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 ๐Ÿ˜‚": "Write ten random adult limericks based on quotes that are tweet length and make you laugh ๐ŸŽญ", "Wise Quotes ๐Ÿง™": "Generate ten wise quotes that are tweet length ๐Ÿฆ‰", "Funny Rhymes ๐ŸŽค": "Create ten funny rhymes that are tweet length ๐ŸŽถ", "Medical Jokes ๐Ÿ’‰": "Create ten medical jokes that are tweet length ๐Ÿฅ", "Minnesota Humor โ„๏ธ": "Create ten jokes about Minnesota that are tweet length ๐ŸŒจ๏ธ", "Top Funny Stories ๐Ÿ“–": "Create ten funny stories that are tweet length ๐Ÿ“š", "More Funny Rhymes ๐ŸŽ™๏ธ": "Create ten more funny rhymes that are tweet length ๐ŸŽต" } # Create columns col1, col2, col3 = st.columns([1, 1, 1], gap="small") # Add buttons to columns if col1.button("Wise Limericks ๐Ÿ˜‚"): StreamLLMChatResponse(descriptions["Generate Limericks ๐Ÿ˜‚"]) if col2.button("Wise Quotes ๐Ÿง™"): StreamLLMChatResponse(descriptions["Wise Quotes ๐Ÿง™"]) #if col3.button("Funny Rhymes ๐ŸŽค"): # StreamLLMChatResponse(descriptions["Funny Rhymes ๐ŸŽค"]) col4, col5, col6 = st.columns([1, 1, 1], gap="small") if col4.button("Top Ten Funniest Clean Jokes ๐Ÿ’‰"): StreamLLMChatResponse(descriptions["Top Ten Funniest Clean Jokes ๐Ÿ’‰"]) if col5.button("Minnesota Humor โ„๏ธ"): StreamLLMChatResponse(descriptions["Minnesota Humor โ„๏ธ"]) 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. ๐ŸŽ™๏ธ"): StreamLLMChatResponse(descriptions["Top Ten Best Write a streamlit python program prompts to build AI programs. ๐ŸŽ™๏ธ"]) def SpeechSynthesis(result): documentHTML5=''' Read It Aloud

๐Ÿ”Š Read It Aloud


''' components.html(documentHTML5, width=1280, height=300) #return result # 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|>", ""], ) 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)] # 9. Sidebar with UI controls to review and re-run prompts and continue responses @st.cache_resource def get_table_download_link(file_path): with open(file_path, 'r') as file: data = file.read() b64 = base64.b64encode(data.encode()).decode() file_name = os.path.basename(file_path) ext = os.path.splitext(file_name)[1] # get the file extension if ext == '.txt': mime_type = 'text/plain' elif ext == '.py': mime_type = 'text/plain' elif ext == '.xlsx': mime_type = 'text/plain' elif ext == '.csv': mime_type = 'text/plain' elif ext == '.htm': mime_type = 'text/html' elif ext == '.md': mime_type = 'text/markdown' elif ext == '.wav': mime_type = 'audio/wav' else: mime_type = 'application/octet-stream' # general binary data type href = f'{file_name}' return href def CompressXML(xml_text): root = ET.fromstring(xml_text) for elem in list(root.iter()): if isinstance(elem.tag, str) and 'Comment' in elem.tag: elem.parent.remove(elem) return ET.tostring(root, encoding='unicode', method="xml") # 10. Read in and provide UI for past files @st.cache_resource def read_file_content(file,max_length): if file.type == "application/json": content = json.load(file) return str(content) elif file.type == "text/html" or file.type == "text/htm": content = BeautifulSoup(file, "html.parser") return content.text elif file.type == "application/xml" or file.type == "text/xml": tree = ET.parse(file) root = tree.getroot() xml = CompressXML(ET.tostring(root, encoding='unicode')) return xml elif file.type == "text/markdown" or file.type == "text/md": md = mistune.create_markdown() content = md(file.read().decode()) return content elif file.type == "text/plain": return file.getvalue().decode() else: return "" # 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS @st.cache_resource def chat_with_model(prompt, document_section='', model_choice='gpt-3.5-turbo'): model = model_choice conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}] conversation.append({'role': 'user', 'content': prompt}) if len(document_section)>0: conversation.append({'role': 'assistant', 'content': document_section}) start_time = time.time() report = [] res_box = st.empty() collected_chunks = [] collected_messages = [] 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 @st.cache_resource def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'): conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}] conversation.append({'role': 'user', 'content': prompt}) if len(file_content)>0: conversation.append({'role': 'assistant', 'content': file_content}) response = openai.ChatCompletion.create(model=model_choice, messages=conversation) return response['choices'][0]['message']['content'] def extract_mime_type(file): if isinstance(file, str): pattern = r"type='(.*?)'" match = re.search(pattern, file) if match: return match.group(1) else: raise ValueError(f"Unable to extract MIME type from {file}") elif isinstance(file, streamlit.UploadedFile): return file.type else: raise TypeError("Input should be a string or a streamlit.UploadedFile object") def extract_file_extension(file): # get the file name directly from the UploadedFile object file_name = file.name pattern = r".*?\.(.*?)$" match = re.search(pattern, file_name) if match: return match.group(1) else: raise ValueError(f"Unable to extract file extension from {file_name}") # Normalize input as text from PDF and other formats @st.cache_resource def pdf2txt(docs): text = "" for file in docs: file_extension = extract_file_extension(file) st.write(f"File type extension: {file_extension}") if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']: text += file.getvalue().decode('utf-8') elif file_extension.lower() == 'pdf': from PyPDF2 import PdfReader pdf = PdfReader(BytesIO(file.getvalue())) for page in range(len(pdf.pages)): text += pdf.pages[page].extract_text() # new PyPDF2 syntax return text def txt2chunks(text): text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len) return text_splitter.split_text(text) # Vector Store using FAISS @st.cache_resource def vector_store(text_chunks): embeddings = OpenAIEmbeddings(openai_api_key=key) return FAISS.from_texts(texts=text_chunks, embedding=embeddings) # Memory and Retrieval chains @st.cache_resource def get_chain(vectorstore): llm = ChatOpenAI() memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory) def process_user_input(user_question): response = st.session_state.conversation({'question': user_question}) st.session_state.chat_history = response['chat_history'] for i, message in enumerate(st.session_state.chat_history): template = user_template if i % 2 == 0 else bot_template st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True) filename = generate_filename(user_question, 'txt') response = message.content user_prompt = user_question create_file(filename, user_prompt, response, should_save) def divide_prompt(prompt, max_length): words = prompt.split() chunks = [] current_chunk = [] current_length = 0 for word in words: if len(word) + current_length <= max_length: current_length += len(word) + 1 current_chunk.append(word) else: chunks.append(' '.join(current_chunk)) current_chunk = [word] current_length = len(word) chunks.append(' '.join(current_chunk)) return chunks # 13. Provide way of saving all and deleting all to give way of reviewing output and saving locally before clearing it @st.cache_resource def create_zip_of_files(files): zip_name = "all_files.zip" with zipfile.ZipFile(zip_name, 'w') as zipf: for file in files: zipf.write(file) return zip_name @st.cache_resource def get_zip_download_link(zip_file): with open(zip_file, 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() href = f'Download All' 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}") def add_medical_exam_buttons(): # Medical exam terminology descriptions descriptions = { "White Blood Cells ๐ŸŒŠ": "3 Q&A with emojis about types, facts, function, inputs and outputs of white blood cells ๐ŸŽฅ", "CT Imaging๐Ÿฆ ": "3 Q&A with emojis on CT Imaging post surgery, how to, what to look for ๐Ÿ’Š", "Hematoma ๐Ÿ’‰": "3 Q&A with emojis about hematoma and infection care and study including bacteria cultures and tests or labs๐Ÿ’ช", "Post Surgery Wound Care ๐ŸŒ": "3 Q&A with emojis on wound care, and good bedside manner ๐Ÿฉธ", "Healing and humor ๐Ÿ’Š": "3 Q&A with emojis on stories and humor about healing and caregiving ๐Ÿš‘", "Psychology of bedside manner ๐Ÿงฌ": "3 Q&A with emojis on bedside manner and how to make patients feel at ease๐Ÿ› ", "CT scan ๐Ÿ’Š": "3 Q&A with analysis on infection using CT scan and packing for skin, cellulitus and fascia ๐Ÿฉบ" } # Expander for medical topics with st.expander("Medical Licensing Exam Topics ๐Ÿ“š", expanded=False): st.markdown("๐Ÿฉบ **Important**: Variety of topics for medical licensing exams.") # Create buttons for each description with unique keys for idx, (label, content) in enumerate(descriptions.items()): button_key = f"button_{idx}" if st.button(label, key=button_key): st.write(f"Running {label}") input='Create markdown outline for definition of topic ' + label + ' also short quiz with appropriate emojis and definitions for: ' + content response=StreamLLMChatResponse(input) filename = generate_filename(response, 'txt') create_file(filename, input, response, should_save) def add_medical_exam_buttons2(): with st.expander("Medical Licensing Exam Topics ๐Ÿ“š", expanded=False): st.markdown("๐Ÿฉบ **Important**: This section provides a variety of medical topics that are often encountered in medical licensing exams.") # Define medical exam terminology descriptions descriptions = { "White Blood Cells ๐ŸŒŠ": "3 Questions and Answers with emojis about white blood cells ๐ŸŽฅ", "CT Imaging๐Ÿฆ ": "3 Questions and Answers with emojis about CT Imaging of post surgery abscess, hematoma, and cerosanguiness fluid ๐Ÿ’Š", "Hematoma ๐Ÿ’‰": "3 Questions and Answers with emojis about hematoma and infection and how heat helps white blood cells ๐Ÿ’ช", "Post Surgery Wound Care ๐ŸŒ": "3 Questions and Answers with emojis about wound care and how to help as a caregiver๐Ÿฉธ", "Healing and humor ๐Ÿ’Š": "3 Questions and Answers with emojis on the use of stories and humor to help patients and family ๐Ÿš‘", "Psychology of bedside manner ๐Ÿงฌ": "3 Questions and Answers with emojis about good bedside manner ๐Ÿ› ", "CT scan ๐Ÿ’Š": "3 Questions and Answers with analysis of bacteria and understanding infection using cultures and CT scan ๐Ÿฉบ" } # Create columns col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small") # Add buttons to columns if col1.button("Ultrasound with Doppler ๐ŸŒŠ"): StreamLLMChatResponse(descriptions["Ultrasound with Doppler ๐ŸŒŠ"]) if col2.button("Oseltamivir ๐Ÿฆ "): StreamLLMChatResponse(descriptions["Oseltamivir ๐Ÿฆ "]) if col3.button("IM Epinephrine ๐Ÿ’‰"): StreamLLMChatResponse(descriptions["IM Epinephrine ๐Ÿ’‰"]) if col4.button("Hypokalemia ๐ŸŒ"): StreamLLMChatResponse(descriptions["Hypokalemia ๐ŸŒ"]) col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small") if col5.button("Succinylcholine ๐Ÿ’Š"): StreamLLMChatResponse(descriptions["Succinylcholine ๐Ÿ’Š"]) if col6.button("Phosphoinositol System ๐Ÿงฌ"): StreamLLMChatResponse(descriptions["Phosphoinositol System ๐Ÿงฌ"]) if col7.button("Ramipril ๐Ÿ’Š"): StreamLLMChatResponse(descriptions["Ramipril ๐Ÿ’Š"]) # 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 ๐Ÿ“š", 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("๐Ÿ‘๏ธ 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('๐Ÿ’ฌ 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) # 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("๐Ÿ—‘ Delete All Text"): for file in all_files: os.remove(file) st.experimental_rerun() if st.sidebar.button("โฌ‡๏ธ 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("๐ŸŒ", 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("๐Ÿ“‚", key="open_"+file): # open emoji button with open(file, 'r') as f: file_contents = f.read() next_action='open' with col4: if st.button("๐Ÿ”", key="read_"+file): # search emoji button with open(file, 'r') as f: file_contents = f.read() next_action='search' with col5: if st.button("๐Ÿ—‘", 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 = '๐Ÿ”Run with Llama and GPT.' if st.button(key='RunWithLlamaandGPT', label = buttonlabel): user_prompt = file_contents # Llama versus GPT Battle! all="" try: st.write('๐Ÿ”Running with Llama.') response = StreamLLMChatResponse(file_contents) filename = generate_filename(user_prompt, "md") create_file(filename, file_contents, response, should_save) all=response #SpeechSynthesis(response) except: st.markdown('Llama is sleeping. Restart ETA 30 seconds.') # gpt try: st.write('๐Ÿ”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=all+response2 #SpeechSynthesis(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('๐Ÿ”Running with Llama and GPT.') user_prompt = file_contents # Llama versus GPT Battle! all="" try: st.write('๐Ÿ”Running with Llama.') response = StreamLLMChatResponse(file_contents) filename = generate_filename(user_prompt, ".md") create_file(filename, file_contents, response, should_save) all=response #SpeechSynthesis(response) except: st.markdown('Llama is sleeping. Restart ETA 30 seconds.') # gpt try: st.write('๐Ÿ”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=all+response2 #SpeechSynthesis(response2) except: st.markdown('GPT is sleeping. Restart ETA 30 seconds.') SpeechSynthesis(all) # 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'โฌ‡๏ธ Download Audio' # 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("๐Ÿ—‘ 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("๐ŸŽต", 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("๐Ÿ—‘", 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 ๐Ÿ‘", expanded=False): feedback = st.radio("Step 8: Give your feedback", ("๐Ÿ‘ Upvote", "๐Ÿ‘Ž Downvote")) if feedback == "๐Ÿ‘ Upvote": st.write("You upvoted ๐Ÿ‘. Thank you for your feedback!") else: st.write("You downvoted ๐Ÿ‘Ž. 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('No glossary lookup') # Display the glossary grid st.title("Roleplaying Games Glossary ๐ŸŽฒ") display_glossary_grid(roleplaying_glossary) st.title("๐ŸŽฒ๐Ÿ—บ๏ธ Roleplaying Game Universe") st.markdown("## Explore the vast universes of Dungeons and Dragons, Call of Cthulhu, GURPS, and more through interactive storytelling and encyclopedic knowledge.๐ŸŒ ") display_buttons_with_scores() display_images_and_wikipedia_summaries() # 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()