Delete backup011224.app.py

backup011224.app.py

@@ -1,1188 +0,0 @@
-import streamlit as st
-import os
-import json
-from PIL import Image
-
-# Set page configuration with a title and favicon
-st.set_page_config(page_title="🌌🚀 Transhuman Space Encyclopedia", page_icon="🌠", layout="wide")
-
-# 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
-
-# Transhuman Space glossary with full content
-transhuman_glossary = {
-    "🚀 Core Technologies": ["Nanotechnology🔬", "Artificial Intelligence🤖", "Quantum Computing💻", "Spacecraft Engineering🛸", "Biotechnology🧬", "Cybernetics🦾", "Virtual Reality🕶️", "Energy Systems⚡", "Material Science🧪", "Communication Technologies📡"],
-    "🌐 Nations": ["Terran Federation🌍", "Martian Syndicate🔴", "Jovian Republics🪐", "Asteroid Belt Communities🌌", "Venusian Colonies🌋", "Lunar States🌖", "Outer System Alliances✨", "Digital Consciousness Collectives🧠", "Transhumanist Enclaves🦿", "Non-Human Intelligence Tribes👽"],
-    "💡 Memes": ["Post-Humanism🚶‍♂️➡️🚀", "Neo-Evolutionism🧬📈", "Digital Ascendancy💾👑", "Solar System Nationalism🌞🏛", "Space Explorationism🚀🛰", "Cyber Democracy🖥️🗳️", "Interstellar Environmentalism🌍💚", "Quantum Mysticism🔮💫", "Techno-Anarchism🔌🏴", "Cosmic Preservationism🌌🛡️"],
-    "🏛 Institutions": ["Interstellar Council🪖", "Transhuman Ethical Standards Organization📜", "Galactic Trade Union🤝", "Space Habitat Authority🏠", "Artificial Intelligence Safety Commission🤖🔒", "Extraterrestrial Relations Board👽🤝", "Quantum Research Institute🔬", "Biogenetics Oversight Committee🧫", "Cyberspace Regulatory Agency💻", "Planetary Defense Coalition🌍🛡"],
-    "🔗 Organizations": ["Neural Network Pioneers🧠🌐", "Spacecraft Innovators Guild🚀🛠", "Quantum Computing Consortium💻🔗", "Interplanetary Miners Union⛏️🪐", "Cybernetic Augmentation Advocates🦾❤️", "Biotechnological Harmony Group🧬🕊", "Stellar Navigation Circle🧭✨", "Virtual Reality Creators Syndicate🕶️🎨", "Renewable Energy Pioneers⚡🌱", "Transhuman Rights Activists🦿📢"],
-    "⚔️ War": ["Space Warfare Tactics🚀⚔️", "Cyber Warfare🖥️🔒", "Biological Warfare🧬💣", "Nanotech Warfare🔬⚔️", "Psychological Operations🧠🗣️", "Quantum Encryption & Decryption🔐💻", "Kinetic Bombardment🚀💥", "Energy Shield Defense🛡️⚡", "Stealth Spacecraft🚀🔇", "Artificial Intelligence Combat🤖⚔️"],
-    "🎖 Military": ["Interstellar Navy🚀🎖", "Planetary Guard🌍🛡", "Cybernetic Marines🦾🔫", "Nanotech Soldiers🔬💂", "Space Drone Fleet🛸🤖", "Quantum Signal Corps💻📡", "Special Operations Forces👥⚔️", "Artificial Intelligence Strategists🤖🗺️", "Orbital Defense Systems🌌🛡️", "Exoskeleton Brigades🦾🚶‍♂️"],
-    "🦹 Outlaws": ["Pirate Fleets🏴‍☠️🚀", "Hacktivist Collectives💻🚫", "Smuggler Caravans🛸💼", "Rebel AI Entities🤖🚩", "Black Market Biotech Dealers🧬💰", "Quantum Thieves💻🕵️‍♂️", "Space Nomad Raiders🚀🏴‍☠️", "Cyberspace Intruders💻👾", "Anti-Transhumanist Factions🚫🦾", "Rogue Nanotech Swarms🔬🦠"],
-    "👽 Terrorists": ["Bioengineered Virus Spreaders🧬💉", "Nanotechnology Saboteurs🔬🧨", "Cyber Terrorist Networks💻🔥", "Rogue AI Sects🤖🛑", "Space Anarchist Cells🚀Ⓐ", "Quantum Data Hijackers💻🔓", "Environmental Extremists🌍💣", "Technological Singularity Cults🤖🙏", "Interspecies Supremacists👽👑", "Orbital Bombardment Threats🛰️💥"],
-}
-
-
-# Function to search glossary and display results
-def search_glossary(query):
-    for category, terms in transhuman_glossary.items():
-        if query.lower() in (term.lower() for term in terms):
-            st.markdown(f"### {category}")
-            st.write(f"- {query}")
-
-    st.write('## Processing query against GPT and Llama:')
-    # ------------------------------------------------------------------------------------------------
-    st.write('Reasoning with your inputs using GPT...')
-    response = chat_with_model(query)
-    st.write('Response:')
-    st.write(response)
-    filename = generate_filename(response, "txt")
-    create_file(filename, query, response, should_save)
-    
-    st.write('Reasoning with your inputs using Llama...')    
-    response = StreamLLMChatResponse(query)
-    filename_txt = generate_filename(query, "md")
-    create_file(filename_txt, query, response, should_save)
-    # ------------------------------------------------------------------------------------------------
-        
-            
-# Display the glossary with Streamlit components, ensuring emojis are used
-def display_glossary(area):
-    st.subheader(f"📘 Glossary for {area}")
-    terms = transhuman_glossary[area]
-    for idx, term in enumerate(terms, start=1):
-        st.write(f"{idx}. {term}")
-
-
-# Function to display glossary in a 3x3 grid
-def display_glossary_grid(glossary):
-    # Group related categories for a 3x3 grid
-    groupings = [
-        ["🚀 Core Technologies", "🌐 Nations", "💡 Memes"],
-        ["🏛 Institutions", "🔗 Organizations", "⚔️ War"],
-        ["🎖 Military", "🦹 Outlaws", "👽 Terrorists"],
-    ]
-    
-    for group in groupings:
-        cols = st.columns(3)  # Create three columns
-        for idx, category in enumerate(group):
-            with cols[idx]:
-                st.markdown(f"### {category}")
-                terms = glossary[category]
-                for term in terms:
-                    st.write(f"- {term}")
-
-# Streamlined UI for displaying buttons with scores, integrating emojis
-def display_buttons_with_scores():
-    for header, terms in transhuman_glossary.items():
-        st.markdown(f"## {header}")
-        for term in terms:
-            key = generate_key(term, header, terms.index(term))
-            score = load_score(key)
-            if st.button(f"{term} {score}🚀", key=key):
-                update_score(key)
-                search_glossary('Create a three level markdown outline with 3 subpoints each where each line defines and writes out the core technology descriptions with appropriate emojis for the glossary term: ' + term)
-                st.experimental_rerun()
-
-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("😢 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. 🎙️"])
-
-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
-
-
-# 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)]
-
-# 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'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
-    return href
-
-
-def CompressXML(xml_text):
-    root = ET.fromstring(xml_text)
-    for elem in list(root.iter()):
-        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
-            elem.parent.remove(elem)
-    return ET.tostring(root, encoding='unicode', method="xml")
-
-# 10. Read in and provide UI for past files
-@st.cache_resource
-def read_file_content(file,max_length):
-    if file.type == "application/json":
-        content = json.load(file)
-        return str(content)
-    elif file.type == "text/html" or file.type == "text/htm":
-        content = BeautifulSoup(file, "html.parser")
-        return content.text
-    elif file.type == "application/xml" or file.type == "text/xml":
-        tree = ET.parse(file)
-        root = tree.getroot()
-        xml = CompressXML(ET.tostring(root, encoding='unicode'))
-        return xml
-    elif file.type == "text/markdown" or file.type == "text/md":
-        md = mistune.create_markdown()
-        content = md(file.read().decode())
-        return content
-    elif file.type == "text/plain":
-        return file.getvalue().decode()
-    else:
-        return ""
-
-# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS
-@st.cache_resource
-def chat_with_model(prompt, document_section='', model_choice='gpt-3.5-turbo'):
-    model = model_choice
-    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
-    conversation.append({'role': 'user', 'content': prompt})
-    if len(document_section)>0:
-        conversation.append({'role': 'assistant', 'content': document_section})
-    start_time = time.time()
-    report = []
-    res_box = st.empty()
-    collected_chunks = []
-    collected_messages = []
-
-    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'<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}")
-
-
-
-def add_medical_exam_buttons():
-    # Medical exam terminology descriptions
-    descriptions = {
-        "White Blood Cells 🌊": "3 Q&A with emojis about types, facts, function, inputs and outputs of white blood cells 🎥",
-        "CT Imaging🦠": "3 Q&A with emojis on CT Imaging post surgery, how to, what to look for 💊",
-        "Hematoma 💉": "3 Q&A with emojis about hematoma and infection care and study including bacteria cultures and tests or labs💪",
-        "Post Surgery Wound Care 🍌": "3 Q&A with emojis on wound care, and good bedside manner 🩸",
-        "Healing and humor 💊": "3 Q&A with emojis on stories and humor about healing and caregiving 🚑",
-        "Psychology of bedside manner 🧬": "3 Q&A with emojis on bedside manner and how to make patients feel at ease🛠",
-        "CT scan 💊": "3 Q&A with analysis on infection using CT scan and packing for skin, cellulitus and fascia 🩺"
-    }
-
-    # Expander for medical topics
-    with st.expander("Medical Licensing Exam Topics 📚", expanded=False):
-        st.markdown("🩺 **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 📚", expanded=False):
-        st.markdown("🩺 **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 🌊": "3 Questions and Answers with emojis about white blood cells 🎥",
-            "CT Imaging🦠": "3 Questions and Answers with emojis about CT Imaging of post surgery abscess, hematoma, and cerosanguiness fluid 💊",
-            "Hematoma 💉": "3 Questions and Answers with emojis about hematoma and infection and how heat helps white blood cells 💪",
-            "Post Surgery Wound Care 🍌": "3 Questions and Answers with emojis about wound care and how to help as a caregiver🩸",
-            "Healing and humor 💊": "3 Questions and Answers with emojis on the use of stories and humor to help patients and family 🚑",
-            "Psychology of bedside manner 🧬": "3 Questions and Answers with emojis  about good bedside manner 🛠",
-            "CT scan 💊": "3 Questions and Answers with analysis of bacteria and understanding infection using cultures and CT scan 🩺"
-        }
-
-        # Create columns
-        col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small")
-        
-        # Add buttons to columns
-        if col1.button("Ultrasound with Doppler 🌊"):
-            StreamLLMChatResponse(descriptions["Ultrasound with Doppler 🌊"])
-
-        if col2.button("Oseltamivir 🦠"):
-            StreamLLMChatResponse(descriptions["Oseltamivir 🦠"])
-
-        if col3.button("IM Epinephrine 💉"):
-            StreamLLMChatResponse(descriptions["IM Epinephrine 💉"])
-
-        if col4.button("Hypokalemia 🍌"):
-            StreamLLMChatResponse(descriptions["Hypokalemia 🍌"])
-
-        col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small")
-
-        if col5.button("Succinylcholine 💊"):
-            StreamLLMChatResponse(descriptions["Succinylcholine 💊"])
-
-        if col6.button("Phosphoinositol System 🧬"):
-            StreamLLMChatResponse(descriptions["Phosphoinositol System 🧬"])
-
-        if col7.button("Ramipril 💊"):
-            StreamLLMChatResponse(descriptions["Ramipril 💊"])
-        
-
-
-# 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)
-
-    # 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 with Llama and GPT.'
-            if st.button(key='RunWithLlamaandGPT', label = buttonlabel):
-                user_prompt = file_contents
-                
-                # Llama versus GPT Battle!
-                all=""
-                try:
-                    st.write('🔍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('🔍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('🔍Running with Llama and GPT.')
-
-            user_prompt = file_contents
-            
-            # Llama versus GPT Battle!
-            all=""
-            try:
-                st.write('🔍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('🔍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'<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('No glossary lookup')
-
-    # Display the glossary grid
-    st.title("Transhuman Space Glossary 🌌")
-    display_glossary_grid(transhuman_glossary)
-    
-    st.title("🌌🚀 Transhuman Space Encyclopedia")
-    st.markdown("## Explore the universe of Transhuman Space through interactive storytelling and encyclopedic knowledge.🌠")
-    
-    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()