Create backup011224.app.py

backup011224.app.py

@@ -0,0 +1,1188 @@
+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()