app.py

import streamlit as st
import streamlit.components.v1 as components
import os
import json
import random
import base64
import glob
import math
import openai
import pytz
import re
import requests
import textract
import time
import zipfile
import huggingface_hub
import dotenv
from audio_recorder_streamlit import audio_recorder
from bs4 import BeautifulSoup
from collections import deque
from datetime import datetime
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
from io import BytesIO
from openai import ChatCompletion
from PyPDF2 import PdfReader
from templates import bot_template, css, user_template
from xml.etree import ElementTree as ET
from PIL import Image
from urllib.parse import quote  # Ensure this import is included

# Set page configuration with a title and favicon
st.set_page_config(
    page_title="🃏🎴CardGameAI",
    page_icon="🃏🎴",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
        'Get Help': 'https://huggingface.co/awacke1',
        'Report a bug': "https://huggingface.co/spaces/awacke1/WebDataDownload",
        'About': "# Midjourney: https://discord.com/channels/@me/997514686608191558"
    }
)
PromptPrefix = 'Create a markdown outline and table with appropriate emojis for card game rulesets which define the method steps of play for the topics of '
PromptPrefix2 = 'Create a streamlit python user app.  Show full code listing.  Create a UI implementing each feature using variables, reusable functions with parameters, and data driven app with python libraries and streamlit components for Javascript and HTML5. Use appropriate emojis for labels to summarize and list parts, function, conditions for topic: '

st.markdown('''### 🎴📚🧠 CardGameAI''')
with st.expander("Help / About 📚", expanded=False):
    st.markdown('''
    - 🚀 **Elevate Your Game:** Strategy meets creativity in the card game universe.
    - 🛠️ **Capabilities:** Craft intricate glossaries, dynamic rules, and seamless jump-link brains.
    - 🌟 **Experience:** Every card game becomes an adventure, enhancing the play.
    - 🔍 **Query Use:** Input `?q=MagicTheGathering` or `?query=DeckBuilding` in URL for deeper insights.
    ''')

# -----------------------------------------------------------------Art Card Sidebar:
import base64
import requests
def get_image_as_base64(url):
    response = requests.get(url)
    if response.status_code == 200:
        # Convert the image to base64
        return base64.b64encode(response.content).decode("utf-8")
    else:
        return None
        
def create_download_link(filename, base64_str):
    href = f'<a href="data:file/png;base64,{base64_str}" download="{filename}">Download Image</a>'
    return href

# Get this from paste into markdown feature
image_url = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/95rO47yN-MF0bHTr8-IuO.png" 
image_url2 = "https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/yyXi71pd_Z6so2j_JeM2a.png"

image_base64 = get_image_as_base64(image_url)
image_base642 = get_image_as_base64(image_url2)
if image_base64 is not None:
    with st.sidebar:
        st.markdown("""### 🎴📚🧠CardGameAI""")
        st.markdown(f"![image](data:image/png;base64,{image_base64})")
        st.markdown(f"![image](data:image/png;base64,{image_base642})")
        #download_link = create_download_link("downloaded_image.png", image_base64)
        #st.markdown(download_link, unsafe_allow_html=True)
else:
    st.sidebar.write("Failed to load the image.")
# ------------------------------------------------------------- Art Card Sidebar


# Ensure the directory for storing scores exists
score_dir = "scores"
os.makedirs(score_dir, exist_ok=True)

# Function to generate a unique key for each button, including an emoji
def generate_key(label, header, idx):
    return f"{header}_{label}_{idx}_key"

# Function to increment and save score
def update_score(key, increment=1):
    score_file = os.path.join(score_dir, f"{key}.json")
    if os.path.exists(score_file):
        with open(score_file, "r") as file:
            score_data = json.load(file)
    else:
        score_data = {"clicks": 0, "score": 0}
    
    score_data["clicks"] += 1
    score_data["score"] += increment
    
    with open(score_file, "w") as file:
        json.dump(score_data, file)
    
    return score_data["score"]

# Function to load score
def load_score(key):
    score_file = os.path.join(score_dir, f"{key}.json")
    if os.path.exists(score_file):
        with open(score_file, "r") as file:
            score_data = json.load(file)
        return score_data["score"]
    return 0

roleplaying_glossary = {
    "🎴 Traditional Card Games": {
        "Bridge": ["Trick-taking", "Bidding and partnership", "Complex scoring"],
        "Poker": ["Betting/Card ranking", "Bluffing and hand management", "Various play styles"],
        "Hearts": ["Trick-avoidance", "Passing cards strategy", "Shooting the moon"],
        "Spades": ["Trick-taking", "Partnership and bidding", "Blind bidding"],
        "Rummy": ["Matching", "Set and run formation", "Point scoring"],
    },
    "🔮 Collectible Card Games (CCGs)": {
        "Magic: The Gathering": ["Deck building", "Resource management", "Strategic play"],
        "Yu-Gi-Oh!": ["Dueling", "Summoning strategies", "Trap and spell cards"],
        "Pokémon TCG": ["Collectible", "Type advantages", "Energy management"],
        "KeyForge": ["Unique deck", "No deck building", "Chain system"],
        "Legend of the Five Rings": ["Living Card Game", "Honor and conflict", "Clan loyalty"],
    },
    "🕹️ Digital Card Games": {
        "Hearthstone": ["Digital CCG", "Hero powers", "Expansive card sets"],
        "Gwent": ["Strategic depth", "Row-based play", "Witcher universe"],
        "Slay the Spire": ["Roguelike deck-builder", "Card drafting", "Relic synergies"],
        "Eternal Card Game": ["Digital CCG", "Cross-platform", "Drafting and events"],
    },
    "💻 Card Battler Video Games": {
        "Yu-Gi-Oh! Duel Links": ["Speed Duel format", "Mobile and PC", "Competitive ladder"],
        "Magic: The Gathering Arena": ["Digital adaptation", "Regular updates", "Esports"],
        "Monster Train": ["Roguelike", "Multi-tiered defense", "Clan synergies"],
        "Legends of Runeterra": ["League of Legends universe", "Dynamic combat", "Champion leveling"],
    },
    "🧠 Game Design and Dynamics": {
        "Deck Building Strategies": ["Card synergy", "Mana curve", "Meta considerations"],
        "Gameplay Mechanics": ["Turn-based", "Resource management", "Combat dynamics"],
        "Player Engagement": ["Replayability", "Strategic depth", "Social play"],
    },
    "📚 Lore & Background": {
        "Magic: The Gathering": ["Rich lore", "Multiverse settings", "Planeswalker stories"],
        "Yu-Gi-Oh!": ["Anime-based", "Duel Monsters", "Egyptian mythology"],
        "Legends of Runeterra": ["Expansive lore", "Champion backstories", "Faction conflicts"],
    },
    "🛠️ Digital Tools & Platforms": {
        "Online Play": ["Remote gameplay", "Digital tournaments", "Community events"],
        "Deck Building Tools": ["Card database access", "Deck testing", "Community sharing"],
        "Strategy Guides": ["Meta analysis", "Deck guides", "Tournament reports"],
    },
    "🎖️ Competitive Scene": {
        "Tournaments": ["Local game stores", "Regional competitions", "World championships"],
        "Ranking Systems": ["Elo ratings", "Ladder rankings", "Seasonal rewards"],
        "Esports": ["Live-streamed events", "Professional teams", "Sponsorships"],
    },
}


def search_glossary(query):
    for category, terms in roleplaying_glossary.items():
        if query.lower() in (term.lower() for term in terms):
            st.markdown(f"#### {category}")
            st.write(f"- {query}")

    all=""

    query2 = PromptPrefix + query # Add prompt preface for method step task behavior
    # st.write('## ' + query2)
    st.write('## 🔍 Running with GPT.')  #  -------------------------------------------------------------------------------------------------
    response = chat_with_model(query2)    
    filename = generate_filename(query2 + ' --- ' + response, "md")
    create_file(filename, query, response, should_save)
    
    query3 = PromptPrefix2 + query + ' creating streamlit functions that implement outline of method steps below: ' + response # Add prompt preface for coding task behavior
    # st.write('## ' + query3)
    st.write('## 🔍 Coding with GPT.')  #  -------------------------------------------------------------------------------------------------
    response2 = chat_with_model(query3)
    filename_txt = generate_filename(query + ' --- ' + response2, "py")
    create_file(filename_txt, query, response2, should_save)
    
    all = '# Query: ' + query + '# Response: ' + response + '# Response2: ' + response2
    filename_txt2 = generate_filename(query + ' --- ' + all, "md")
    create_file(filename_txt2, query, all, should_save)
    SpeechSynthesis(all)
    return all
    
    
# Function to display the glossary in a structured format
def display_glossary(glossary, area):
    if area in glossary:
        st.subheader(f"📘 Glossary for {area}")
        for game, terms in glossary[area].items():
            st.markdown(f"### {game}")
            for idx, term in enumerate(terms, start=1):
                st.write(f"{idx}. {term}")


# Function to display the entire glossary in a grid format with links
def display_glossary_grid(roleplaying_glossary):
    search_urls = {
        "📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
        "🔍": lambda k: f"https://www.google.com/search?q={quote(k)}",
        "▶️": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
        "🔎": lambda k: f"https://www.bing.com/search?q={quote(k)}",
        "🎲": lambda k: f"https://huggingface.co/spaces/awacke1/MixableCardGameAI?q={quote(k)}",  # this url plus query!
        
    }

    for category, details in roleplaying_glossary.items():
        st.write(f"### {category}")
        cols = st.columns(len(details))  # Create dynamic columns based on the number of games
        for idx, (game, terms) in enumerate(details.items()):
            with cols[idx]:
                st.markdown(f"#### {game}")
                for term in terms:
                    links_md = ' '.join([f"[{emoji}]({url(term)})" for emoji, url in search_urls.items()])
                    st.markdown(f"{term} {links_md}", unsafe_allow_html=True)


game_emojis = {
    "Dungeons and Dragons": "🐉",
    "Call of Cthulhu": "🐙",
    "GURPS": "🎲",
    "Pathfinder": "🗺️",
    "Kindred of the East": "🌅",
    "Changeling": "🍃",
}

topic_emojis = {
    "Core Rulebooks": "📚",
    "Maps & Settings": "🗺️",
    "Game Mechanics & Tools": "⚙️",
    "Monsters & Adversaries": "👹",
    "Campaigns & Adventures": "📜",
    "Creatives & Assets": "🎨",
    "Game Master Resources": "🛠️",
    "Lore & Background": "📖",
    "Character Development": "🧍",
    "Homebrew Content": "🔧",
    "General Topics": "🌍",
}

# Adjusted display_buttons_with_scores function
def display_buttons_with_scores():
    for category, games in roleplaying_glossary.items():
        category_emoji = topic_emojis.get(category, "🔍")  # Default to search icon if no match
        st.markdown(f"## {category_emoji} {category}")
        for game, terms in games.items():
            game_emoji = game_emojis.get(game, "🎮")  # Default to generic game controller if no match
            for term in terms:
                key = f"{category}_{game}_{term}".replace(' ', '_').lower()
                score = load_score(key)
                if st.button(f"{game_emoji} {term} {score}", key=key):
                    update_score(key)
                    # Create a dynamic query incorporating emojis and formatting for clarity
                    query_prefix = f"{category_emoji} {game_emoji} **{game} - {category}:**"
                    # -----------------------------------------------------------------
                    # query_body = f"Create a detailed outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements."
                    query_body = f"Create a streamlit python app.py that produces a detailed markdown outline and CSV dataset user interface with an outline for **{term}** with subpoints highlighting key aspects, using emojis for visual engagement. Include step-by-step rules and boldface important entities and ruleset elements."
                    response = search_glossary(query_prefix + query_body)


def fetch_wikipedia_summary(keyword):
    # Placeholder function for fetching Wikipedia summaries
    # In a real app, you might use requests to fetch from the Wikipedia API
    return f"Summary for {keyword}. For more information, visit Wikipedia."

def create_search_url_youtube(keyword):
    base_url = "https://www.youtube.com/results?search_query="
    return base_url + keyword.replace(' ', '+')

def create_search_url_bing(keyword):
    base_url = "https://www.bing.com/search?q="
    return base_url + keyword.replace(' ', '+')

def create_search_url_wikipedia(keyword):
    base_url = "https://www.wikipedia.org/search-redirect.php?family=wikipedia&language=en&search="
    return base_url + keyword.replace(' ', '+')

def create_search_url_google(keyword):
    base_url = "https://www.google.com/search?q="
    return base_url + keyword.replace(' ', '+')

def create_search_url_ai(keyword):
    base_url = "https://huggingface.co/spaces/awacke1/MixableCardGameAI?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:
        keyword = image_file.split('.')[0]  # Assumes keyword is the file name without extension
        image = Image.open(image_file)
        st.image(image, caption=image_file, use_column_width=True)

        # 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)
        ai_url = create_search_url_ai(keyword)
        
        
        links_md = f"""
        [Wikipedia]({wikipedia_url}) | 
        [Google]({google_url}) | 
        [YouTube]({youtube_url}) | 
        [Bing]({bing_url}) | 
        [AI]({ai_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.")

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}")



# 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=Magic the Gathering` or `?query=Yugi-Oh`.")

    
    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

    
        
    display_images_and_wikipedia_summaries()

    st.title("Card Games Glossary 🎲")
    display_glossary_grid(roleplaying_glossary)
    
    st.title("🎲🗺️ Card Game Universe")
    st.markdown("## Explore the vast universes of Dungeons and Dragons, Call of Cthulhu, GURPS, and more through interactive storytelling and encyclopedic knowledge.🌠")
    
    display_buttons_with_scores()
    
    # Assuming the transhuman_glossary and other setup code remains the same
    #st.write("Current Query Parameters:", st.query_params)
    #st.markdown("### Query Parameters - These Deep Link Map to Remixable Methods, Navigate or Trigger Functionalities")
    
    # Example: Using query parameters to navigate or trigger functionalities
    if 'action' in st.query_params:
        action = st.query_params()['action'][0]  # Get the first (or only) 'action' parameter
        if action == 'show_message':
            st.success("Showing a message because 'action=show_message' was found in the URL.")
        elif action == 'clear':
            clear_query_params()
            st.experimental_rerun()
    
    # Handling repeated keys
    if 'multi' in st.query_params:
        multi_values = get_all_query_params('multi')
        st.write("Values for 'multi':", multi_values)
    
    # Manual entry for demonstration
    st.write("Enter query parameters in the URL like this: ?action=show_message&multi=1&multi=2")
    
    if 'query' in st.query_params:
        query = st.query_params['query'][0]  # Get the query parameter
        # Display content or image based on the query
        display_content_or_image(query)
    
    # Add a clear query parameters button for convenience
    if st.button("Clear Query Parameters", key='ClearQueryParams'):
        # This will clear the browser URL's query parameters
        st.experimental_set_query_params
        st.experimental_rerun()
                    
# 18. Run AI Pipeline
if __name__ == "__main__":
    whisper_main()
    main()