backup17.app.py

import streamlit as st
import asyncio
import websockets
import uuid
from datetime import datetime
import os
import random
import time
import hashlib
from PIL import Image
import glob
import base64
import io
import streamlit.components.v1 as components
import edge_tts
from audio_recorder_streamlit import audio_recorder
import nest_asyncio
import re
from streamlit_paste_button import paste_image_button
import pytz
import shutil
import anthropic
import openai
from PyPDF2 import PdfReader
import threading
import json
import zipfile
from gradio_client import Client
from dotenv import load_dotenv
from streamlit_marquee import streamlit_marquee
from collections import defaultdict, Counter
import pandas as pd

# 🛠️ Patch asyncio for nesting
nest_asyncio.apply()

# 🎨 Page Config
st.set_page_config(
    page_title="🚲TalkingAIResearcher🏆",
    page_icon="🚲🏆",
    layout="wide",
    initial_sidebar_state="auto"
)

# 🌟 Static Config
icons = '🤖🧠🔬📝'
Site_Name = '🤖🧠Chat & Quote Node📝🔬'
START_ROOM = "Sector 🌌"
FUN_USERNAMES = {
    "CosmicJester 🌌": "en-US-AriaNeural",
    "PixelPanda 🐼": "en-US-JennyNeural",
    "QuantumQuack 🦆": "en-GB-SoniaNeural",
    "StellarSquirrel 🐿️": "en-AU-NatashaNeural",
    "GizmoGuru ⚙️": "en-CA-ClaraNeural",
    "NebulaNinja 🌠": "en-US-GuyNeural",
    "ByteBuster 💾": "en-GB-RyanNeural",
    "GalacticGopher 🌍": "en-AU-WilliamNeural",
    "RocketRaccoon 🚀": "en-CA-LiamNeural",
    "EchoElf 🧝": "en-US-AnaNeural",
    "PhantomFox 🦊": "en-US-BrandonNeural",
    "WittyWizard 🧙": "en-GB-ThomasNeural",
    "LunarLlama 🌙": "en-AU-FreyaNeural",
    "SolarSloth ☀️": "en-CA-LindaNeural",
    "AstroAlpaca 🦙": "en-US-ChristopherNeural",
    "CyberCoyote 🐺": "en-GB-ElliotNeural",
    "MysticMoose 🦌": "en-AU-JamesNeural",
    "GlitchGnome 🧚": "en-CA-EthanNeural",
    "VortexViper 🐍": "en-US-AmberNeural",
    "ChronoChimp 🐒": "en-GB-LibbyNeural"
}
EDGE_TTS_VOICES = list(set(FUN_USERNAMES.values()))
FILE_EMOJIS = {"md": "📝", "mp3": "🎵", "png": "🖼️", "mp4": "🎥"}

# 📁 Directories
for d in ["chat_logs", "vote_logs", "audio_logs", "history_logs", "audio_cache", "paper_metadata"]:
    os.makedirs(d, exist_ok=True)

CHAT_DIR = "chat_logs"
VOTE_DIR = "vote_logs"
MEDIA_DIR = "."
AUDIO_CACHE_DIR = "audio_cache"
AUDIO_DIR = "audio_logs"
PAPER_DIR = "paper_metadata"
STATE_FILE = "user_state.txt"

CHAT_FILE = os.path.join(CHAT_DIR, "global_chat.md")
QUOTE_VOTES_FILE = os.path.join(VOTE_DIR, "quote_votes.md")
IMAGE_VOTES_FILE = os.path.join(VOTE_DIR, "image_votes.md")
HISTORY_FILE = os.path.join(VOTE_DIR, "vote_history.md")

# 🔑 API Keys
load_dotenv()
anthropic_key = os.getenv('ANTHROPIC_API_KEY', st.secrets.get('ANTHROPIC_API_KEY', ""))
openai_api_key = os.getenv('OPENAI_API_KEY', st.secrets.get('OPENAI_API_KEY', ""))
openai_client = openai.OpenAI(api_key=openai_api_key)

# 🕒 Timestamp Helper
def format_timestamp_prefix(username=""):
    central = pytz.timezone('US/Central')
    now = datetime.now(central)
    return f"{now.strftime('%Y%m%d_%H%M%S')}-by-{username}"

# 📈 Performance Timer
class PerformanceTimer:
    def __init__(self, name):
        self.name, self.start = name, None
    def __enter__(self):
        self.start = time.time()
        return self
    def __exit__(self, *args):
        duration = time.time() - self.start
        st.session_state['operation_timings'][self.name] = duration
        st.session_state['performance_metrics'][self.name].append(duration)

# 🎛️ Session State Init
def init_session_state():
    defaults = {
        'server_running': False, 'server_task': None, 'active_connections': {},
        'media_notifications': [], 'last_chat_update': 0, 'displayed_chat_lines': [],
        'message_text': "", 'audio_cache': {}, 'pasted_image_data': None,
        'quote_line': None, 'refresh_rate': 5, 'base64_cache': {},
        'transcript_history': [], 'last_transcript': "", 'image_hashes': set(),
        'tts_voice': "en-US-AriaNeural", 'chat_history': [], 'marquee_settings': {
            "background": "#1E1E1E", "color": "#FFFFFF", "font-size": "14px",
            "animationDuration": "20s", "width": "100%", "lineHeight": "35px"
        }, 'operation_timings': {}, 'performance_metrics': defaultdict(list),
        'enable_audio': True, 'download_link_cache': {}, 'username': None,
        'autosend': True, 'autosearch': True, 'last_message': "", 'last_query': "",
        'mp3_files': {}, 'timer_start': time.time(), 'quote_index': 0,
        'quote_source': "famous", 'last_sent_transcript': "", 'old_val': None,
        'last_refresh': time.time(), 'paper_metadata': {}
    }
    for k, v in defaults.items():
        if k not in st.session_state:
            st.session_state[k] = v

# 🖌️ Marquee Helpers
def update_marquee_settings_ui():
    st.sidebar.markdown("### 🎯 Marquee Settings")
    cols = st.sidebar.columns(2)
    with cols[0]:
        st.session_state['marquee_settings']['background'] = st.color_picker("🎨 Background", "#1E1E1E")
        st.session_state['marquee_settings']['color'] = st.color_picker("✍️ Text", "#FFFFFF")
    with cols[1]:
        st.session_state['marquee_settings']['font-size'] = f"{st.slider('📏 Size', 10, 24, 14)}px"
        st.session_state['marquee_settings']['animationDuration'] = f"{st.slider('⏱️ Speed', 1, 20, 20)}s"

def display_marquee(text, settings, key_suffix=""):
    truncated = text[:280] + "..." if len(text) > 280 else text
    streamlit_marquee(content=truncated, **settings, key=f"marquee_{key_suffix}")
    st.write("")

# 📝 Text & File Helpers
def clean_text_for_tts(text):
    return re.sub(r'[#*!\[\]]+', '', ' '.join(text.split()))[:200] or "No text"

def clean_text_for_filename(text):
    return '_'.join(re.sub(r'[^\w\s-]', '', text.lower()).split())[:200]

def get_high_info_terms(text, top_n=10):
    stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with'}
    words = re.findall(r'\b\w+(?:-\w+)*\b', text.lower())
    bi_grams = [' '.join(pair) for pair in zip(words, words[1:])]
    filtered = [t for t in words + bi_grams if t not in stop_words and len(t.split()) <= 2]
    return [t for t, _ in Counter(filtered).most_common(top_n)]

def generate_filename(prompt, username, file_type="md", title=None):
    timestamp = format_timestamp_prefix(username)
    if title:
        high_info = '-'.join(get_high_info_terms(title, 5))
        return f"{timestamp}-{clean_text_for_filename(prompt[:20])}-{high_info}.{file_type}"
    hash_val = hashlib.md5(prompt.encode()).hexdigest()[:8]
    return f"{timestamp}-{hash_val}.{file_type}"

def create_file(prompt, username, file_type="md", title=None):
    filename = generate_filename(prompt, username, file_type, title)
    with open(filename, 'w', encoding='utf-8') as f:
        f.write(prompt)
    return filename

def get_download_link(file, file_type="mp3"):
    cache_key = f"dl_{file}"
    if cache_key not in st.session_state['download_link_cache']:
        with open(file, "rb") as f:
            b64 = base64.b64encode(f.read()).decode()
        mime_types = {"mp3": "audio/mpeg", "png": "image/png", "mp4": "video/mp4", "md": "text/markdown", "zip": "application/zip"}
        st.session_state['download_link_cache'][cache_key] = f'<a href="data:{mime_types.get(file_type, "application/octet-stream")};base64,{b64}" download="{os.path.basename(file)}">{FILE_EMOJIS.get(file_type, "Download")} Download {os.path.basename(file)}</a>'
    return st.session_state['download_link_cache'][cache_key]

def save_username(username):
    try:
        with open(STATE_FILE, 'w') as f:
            f.write(username)
    except Exception as e:
        print(f"Failed to save username: {e}")

def load_username():
    if os.path.exists(STATE_FILE):
        try:
            with open(STATE_FILE, 'r') as f:
                return f.read().strip()
        except Exception as e:
            print(f"Failed to load username: {e}")
    return None

def concatenate_markdown_files():
    md_files = sorted(glob.glob("*.md"), key=os.path.getmtime, reverse=True)
    all_md_content = ""
    for md_file in md_files:
        with open(md_file, 'r', encoding='utf-8') as f:
            all_md_content += f.read() + "\n\n---\n\n"
    return all_md_content.strip()

# 🎶 Audio Processing
async def async_edge_tts_generate(text, voice, username, rate=0, pitch=0, file_format="mp3"):
    cache_key = f"{text[:100]}_{voice}_{rate}_{pitch}_{file_format}"
    if cache_key in st.session_state['audio_cache']:
        return st.session_state['audio_cache'][cache_key], 0
    start_time = time.time()
    text = clean_text_for_tts(text)
    if not text or text == "No text":
        print(f"Skipping audio generation for empty/invalid text: '{text}'")
        return None, 0
    filename = f"{format_timestamp_prefix(username)}-{hashlib.md5(text.encode()).hexdigest()[:8]}.{file_format}"
    try:
        communicate = edge_tts.Communicate(text, voice, rate=f"{rate:+d}%", pitch=f"{pitch:+d}Hz")
        await communicate.save(filename)
        st.session_state['audio_cache'][cache_key] = filename
        return filename, time.time() - start_time
    except edge_tts.exceptions.NoAudioReceived as e:
        print(f"No audio received for text: '{text}' with voice: {voice}. Error: {e}")
        return None, 0
    except Exception as e:
        print(f"Error generating audio for text: '{text}' with voice: {voice}. Error: {e}")
        return None, 0

def play_and_download_audio(file_path):
    if file_path and os.path.exists(file_path):
        st.audio(file_path)
        st.markdown(get_download_link(file_path), unsafe_allow_html=True)

def load_mp3_viewer():
    mp3_files = sorted(glob.glob(f"*.mp3"), key=os.path.getmtime, reverse=True)
    for mp3 in mp3_files:
        filename = os.path.basename(mp3)
        if filename not in st.session_state['mp3_files']:
            st.session_state['mp3_files'][filename] = mp3

async def save_chat_entry(username, message, voice, is_markdown=False):
    if not message.strip() or message == st.session_state.last_transcript:
        return None, None
    central = pytz.timezone('US/Central')
    timestamp = datetime.now(central).strftime("%Y-%m-%d %H:%M:%S")
    entry = f"[{timestamp}] {username} ({voice}): {message}" if not is_markdown else f"[{timestamp}] {username} ({voice}):\n```markdown\n{message}\n```"
    md_file = create_file(entry, username, "md")
    with open(CHAT_FILE, 'a') as f:
        f.write(f"{entry}\n")
    audio_file, _ = await async_edge_tts_generate(message, voice, username)
    if audio_file:
        with open(HISTORY_FILE, 'a') as f:
            f.write(f"[{timestamp}] {username}: Audio - {audio_file}\n")
        st.session_state['mp3_files'][os.path.basename(audio_file)] = audio_file
    await broadcast_message(f"{username}|{message}", "chat")
    st.session_state.last_chat_update = time.time()
    st.session_state.chat_history.append(entry)
    st.session_state.last_transcript = message
    return md_file, audio_file

async def load_chat():
    if not os.path.exists(CHAT_FILE):
        with open(CHAT_FILE, 'a') as f:
            f.write(f"# {START_ROOM} Chat\n\nWelcome to the cosmic hub! 🎤\n")
    with open(CHAT_FILE, 'r') as f:
        content = f.read().strip()
    lines = content.split('\n')
    unique_lines = list(dict.fromkeys(line for line in lines if line.strip()))
    return unique_lines

# Claude Search Function
async def perform_claude_search(query, username):
    if not query.strip() or query == st.session_state.last_transcript:
        return None, None, None
    client = anthropic.Anthropic(api_key=anthropic_key)
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[{"role": "user", "content": query}]
    )
    result = response.content[0].text
    st.markdown(f"### Claude's Reply 🧠\n{result}")
    
    voice = FUN_USERNAMES.get(username, "en-US-AriaNeural")
    md_file, audio_file = await save_chat_entry(username, f"Claude Search: {query}\nResponse: {result}", voice, True)
    return md_file, audio_file, result

# ArXiv Search Function
async def perform_arxiv_search(query, username, claude_result=None):
    if not query.strip() or query == st.session_state.last_transcript:
        return None, None
    if claude_result is None:
        client = anthropic.Anthropic(api_key=anthropic_key)
        claude_response = client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=1000,
            messages=[{"role": "user", "content": query}]
        )
        claude_result = claude_response.content[0].text
        st.markdown(f"### Claude's Reply 🧠\n{claude_result}")
    
    enhanced_query = f"{query}\n\n{claude_result}"
    gradio_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    refs = gradio_client.predict(
        enhanced_query, 10, "Semantic Search", "mistralai/Mixtral-8x7B-Instruct-v0.1", api_name="/update_with_rag_md"
    )[0]
    result = f"🔎 {enhanced_query}\n\n{refs}"
    voice = FUN_USERNAMES.get(username, "en-US-AriaNeural")
    md_file, audio_file = await save_chat_entry(username, f"ArXiv Search: {query}\nClaude Response: {claude_result}\nArXiv Results: {refs}", voice, True)
    return md_file, audio_file

async def perform_ai_lookup(q, vocal_summary=True, extended_refs=False, titles_summary=True, full_audio=False, useArxiv=True, useArxivAudio=False):
    start = time.time()
    client = anthropic.Anthropic(api_key=anthropic_key)
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=1000,
        messages=[{"role": "user", "content": q}]
    )
    st.write("Claude's reply 🧠:")
    st.markdown(response.content[0].text)

    result = response.content[0].text
    md_file = create_file(result, "System", "md")
    audio_file, _ = await async_edge_tts_generate(result, st.session_state['tts_voice'], "System")
    st.subheader("📝 Main Response Audio")
    play_and_download_audio(audio_file)

    papers = []
    if useArxiv:
        q = q + result
        st.write('Running Arxiv RAG with Claude inputs.')
        gradio_client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
        refs = gradio_client.predict(
            q, 20, "Semantic Search", "mistralai/Mixtral-8x7B-Instruct-v0.1", api_name="/update_with_rag_md"
        )[0]
        papers = parse_arxiv_refs(refs, q)
        for paper in papers:
            filename = create_file(generate_5min_feature_markdown(paper), "System", "md", paper['title'])
            paper['md_file'] = filename
            st.session_state['paper_metadata'][paper['title']] = filename
        if papers and useArxivAudio:
            await create_paper_audio_files(papers, q)
    elapsed = time.time() - start
    st.write(f"**Total Elapsed:** {elapsed:.2f} s")
    return result, papers

# 🌐 WebSocket Handling
async def websocket_handler(websocket, path):
    client_id = str(uuid.uuid4())
    room_id = "chat"
    if room_id not in st.session_state.active_connections:
        st.session_state.active_connections[room_id] = {}
    st.session_state.active_connections[room_id][client_id] = websocket
    username = st.session_state.get('username', random.choice(list(FUN_USERNAMES.keys())))
    chat_content = await load_chat()
    if not any(f"Client-{client_id}" in line for line in chat_content):
        await save_chat_entry("System 🌟", f"{username} has joined {START_ROOM}!", "en-US-AriaNeural")
    try:
        async for message in websocket:
            if '|' in message:
                username, content = message.split('|', 1)
                voice = FUN_USERNAMES.get(username, "en-US-AriaNeural")
                await save_chat_entry(username, content, voice)
            else:
                await websocket.send("ERROR|Message format: username|content")
    except websockets.ConnectionClosed:
        await save_chat_entry("System 🌟", f"{username} has left {START_ROOM}!", "en-US-AriaNeural")
    finally:
        if room_id in st.session_state.active_connections and client_id in st.session_state.active_connections[room_id]:
            del st.session_state.active_connections[room_id][client_id]

async def broadcast_message(message, room_id):
    if room_id in st.session_state.active_connections:
        disconnected = []
        for client_id, ws in st.session_state.active_connections[room_id].items():
            try:
                await ws.send(message)
            except websockets.ConnectionClosed:
                disconnected.append(client_id)
        for client_id in disconnected:
            if client_id in st.session_state.active_connections[room_id]:
                del st.session_state.active_connections[room_id][client_id]

async def run_websocket_server():
    if not st.session_state.server_running:
        server = await websockets.serve(websocket_handler, '0.0.0.0', 8765)
        st.session_state.server_running = True
        await server.wait_closed()

def start_websocket_server():
    asyncio.run(run_websocket_server())

# 📚 PDF to Audio
class AudioProcessor:
    def __init__(self):
        self.cache_dir = AUDIO_CACHE_DIR
        os.makedirs(self.cache_dir, exist_ok=True)
        self.metadata = json.load(open(f"{self.cache_dir}/metadata.json")) if os.path.exists(f"{self.cache_dir}/metadata.json") else {}

    def _save_metadata(self):
        with open(f"{self.cache_dir}/metadata.json", 'w') as f:
            json.dump(self.metadata, f)

    async def create_audio(self, text, voice='en-US-AriaNeural'):
        cache_key = hashlib.md5(f"{text}:{voice}".encode()).hexdigest()
        cache_path = f"{self.cache_dir}/{cache_key}.mp3"
        if cache_key in self.metadata and os.path.exists(cache_path):
            return cache_path
        text = clean_text_for_tts(text)
        if not text:
            return None
        communicate = edge_tts.Communicate(text, voice)
        await communicate.save(cache_path)
        self.metadata[cache_key] = {'timestamp': datetime.now().isoformat(), 'text_length': len(text), 'voice': voice}
        self._save_metadata()
        return cache_path

def process_pdf(pdf_file, max_pages, voice, audio_processor):
    reader = PdfReader(pdf_file)
    total_pages = min(len(reader.pages), max_pages)
    texts, audios = [], {}
    async def process_page(i, text):
        audio_path = await audio_processor.create_audio(text, voice)
        if audio_path:
            audios[i] = audio_path
    for i in range(total_pages):
        text = reader.pages[i].extract_text()
        texts.append(text)
        threading.Thread(target=lambda: asyncio.run(process_page(i, text))).start()
    return texts, audios, total_pages

# 🔍 ArXiv & AI Lookup
def parse_arxiv_refs(ref_text, query):
    if not ref_text:
        return []
    papers = []
    current = {}
    for line in ref_text.split('\n'):
        if line.count('|') == 2:
            if current:
                papers.append(current)
            date, title, *_ = line.strip('* ').split('|')
            url = re.search(r'(https://arxiv.org/\S+)', line).group(1) if re.search(r'(https://arxiv.org/\S+)', line) else f"paper_{len(papers)}"
            current = {'date': date, 'title': title, 'url': url, 'authors': '', 'summary': '', 'full_audio': None, 'download_base64': '', 'query': query}
        elif current:
            if not current['authors']:
                current['authors'] = line.strip('* ')
            else:
                current['summary'] += ' ' + line.strip() if current['summary'] else line.strip()
    if current:
        papers.append(current)
    return papers[:20]

def generate_5min_feature_markdown(paper):
    title, summary, authors, date, url = paper['title'], paper['summary'], paper['authors'], paper['date'], paper['url']
    pdf_url = url.replace("abs", "pdf") + (".pdf" if not url.endswith(".pdf") else "")
    wct, sw = len(title.split()), len(summary.split())
    terms = get_high_info_terms(summary, 15)
    rouge = round((len(terms) / max(sw, 1)) * 100, 2)
    mermaid = "```mermaid\nflowchart TD\n" + "\n".join(f'    T{i+1}["{t}"] --> T{i+2}["{terms[i+1]}"]' for i in range(len(terms)-1)) + "\n```"
    return f"""
## 📄 {title}
**Authors:** {authors}  
**Date:** {date}  
**Words:** Title: {wct}, Summary: {sw}  
**Links:** [Abstract]({url}) | [PDF]({pdf_url})  
**Terms:** {', '.join(terms)}  
**ROUGE:** {rouge}%  
### 🎤 TTF Read Aloud
- **Title:** {title}  
- **Terms:** {', '.join(terms)}  
- **ROUGE:** {rouge}%  
#### Concepts Graph
{mermaid}
---
"""

async def create_paper_audio_files(papers, query):
    for p in papers:
        audio_text = clean_text_for_tts(f"{p['title']} by {p['authors']}. {p['summary']}")
        p['full_audio'], _ = await async_edge_tts_generate(audio_text, st.session_state['tts_voice'], p['authors'])
        if p['full_audio']:
            p['download_base64'] = get_download_link(p['full_audio'])

def save_vote(file, item, user_hash):
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    entry = f"[{timestamp}] {user_hash} voted for {item}"
    try:
        with open(file, 'a') as f:
            f.write(f"{entry}\n")
        with open(HISTORY_FILE, 'a') as f:
            f.write(f"- {timestamp} - User {user_hash} voted for {item}\n")
        return True
    except Exception as e:
        print(f"Vote save flop: {e}")
        return False

def load_votes(file):
    if not os.path.exists(file):
        with open(file, 'w') as f:
            f.write("# Vote Tally\n\nNo votes yet - get clicking! 🖱️\n")
    try:
        with open(file, 'r') as f:
            lines = f.read().strip().split('\n')
        votes = {}
        for line in lines[2:]:
            if line.strip() and 'voted for' in line:
                item = line.split('voted for ')[1]
                votes[item] = votes.get(item, 0) + 1
        return votes
    except Exception as e:
        print(f"Vote load oopsie: {e}")
        return {}

def generate_user_hash():
    if 'user_hash' not in st.session_state:
        session_id = str(random.getrandbits(128))
        hash_object = hashlib.md5(session_id.encode())
        st.session_state['user_hash'] = hash_object.hexdigest()[:8]
    return st.session_state['user_hash']

async def save_pasted_image(image, username):
    img_hash = hashlib.md5(image.tobytes()).hexdigest()[:8]
    if img_hash in st.session_state.image_hashes:
        return None
    timestamp = format_timestamp_prefix(username)
    filename = f"{timestamp}-{img_hash}.png"
    filepath = filename
    image.save(filepath, "PNG")
    st.session_state.image_hashes.add(img_hash)
    return filepath

# 📦 Zip Files
def create_zip_of_files(md_files, mp3_files, png_files, mp4_files, query):
    all_files = md_files + mp3_files + png_files + mp4_files
    if not all_files:
        return None
    terms = get_high_info_terms(" ".join([open(f, 'r', encoding='utf-8').read() if f.endswith('.md') else os.path.splitext(os.path.basename(f))[0].replace('_', ' ') for f in all_files] + [query]), 5)
    zip_name = f"{format_timestamp_prefix()}_{'-'.join(terms)[:20]}.zip"
    with zipfile.ZipFile(zip_name, 'w') as z:
        [z.write(f) for f in all_files]
    return zip_name

# 🎮 Main Interface
def main():
    init_session_state()
    load_mp3_viewer()
    saved_username = load_username()
    if saved_username and saved_username in FUN_USERNAMES:
        st.session_state.username = saved_username
    if not st.session_state.username:
        available = [n for n in FUN_USERNAMES if not any(f"{n} has joined" in l for l in asyncio.run(load_chat()))]
        st.session_state.username = random.choice(available or list(FUN_USERNAMES.keys()))
        st.session_state.tts_voice = FUN_USERNAMES[st.session_state.username]
        asyncio.run(save_chat_entry("System 🌟", f"{st.session_state.username} has joined {START_ROOM}!", "en-US-AriaNeural"))
    save_username(st.session_state.username)

    st.title(f"{Site_Name} for {st.session_state.username}")
    update_marquee_settings_ui()
    display_marquee(f"🚀 Welcome to {START_ROOM} | 🤖 {st.session_state.username}", st.session_state['marquee_settings'], "welcome")

    # Speech Component at Top Level
    mycomponent = components.declare_component("mycomponent", path="mycomponent")
    val = mycomponent(my_input_value="")
    if val and val != st.session_state.last_transcript:
        val_stripped = val.strip().replace('\n', ' ')
        if val_stripped:
            voice = FUN_USERNAMES.get(st.session_state.username, "en-US-AriaNeural")
            md_file, audio_file = asyncio.run(save_chat_entry(st.session_state.username, val_stripped, voice))
            if audio_file:
                play_and_download_audio(audio_file)
            st.rerun()

    tab_main = st.radio("Action:", ["🎤 Chat & Voice", "🔍 ArXiv", "📚 PDF to Audio"], horizontal=True, key="tab_main")
    useArxiv = st.checkbox("Search ArXiv", True, key="use_arxiv")
    useArxivAudio = st.checkbox("ArXiv Audio", False, key="use_arxiv_audio")
    st.checkbox("Autosend Chat", value=True, key="autosend")
    st.checkbox("Autosearch ArXiv", value=True, key="autosearch")

    # 🎤 Chat & Voice
    if tab_main == "🎤 Chat & Voice":
        st.subheader(f"{START_ROOM} Chat 💬")
        chat_content = asyncio.run(load_chat())
        chat_container = st.container()
        with chat_container:
            numbered_content = "\n".join(f"{i+1}. {line}" for i, line in enumerate(chat_content))
            st.code(numbered_content, language="python")

        message = st.text_input(f"Message as {st.session_state.username}", key="message_input")
        paste_result = paste_image_button("📋 Paste Image or Text", key="paste_button_msg")
        if paste_result.image_data is not None:
            voice = FUN_USERNAMES.get(st.session_state.username, "en-US-AriaNeural")
            if isinstance(paste_result.image_data, str):
                st.session_state.message_text = paste_result.image_data
                message = st.text_input(f"Message as {st.session_state.username}", key="message_input_paste", value=st.session_state.message_text)
            else:
                st.image(paste_result.image_data, caption="Pasted Image")
                filename = asyncio.run(save_pasted_image(paste_result.image_data, st.session_state.username))
                if filename:
                    st.session_state.pasted_image_data = filename
                    asr_text = f"User {st.session_state.username} requested analysis of an image uploaded at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
                    md_file_claude, audio_file_claude, claude_result = asyncio.run(perform_claude_search(asr_text, st.session_state.username))
                    if audio_file_claude:
                        play_and_download_audio(audio_file_claude)
                    md_file_arxiv, audio_file_arxiv = asyncio.run(perform_arxiv_search(asr_text, st.session_state.username, claude_result))
                    if audio_file_arxiv:
                        play_and_download_audio(audio_file_arxiv)
                    st.session_state.timer_start = time.time()
                    save_username(st.session_state.username)
                    st.rerun()

        if (message and message != st.session_state.last_message) or st.session_state.pasted_image_data:
            st.session_state.last_message = message
            col_send, col_claude, col_arxiv = st.columns([1, 1, 1])
            
            with col_send:
                if st.session_state.autosend or st.button("Send 🚀", key="send_button"):
                    voice = FUN_USERNAMES.get(st.session_state.username, "en-US-AriaNeural")
                    if message.strip():
                        md_file, audio_file = asyncio.run(save_chat_entry(st.session_state.username, message, voice, True))
                        if audio_file:
                            play_and_download_audio(audio_file)
                    if st.session_state.pasted_image_data:
                        asyncio.run(save_chat_entry(st.session_state.username, f"Pasted image: {st.session_state.pasted_image_data}", voice))
                        st.session_state.pasted_image_data = None
                    st.session_state.timer_start = time.time()
                    save_username(st.session_state.username)
                    st.rerun()

            with col_claude:
                if st.button("🧠 Claude", key="claude_button"):
                    voice = FUN_USERNAMES.get(st.session_state.username, "en-US-AriaNeural")
                    if message.strip():
                        md_file, audio_file, _ = asyncio.run(perform_claude_search(message, st.session_state.username))
                        if audio_file:
                            play_and_download_audio(audio_file)
                        st.session_state.timer_start = time.time()
                        save_username(st.session_state.username)
                        st.rerun()

            with col_arxiv:
                if st.button("🔍 ArXiv", key="arxiv_button"):
                    voice = FUN_USERNAMES.get(st.session_state.username, "en-US-AriaNeural")
                    if message.strip():
                        md_file, audio_file = asyncio.run(perform_arxiv_search(message, st.session_state.username))
                        if audio_file:
                            play_and_download_audio(audio_file)
                        st.session_state.timer_start = time.time()
                        save_username(st.session_state.username)
                        st.rerun()

    # 🔍 ArXiv
    elif tab_main == "🔍 ArXiv":
        st.subheader("🔍 Query ArXiv")
        q = st.text_input("🔍 Query:", key="arxiv_query")
        if q and q != st.session_state.last_query:
            st.session_state.last_query = q
            if st.session_state.autosearch or st.button("🔍 Run", key="arxiv_run"):
                result, papers = asyncio.run(perform_ai_lookup(q, useArxiv=useArxiv, useArxivAudio=useArxivAudio))
                st.markdown(f"### Query: {q}")
                for i, p in enumerate(papers, 1):
                    expander_label = f"{p['title']} | [arXiv Link]({p['url']})"
                    with st.expander(expander_label):
                        with open(p['md_file'], 'r', encoding='utf-8') as f:
                            content = f.read()
                        numbered_content = "\n".join(f"{j+1}. {line}" for j, line in enumerate(content.split('\n')))
                        st.code(numbered_content, language="python")

    # 📚 PDF to Audio
    elif tab_main == "📚 PDF to Audio":
        audio_processor = AudioProcessor()
        pdf_file = st.file_uploader("Choose PDF", "pdf", key="pdf_upload")
        max_pages = st.slider('Pages', 1, 100, 10, key="pdf_pages")
        if pdf_file:
            with st.spinner('Processing...'):
                texts, audios, total = process_pdf(pdf_file, max_pages, st.session_state['tts_voice'], audio_processor)
                for i, text in enumerate(texts):
                    with st.expander(f"Page {i+1}"):
                        st.markdown(text)
                        while i not in audios:
                            time.sleep(0.1)
                        if audios.get(i):
                            st.audio(audios[i])
                            st.markdown(get_download_link(audios[i], "mp3"), unsafe_allow_html=True)
                            voice = FUN_USERNAMES.get(st.session_state.username, "en-US-AriaNeural")
                            asyncio.run(save_chat_entry(st.session_state.username, f"PDF Page {i+1} converted to audio: {audios[i]}", voice))

    # Always Visible Media Gallery
    st.header("📸 Media Gallery")
    all_files = sorted(glob.glob("*.md") + glob.glob("*.mp3") + glob.glob("*.png") + glob.glob("*.mp4"), key=os.path.getmtime, reverse=True)
    md_files = [f for f in all_files if f.endswith('.md')]
    mp3_files = [f for f in all_files if f.endswith('.mp3')]
    png_files = [f for f in all_files if f.endswith('.png')]
    mp4_files = [f for f in all_files if f.endswith('.mp4')]

    st.subheader("All Submitted Text")
    all_md_content = concatenate_markdown_files()
    with st.expander("View All Markdown Content"):
        st.markdown(all_md_content)

    st.subheader("🎵 Audio (MP3)")
    for mp3 in mp3_files:
        with st.expander(os.path.basename(mp3)):
            st.audio(mp3)
            st.markdown(get_download_link(mp3, "mp3"), unsafe_allow_html=True)

    st.subheader("🖼️ Images (PNG)")
    for png in png_files:
        with st.expander(os.path.basename(png)):
            st.image(png, use_container_width=True)
            st.markdown(get_download_link(png, "png"), unsafe_allow_html=True)

    st.subheader("🎥 Videos (MP4)")
    for mp4 in mp4_files:
        with st.expander(os.path.basename(mp4)):
            st.video(mp4)
            st.markdown(get_download_link(mp4, "mp4"), unsafe_allow_html=True)

    # 🗂️ Sidebar with Dialog and Audio
    st.sidebar.subheader("Voice Settings")
    new_username = st.sidebar.selectbox("Change Name/Voice", list(FUN_USERNAMES.keys()), index=list(FUN_USERNAMES.keys()).index(st.session_state.username), key="username_select")
    if new_username != st.session_state.username:
        asyncio.run(save_chat_entry("System 🌟", f"{st.session_state.username} changed to {new_username}", "en-US-AriaNeural"))
        st.session_state.username, st.session_state.tts_voice = new_username, FUN_USERNAMES[new_username]
        st.session_state.timer_start = time.time()
        save_username(st.session_state.username)
        st.rerun()

    st.sidebar.markdown("### 💬 Chat Dialog")
    chat_content = asyncio.run(load_chat())
    with st.sidebar.expander("Chat History"):
        numbered_content = "\n".join(f"{i+1}. {line}" for i, line in enumerate(chat_content))
        st.code(numbered_content, language="python")

    st.sidebar.subheader("Vote Totals")
    chat_votes = load_votes(QUOTE_VOTES_FILE)
    image_votes = load_votes(IMAGE_VOTES_FILE)
    for item, count in chat_votes.items():
        st.sidebar.write(f"{item}: {count} votes")
    for image, count in image_votes.items():
        st.sidebar.write(f"{image}: {count} votes")

    st.sidebar.markdown("### 📂 File History")
    for f in all_files[:10]:
        st.sidebar.write(f"{FILE_EMOJIS.get(f.split('.')[-1], '📄')} {os.path.basename(f)}")
    if st.sidebar.button("⬇️ Zip All", key="zip_all"):
        zip_name = create_zip_of_files(md_files, mp3_files, png_files, mp4_files, "latest_query")
        if zip_name:
            st.sidebar.markdown(get_download_link(zip_name, "zip"), unsafe_allow_html=True)

    # Refresh Timer in Sidebar
    st.sidebar.subheader("Set Refresh Rate ⏳")
    st.markdown("""
        <style>
        .timer {
            font-size: 24px;
            color: #ffcc00;
            text-align: center;
            animation: pulse 1s infinite;
        }
        @keyframes pulse {
            0% { transform: scale(1); }
            50% { transform: scale(1.1); }
            100% { transform: scale(1); }
        }
        </style>
    """, unsafe_allow_html=True)

    refresh_rate = st.sidebar.slider("Refresh Rate (seconds)", min_value=1, max_value=300, value=st.session_state.refresh_rate, step=1)
    if refresh_rate != st.session_state.refresh_rate:
        st.session_state.refresh_rate = refresh_rate
        st.session_state.timer_start = time.time()
        save_username(st.session_state.username)

    col1, col2, col3 = st.sidebar.columns(3)
    with col1:
        if st.button("🐇 Small (1s)"):
            st.session_state.refresh_rate = 1
            st.session_state.timer_start = time.time()
            save_username(st.session_state.username)
    with col2:
        if st.button("🐢 Medium (5s)"):
            st.session_state.refresh_rate = 5
            st.session_state.timer_start = time.time()
            save_username(st.session_state.username)
    with col3:
        if st.button("🐘 Large (5m)"):
            st.session_state.refresh_rate = 300
            st.session_state.timer_start = time.time()
            save_username(st.session_state.username)

    timer_placeholder = st.sidebar.empty()
    start_time = st.session_state.timer_start
    remaining_time = int(st.session_state.refresh_rate - (time.time() - start_time))
    if remaining_time <= 0:
        st.session_state.timer_start = time.time()
        st.session_state.last_refresh = time.time()
        st.rerun()
    else:
        timer_placeholder.markdown(f"<p class='timer'>⏳ Next refresh in: {remaining_time} seconds</p>", unsafe_allow_html=True)

    # Start WebSocket server in a separate thread
    if not st.session_state.server_running and not st.session_state.server_task:
        st.session_state.server_task = threading.Thread(target=start_websocket_server, daemon=True)
        st.session_state.server_task.start()

if __name__ == "__main__":
    main()