import streamlit as st
import anthropic
import openai
import base64
from datetime import datetime
import plotly.graph_objects as go
import cv2
import glob
import json
import math
import os
import pytz
import random
import re
import requests
import streamlit.components.v1 as components
import textract
import time
import zipfile
from audio_recorder_streamlit import audio_recorder
from bs4 import BeautifulSoup
from collections import deque
from dotenv import load_dotenv
from gradio_client import Client, handle_file
from huggingface_hub import InferenceClient
from io import BytesIO
from moviepy.editor import VideoFileClip
from PIL import Image
from PyPDF2 import PdfReader
from urllib.parse import quote
from xml.etree import ElementTree as ET
from openai import OpenAI






# 1. 🚲BikeAI🏆 Configuration and Setup
Site_Name = '🚲BikeAI🏆 Claude and GPT Multi-Agent Research AI'
title = "🚲BikeAI🏆 Claude and GPT Multi-Agent Research AI"
helpURL = 'https://huggingface.co/awacke1'
bugURL = 'https://huggingface.co/spaces/awacke1'
icons = '🚲🏆'

st.set_page_config(
    page_title=title,
    page_icon=icons,
    layout="wide",
    initial_sidebar_state="auto",
    menu_items={
        'Get Help': helpURL,
        'Report a bug': bugURL,
        'About': title
    }
)

# 2. 🚲BikeAI🏆 Load environment variables and initialize clients
load_dotenv()

# OpenAI setup
openai.api_key = os.getenv('OPENAI_API_KEY')
if openai.api_key == None:
    openai.api_key = st.secrets['OPENAI_API_KEY']

openai_client = OpenAI(
    api_key=os.getenv('OPENAI_API_KEY'),
    organization=os.getenv('OPENAI_ORG_ID')
)

# 3.🚲BikeAI🏆 Claude setup
anthropic_key = os.getenv("ANTHROPIC_API_KEY_3")
if anthropic_key == None:
    anthropic_key = st.secrets["ANTHROPIC_API_KEY"]
claude_client = anthropic.Anthropic(api_key=anthropic_key)

# 4.🚲BikeAI🏆 Initialize session states
if 'transcript_history' not in st.session_state:
    st.session_state.transcript_history = []
if "chat_history" not in st.session_state:
    st.session_state.chat_history = []
if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = "gpt-4o-2024-05-13"
if "messages" not in st.session_state:
    st.session_state.messages = []
if 'last_voice_input' not in st.session_state:
    st.session_state.last_voice_input = ""

# 5. 🚲BikeAI🏆 HuggingFace AI setup
API_URL = os.getenv('API_URL')
HF_KEY = os.getenv('HF_KEY')
MODEL1 = "meta-llama/Llama-2-7b-chat-hf"
MODEL2 = "openai/whisper-small.en"
headers = {
    "Authorization": f"Bearer {HF_KEY}",
    "Content-Type": "application/json"
}

# 6. 🚲BikeAI🏆 Custom CSS
st.markdown("""
<style>
    .main {
        background: linear-gradient(to right, #1a1a1a, #2d2d2d);
        color: #ffffff;
    }
    .stMarkdown {
        font-family: 'Helvetica Neue', sans-serif;
    }
    .category-header {
        background: linear-gradient(45deg, #2b5876, #4e4376);
        padding: 20px;
        border-radius: 10px;
        margin: 10px 0;
    }
    .scene-card {
        background: rgba(0,0,0,0.3);
        padding: 15px;
        border-radius: 8px;
        margin: 10px 0;
        border: 1px solid rgba(255,255,255,0.1);
    }
    .media-gallery {
        display: grid;
        gap: 1rem;
        padding: 1rem;
    }
    .bike-card {
        background: rgba(255,255,255,0.05);
        border-radius: 10px;
        padding: 15px;
        transition: transform 0.3s;
    }
    .bike-card:hover {
        transform: scale(1.02);
    }
</style>
""", unsafe_allow_html=True)


# 7. Helper Functions
def generate_filename(prompt, file_type):
    """Generate a safe filename using the prompt and file type."""
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
    replaced_prompt = re.sub(r'[<>:"/\\|?*\n]', ' ', prompt)
    safe_prompt = re.sub(r'\s+', ' ', replaced_prompt).strip()[:230]
    return f"{safe_date_time}_{safe_prompt}.{file_type}"




# 8. Function to create and save a file (and avoid the black hole of lost data 🕳)
def create_file(filename, prompt, response, should_save=True):
    if not should_save:
        return
    with open(filename, 'w', encoding='utf-8') as file:
        file.write(prompt + "\n\n" + response)
def create_and_save_file(content, file_type="md", prompt=None, is_image=False, should_save=True):
    """Create and save file with proper handling of different types."""
    if not should_save:
        return None
    filename = generate_filename(prompt if prompt else content, file_type)
    with open(filename, "w", encoding="utf-8") as f:
        if is_image:
            f.write(content)
        else:
            f.write(prompt + "\n\n" + content if prompt else content)
    return filename

    
def get_download_link(file_path):
    """Create download link for file."""
    with open(file_path, "rb") as file:
        contents = file.read()
    b64 = base64.b64encode(contents).decode()
    return f'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}📂</a>'

@st.cache_resource
def SpeechSynthesis(result):
    """HTML5 Speech Synthesis."""
    documentHTML5 = f'''
    <!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">{result}</textarea>
        <br>
        <button onclick="readAloud()">🔊 Read Aloud</button>
    </body>
    </html>
    '''
    components.html(documentHTML5, width=1280, height=300)

# Media Processing Functions
def process_image(image_input, user_prompt):
    """Process image with GPT-4o vision."""
    if isinstance(image_input, str):
        with open(image_input, "rb") as image_file:
            image_input = image_file.read()
            
    base64_image = base64.b64encode(image_input).decode("utf-8")
    
    response = openai_client.chat.completions.create(
        model=st.session_state["openai_model"],
        messages=[
            {"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
            {"role": "user", "content": [
                {"type": "text", "text": user_prompt},
                {"type": "image_url", "image_url": {
                    "url": f"data:image/png;base64,{base64_image}"
                }}
            ]}
        ],
        temperature=0.0,
    )
    
    return response.choices[0].message.content

def process_audio(audio_input, text_input=''):
    """Process audio with Whisper and GPT."""
    if isinstance(audio_input, str):
        with open(audio_input, "rb") as file:
            audio_input = file.read()

    transcription = openai_client.audio.transcriptions.create(
        model="whisper-1",
        file=audio_input,
    )
    
    st.session_state.messages.append({"role": "user", "content": transcription.text})
    
    with st.chat_message("assistant"):
        st.markdown(transcription.text)
        SpeechSynthesis(transcription.text)
        
        filename = generate_filename(transcription.text, "wav")
        create_and_save_file(audio_input, "wav", transcription.text, True)

def process_video(video_path, seconds_per_frame=1):
    """Process video files for frame extraction and audio."""
    base64Frames = []
    video = cv2.VideoCapture(video_path)
    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = video.get(cv2.CAP_PROP_FPS)
    frames_to_skip = int(fps * seconds_per_frame)
    
    for frame_idx in range(0, total_frames, frames_to_skip):
        video.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
        success, frame = video.read()
        if not success:
            break
        _, buffer = cv2.imencode(".jpg", frame)
        base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
    
    video.release()
    
    # Extract audio
    base_video_path = os.path.splitext(video_path)[0]
    audio_path = f"{base_video_path}.mp3"
    try:
        video_clip = VideoFileClip(video_path)
        video_clip.audio.write_audiofile(audio_path)
        video_clip.close()
    except:
        st.warning("No audio track found in video")
        audio_path = None
    
    return base64Frames, audio_path

def process_video_with_gpt(video_input, user_prompt):
    """Process video with GPT-4o vision."""
    base64Frames, audio_path = process_video(video_input)
    
    response = openai_client.chat.completions.create(
        model=st.session_state["openai_model"],
        messages=[
            {"role": "system", "content": "Analyze the video frames and provide a detailed description."},
            {"role": "user", "content": [
                {"type": "text", "text": user_prompt},
                *[{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{frame}"}}
                  for frame in base64Frames]
            ]}
        ]
    )
    
    return response.choices[0].message.content


def extract_urls(text):
    try:
        date_pattern = re.compile(r'### (\d{2} \w{3} \d{4})')
        abs_link_pattern = re.compile(r'\[(.*?)\]\((https://arxiv\.org/abs/\d+\.\d+)\)')
        pdf_link_pattern = re.compile(r'\[⬇️\]\((https://arxiv\.org/pdf/\d+\.\d+)\)')
        title_pattern = re.compile(r'### \d{2} \w{3} \d{4} \| \[(.*?)\]')
        date_matches = date_pattern.findall(text)
        abs_link_matches = abs_link_pattern.findall(text)
        pdf_link_matches = pdf_link_pattern.findall(text)
        title_matches = title_pattern.findall(text)

        # markdown with the extracted fields
        markdown_text = ""
        for i in range(len(date_matches)):
            date = date_matches[i]
            title = title_matches[i]
            abs_link = abs_link_matches[i][1]
            pdf_link = pdf_link_matches[i]
            markdown_text += f"**Date:** {date}\n\n"
            markdown_text += f"**Title:** {title}\n\n"
            markdown_text += f"**Abstract Link:** [{abs_link}]({abs_link})\n\n"
            markdown_text += f"**PDF Link:** [{pdf_link}]({pdf_link})\n\n"
            markdown_text += "---\n\n"
        return markdown_text
    
    except:
        st.write('.')
        return ''


def search_arxiv(query):
    
    st.write("Performing AI Lookup...")
    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    
    result1 = client.predict(
        prompt=query,
        llm_model_picked="mistralai/Mixtral-8x7B-Instruct-v0.1",
        stream_outputs=True,
        api_name="/ask_llm"
    )
    st.markdown("### Mixtral-8x7B-Instruct-v0.1 Result")
    st.markdown(result1)

    result2 = client.predict(
        prompt=query,
        llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
        stream_outputs=True,
        api_name="/ask_llm"
    )
    st.markdown("### Mistral-7B-Instruct-v0.2 Result")
    st.markdown(result2)
    combined_result = f"{result1}\n\n{result2}"
    return combined_result

    #return responseall


# Function to generate a filename based on prompt and time (because names matter 🕒)
def generate_filename(prompt, file_type):
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
    safe_prompt = re.sub(r'\W+', '_', prompt)[:90]
    return f"{safe_date_time}_{safe_prompt}.{file_type}"

# Function to create and save a file (and avoid the black hole of lost data 🕳)
def create_file(filename, prompt, response):
    with open(filename, 'w', encoding='utf-8') as file:
        file.write(prompt + "\n\n" + response)

    
def perform_ai_lookup(query):
    start_time = time.strftime("%Y-%m-%d %H:%M:%S")
    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    response1 = client.predict(
        query,
        20,
        "Semantic Search",
        "mistralai/Mixtral-8x7B-Instruct-v0.1",
        api_name="/update_with_rag_md"
    )
    Question = '### 🔎 ' + query + '\r\n'  # Format for markdown display with links
    References =  response1[0]  
    ReferenceLinks = extract_urls(References)

    RunSecondQuery = True
    results=''
    if RunSecondQuery:
        # Search 2 - Retrieve the Summary with Papers Context and Original Query
        response2 = client.predict(
            query,
            "mistralai/Mixtral-8x7B-Instruct-v0.1",
            True,
            api_name="/ask_llm"
        )
        if len(response2) > 10:
            Answer = response2
            SpeechSynthesis(Answer)
            # Restructure results to follow format of Question, Answer, References, ReferenceLinks
            results = Question + '\r\n' + Answer + '\r\n' + References + '\r\n' + ReferenceLinks
            st.markdown(results)

    st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete')
    end_time = time.strftime("%Y-%m-%d %H:%M:%S")
    start_timestamp = time.mktime(time.strptime(start_time, "%Y-%m-%d %H:%M:%S"))
    end_timestamp = time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S"))
    elapsed_seconds = end_timestamp - start_timestamp
    st.write(f"Start time: {start_time}")
    st.write(f"Finish time: {end_time}")
    st.write(f"Elapsed time: {elapsed_seconds:.2f} seconds")

    
    filename = generate_filename(query, "md")
    create_file(filename, query, results)
    return results

# Chat Processing Functions
def process_with_gpt(text_input):
    """Process text with GPT-4o."""
    if text_input:
        st.session_state.messages.append({"role": "user", "content": text_input})
        
        with st.chat_message("user"):
            st.markdown(text_input)
        
        with st.chat_message("assistant"):
            completion = openai_client.chat.completions.create(
                model=st.session_state["openai_model"],
                messages=[
                    {"role": m["role"], "content": m["content"]}
                    for m in st.session_state.messages
                ],
                stream=False
            )
            return_text = completion.choices[0].message.content
            st.write("GPT-4o: " + return_text)
            
            #filename = generate_filename(text_input, "md")
            filename = generate_filename("GPT-4o: " + return_text, "md")
            create_file(filename, text_input, return_text)
            st.session_state.messages.append({"role": "assistant", "content": return_text})
            return return_text

def process_with_claude(text_input):
    """Process text with Claude."""
    if text_input:

        with st.chat_message("user"):
            st.markdown(text_input)

        with st.chat_message("assistant"):
            response = claude_client.messages.create(
                model="claude-3-sonnet-20240229",
                max_tokens=1000,
                messages=[
                    {"role": "user", "content": text_input}
                ]
            )
            response_text = response.content[0].text
            st.write("Claude: " + response_text)
            
            #filename = generate_filename(text_input, "md")
            filename = generate_filename("Claude: " + response_text, "md")
            create_file(filename, text_input, response_text)
            
            st.session_state.chat_history.append({
                "user": text_input,
                "claude": response_text
            })
            return response_text

# File Management Functions
def load_file(file_name):
    """Load file content."""
    with open(file_name, "r", encoding='utf-8') as file:
        content = file.read()
    return content

def create_zip_of_files(files):
    """Create zip archive of files."""
    zip_name = "all_files.zip"
    with zipfile.ZipFile(zip_name, 'w') as zipf:
        for file in files:
            zipf.write(file)
    return zip_name



def get_media_html(media_path, media_type="video", width="100%"):
    """Generate HTML for media player."""
    media_data = base64.b64encode(open(media_path, 'rb').read()).decode()
    if media_type == "video":
        return f'''
        <video width="{width}" controls autoplay muted loop>
            <source src="data:video/mp4;base64,{media_data}" type="video/mp4">
            Your browser does not support the video tag.
        </video>
        '''
    else:  # audio
        return f'''
        <audio controls style="width: {width};">
            <source src="data:audio/mpeg;base64,{media_data}" type="audio/mpeg">
            Your browser does not support the audio element.
        </audio>
        '''

def create_media_gallery():
    """Create the media gallery interface."""
    st.header("🎬 Media Gallery")
    
    tabs = st.tabs(["🖼️ Images", "🎵 Audio", "🎥 Video"])
    
    with tabs[0]:
        image_files = glob.glob("*.png") + glob.glob("*.jpg")
        if image_files:
            num_cols = st.slider("Number of columns", 1, 5, 3)
            cols = st.columns(num_cols)
            for idx, image_file in enumerate(image_files):
                with cols[idx % num_cols]:
                    img = Image.open(image_file)
                    st.image(img, use_container_width=True)
                    
                    # Add GPT vision analysis option
                    if st.button(f"Analyze {os.path.basename(image_file)}"):
                        analysis = process_image(image_file, 
                                              "Describe this image in detail and identify key elements.")
                        st.markdown(analysis)
    
    with tabs[1]:
        audio_files = glob.glob("*.mp3") + glob.glob("*.wav")
        for audio_file in audio_files:
            with st.expander(f"🎵 {os.path.basename(audio_file)}"):
                st.markdown(get_media_html(audio_file, "audio"), unsafe_allow_html=True)
                if st.button(f"Transcribe {os.path.basename(audio_file)}"):
                    with open(audio_file, "rb") as f:
                        transcription = process_audio(f)
                        st.write(transcription)
    
    with tabs[2]:
        video_files = glob.glob("*.mp4")
        for video_file in video_files:
            with st.expander(f"🎥 {os.path.basename(video_file)}"):
                st.markdown(get_media_html(video_file, "video"), unsafe_allow_html=True)
                if st.button(f"Analyze {os.path.basename(video_file)}"):
                    analysis = process_video_with_gpt(video_file, 
                                                    "Describe what's happening in this video.")
                    st.markdown(analysis)
    


def display_file_manager():
    """Display file management sidebar with guaranteed unique button keys."""
    st.sidebar.title("📁 File Management")
    
    all_files = glob.glob("*.md")
    all_files.sort(reverse=True)

    if st.sidebar.button("🗑 Delete All", key="delete_all_files_button"):
        for file in all_files:
            os.remove(file)
        st.rerun()

    if st.sidebar.button("⬇️ Download All", key="download_all_files_button"):
        zip_file = create_zip_of_files(all_files)
        st.sidebar.markdown(get_download_link(zip_file), unsafe_allow_html=True)

    # Create unique keys using file attributes
    for idx, file in enumerate(all_files):
        # Get file stats for unique identification
        file_stat = os.stat(file)
        unique_id = f"{idx}_{file_stat.st_size}_{file_stat.st_mtime}"
        
        col1, col2, col3, col4 = st.sidebar.columns([1,3,1,1])
        with col1:
            if st.button("🌐", key=f"view_{unique_id}"):
                st.session_state.current_file = file
                st.session_state.file_content = load_file(file)
        with col2:
            st.markdown(get_download_link(file), unsafe_allow_html=True)
        with col3:
            if st.button("📂", key=f"edit_{unique_id}"):
                st.session_state.current_file = file
                st.session_state.file_content = load_file(file)
        with col4:
            if st.button("🗑", key=f"delete_{unique_id}"):
                os.remove(file)
                st.rerun()


speech_recognition_html = """
<!DOCTYPE html>
<html>
<head>
    <style>
        body { font-family: sans-serif; padding: 20px; }
        button { padding: 10px 20px; margin: 10px 5px; }
        #status { margin: 10px 0; padding: 10px; background: #e8f5e9; }
        #output { 
            white-space: pre-wrap; 
            padding: 15px; 
            background: #f5f5f5; 
            margin: 10px 0; 
            min-height: 100px; 
            max-height: 300px; 
            overflow-y: auto;
        }
    </style>
</head>
<body>
    <div>
        <button id="startBtn">Start</button>
        <button id="stopBtn" disabled>Stop</button>
        <button id="clearBtn">Clear</button>
    </div>
    <div id="status">Ready</div>
    <div id="output"></div>

    <script>
        const startBtn = document.getElementById('startBtn');
        const stopBtn = document.getElementById('stopBtn');
        const clearBtn = document.getElementById('clearBtn');
        const status = document.getElementById('status');
        const output = document.getElementById('output');
        let recognition;
        let fullTranscript = '';

        if ('webkitSpeechRecognition' in window) {
            recognition = new webkitSpeechRecognition();
            recognition.continuous = true;
            recognition.interimResults = true;

            recognition.onstart = () => {
                status.textContent = 'Listening...';
                startBtn.disabled = true;
                stopBtn.disabled = false;
            };

            recognition.onend = () => {
                status.textContent = 'Click Start to begin';
                startBtn.disabled = false;
                stopBtn.disabled = true;
            };

            recognition.onresult = (event) => {
                let interim = '';
                let final = '';

                for (let i = event.resultIndex; i < event.results.length; i++) {
                    if (event.results[i].isFinal) {
                        final += event.results[i][0].transcript + ' ';
                        fullTranscript += event.results[i][0].transcript + ' ';
                    } else {
                        interim += event.results[i][0].transcript;
                    }
                }

                if (final) {
                    // Send to Streamlit
                    window.parent.postMessage({
                        type: 'final_transcript',
                        text: fullTranscript
                    }, '*');
                }
                
                output.textContent = fullTranscript + interim;
                output.scrollTop = output.scrollHeight;
            };

            recognition.onerror = (event) => {
                status.textContent = 'Error: ' + event.error;
                startBtn.disabled = false;
                stopBtn.disabled = true;
            };

            // Button handlers
            startBtn.onclick = () => {
                try {
                    recognition.start();
                } catch (e) {
                    status.textContent = 'Error starting: ' + e;
                }
            };

            stopBtn.onclick = () => recognition.stop();

            clearBtn.onclick = () => {
                fullTranscript = '';
                output.textContent = '';
                window.parent.postMessage({
                    type: 'final_transcript',
                    text: ''
                }, '*');
            };

            // Auto-start
            document.addEventListener('DOMContentLoaded', () => {
                setTimeout(() => startBtn.click(), 1000);
            });

        } else {
            status.textContent = 'Speech recognition not supported in this browser';
            startBtn.disabled = true;
            stopBtn.disabled = true;
        }
    </script>
</body>
</html>
"""

# Helper Functions
def generate_filename(prompt, file_type):
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
    replaced_prompt = re.sub(r'[<>:"/\\|?*\n]', ' ', prompt)
    safe_prompt = re.sub(r'\s+', ' ', replaced_prompt).strip()[:230]
    return f"{safe_date_time}_{safe_prompt}.{file_type}"

# File Management Functions
def load_file(file_name):
    """Load file content."""
    with open(file_name, "r", encoding='utf-8') as file:
        content = file.read()
    return content

def create_zip_of_files(files):
    """Create zip archive of files."""
    zip_name = "all_files.zip"
    with zipfile.ZipFile(zip_name, 'w') as zipf:
        for file in files:
            zipf.write(file)
    return zip_name

def get_download_link(file):
    """Create download link for file."""
    with open(file, "rb") as f:
        contents = f.read()
    b64 = base64.b64encode(contents).decode()
    return f'<a href="data:file/txt;base64,{b64}" download="{os.path.basename(file)}">Download {os.path.basename(file)}📂</a>'

def display_file_manager():
    """Display file management sidebar."""
    st.sidebar.title("📁 File Management")
    
    all_files = glob.glob("*.md")
    all_files.sort(reverse=True)

    if st.sidebar.button("🗑 Delete All"):
        for file in all_files:
            os.remove(file)
        st.rerun()

    if st.sidebar.button("⬇️ Download All"):
        zip_file = create_zip_of_files(all_files)
        st.sidebar.markdown(get_download_link(zip_file), unsafe_allow_html=True)

    for file in all_files:
        col1, col2, col3, col4 = st.sidebar.columns([1,3,1,1])
        with col1:
            if st.button("🌐", key="view_"+file):
                st.session_state.current_file = file
                st.session_state.file_content = load_file(file)
        with col2:
            st.markdown(get_download_link(file), unsafe_allow_html=True)
        with col3:
            if st.button("📂", key="edit_"+file):
                st.session_state.current_file = file
                st.session_state.file_content = load_file(file)
        with col4:
            if st.button("🗑", key="delete_"+file):
                os.remove(file)
                st.rerun()

def create_media_gallery():
    """Create the media gallery interface."""
    st.header("🎬 Media Gallery")
    
    tabs = st.tabs(["🖼️ Images", "🎵 Audio", "🎥 Video"])
    
    with tabs[0]:
        image_files = glob.glob("*.png") + glob.glob("*.jpg")
        if image_files:
            num_cols = st.slider("Number of columns", 1, 5, 3)
            cols = st.columns(num_cols)
            for idx, image_file in enumerate(image_files):
                with cols[idx % num_cols]:
                    img = Image.open(image_file)
                    st.image(img, use_container_width=True)
                    
                    # Add GPT vision analysis option
                    if st.button(f"Analyze {os.path.basename(image_file)}"):
                        analysis = process_image(image_file, 
                                              "Describe this image in detail and identify key elements.")
                        st.markdown(analysis)
    
    with tabs[1]:
        audio_files = glob.glob("*.mp3") + glob.glob("*.wav")
        for audio_file in audio_files:
            with st.expander(f"🎵 {os.path.basename(audio_file)}"):
                st.markdown(get_media_html(audio_file, "audio"), unsafe_allow_html=True)
                if st.button(f"Transcribe {os.path.basename(audio_file)}"):
                    with open(audio_file, "rb") as f:
                        transcription = process_audio(f)
                        st.write(transcription)
    
    with tabs[2]:
        video_files = glob.glob("*.mp4")
        for video_file in video_files:
            with st.expander(f"🎥 {os.path.basename(video_file)}"):
                st.markdown(get_media_html(video_file, "video"), unsafe_allow_html=True)
                if st.button(f"Analyze {os.path.basename(video_file)}"):
                    analysis = process_video_with_gpt(video_file, 
                                                    "Describe what's happening in this video.")
                    st.markdown(analysis)
    


def get_media_html(media_path, media_type="video", width="100%"):
    """Generate HTML for media player."""
    media_data = base64.b64encode(open(media_path, 'rb').read()).decode()
    if media_type == "video":
        return f'''
        <video width="{width}" controls autoplay muted loop>
            <source src="data:video/mp4;base64,{media_data}" type="video/mp4">
            Your browser does not support the video tag.
        </video>
        '''
    else:  # audio
        return f'''
        <audio controls style="width: {width};">
            <source src="data:audio/mpeg;base64,{media_data}" type="audio/mpeg">
            Your browser does not support the audio element.
        </audio>
        '''

@st.cache_resource
def set_transcript(text):
    """Set transcript in session state."""
    st.session_state.voice_transcript = text
    if tab_main == "🎤 Voice Input":
        st.subheader("Voice Recognition")
        
        if 'voice_transcript' not in st.session_state:
            st.session_state.voice_transcript = ""

        # Speech recognition component
        st.components.v1.html(speech_recognition_html, height=400)

        # Transcript receiver
        transcript_receiver = st.components.v1.html("""
            <script>
                window.addEventListener('message', function(e) {
                    if (e.data && e.data.type === 'final_transcript') {
                        window.Streamlit.setComponentValue(e.data.text);
                    }
                });
            </script>
        """, height=0)

        # Update session state if new transcript received
        if transcript_receiver:
            st.session_state.voice_transcript = transcript_receiver

        # Display transcript
        st.markdown("### Processed Voice Input:")
        st.text_area(
            "Voice Transcript",
            value=st.session_state.voice_transcript if isinstance(st.session_state.voice_transcript, str) else "",
            height=100
        )

        
        # Process buttons
        col1, col2, col3 = st.columns(3)
        
        with col1:
            if st.button("Process with GPT"):
                if st.session_state.voice_transcript:
                    st.markdown("### GPT Response:")
                    gpt_response = process_with_gpt(st.session_state.voice_transcript)
                    st.markdown(gpt_response)
        
        with col2:
            if st.button("Process with Claude"):
                if st.session_state.voice_transcript:
                    st.markdown("### Claude Response:")
                    claude_response = process_with_claude(st.session_state.voice_transcript)
                    st.markdown(claude_response)
        
        with col3:
            if st.button("Clear Transcript"):
                st.session_state.voice_transcript = ""
                st.experimental_rerun()

        # Show ArXiv search option if there's a transcript
        if st.session_state.voice_transcript:
            if st.button("Search ArXiv"):
                st.markdown("### ArXiv Search Results:")
                arxiv_results = perform_ai_lookup(st.session_state.voice_transcript)
                st.markdown(arxiv_results)
    
    elif tab_main == "📝 File Editor":
        if hasattr(st.session_state, 'current_file'):
            st.subheader(f"Editing: {st.session_state.current_file}")
            new_content = st.text_area("Content:", st.session_state.file_content, height=300)
            if st.button("Save Changes"):
                with open(st.session_state.current_file, 'w', encoding='utf-8') as file:
                    file.write(new_content)
                st.success("File updated successfully!")

    # Always show file manager in sidebar
    display_file_manager()
      
if __name__ == "__main__":
    main()