Create app.py

app.py

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+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. 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. 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. 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. 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. # HuggingFace 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"
+}
+
+# Initialize session states
+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 = []
+
+# 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)
+
+
+# Bike Collections
+bike_collections = {
+    "Celestial Collection 🌌": {
+        "Eclipse Vaulter": {
+            "prompt": """Cinematic shot of a sleek black mountain bike silhouetted against a total solar eclipse. 
+                     The corona creates an ethereal halo effect, with lens flares accentuating key points of the frame.
+                     Dynamic composition shows the bike mid-leap, with stardust particles trailing behind.
+                     Camera angle: Low angle, wide shot
+                     Lighting: Dramatic rim lighting from eclipse
+                     Color palette: Deep purples, cosmic blues, corona gold""",
+            "emoji": "🌑"
+        },
+        "Starlight Leaper": {
+            "prompt": """A black bike performing an epic leap under a vast Milky Way galaxy.
+                     Shimmering stars blanket the sky while the bike's wheels leave a trail of stardust.
+                     Camera angle: Wide-angle upward shot
+                     Lighting: Natural starlight with subtle rim lighting
+                     Color palette: Deep blues, silver highlights, cosmic purples""",
+            "emoji": "✨"
+        },
+        "Moonlit Hopper": {
+            "prompt": """A sleek black bike mid-hop over a moonlit meadow,
+                     the full moon illuminating the misty surroundings. Fireflies dance around the bike,
+                     and soft shadows create a serene yet dynamic atmosphere.
+                     Camera angle: Side profile with slight low angle
+                     Lighting: Soft moonlight with atmospheric fog
+                     Color palette: Silver blues, soft whites, deep shadows""",
+            "emoji": "🌙"
+        }
+    },
+    "Nature-Inspired Collection 🌲": {
+        "Shadow Grasshopper": {
+            "prompt": """A black bike jumping between forest paths,
+                     with dappled sunlight streaming through the canopy. Shadows dance on the bike's frame
+                     as it soars above mossy logs.
+                     Camera angle: Through-the-trees tracking shot
+                     Lighting: Natural forest lighting with sun rays
+                     Color palette: Forest greens, golden sunlight, deep shadows""",
+            "emoji": "🦗"
+        },
+        "Onyx Leapfrog": {
+            "prompt": """A bike with obsidian-black finish jumping over a sparkling creek,
+                     the reflection on the water broken into ripples by the leap. The surrounding forest
+                     is vibrant with greens and browns.
+                     Camera angle: Low angle from water level
+                     Lighting: Golden hour side lighting
+                     Color palette: Deep blacks, water blues, forest greens""",
+            "emoji": "🐸"
+        }
+    }
+}
+
+
+# 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}"
+
+
+
+
+# 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", "🎨 Scene Generator"])
+    
+    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)
+    
+    with tabs[3]:
+        for collection_name, bikes in bike_collections.items():
+            st.subheader(collection_name)
+            cols = st.columns(len(bikes))
+            
+            for idx, (bike_name, details) in enumerate(bikes.items()):
+                with cols[idx]:
+                    st.markdown(f"""
+                    <div class='bike-card'>
+                        <h3>{details['emoji']} {bike_name}</h3>
+                        <p>{details['prompt']}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+                    
+                    if st.button(f"Generate {bike_name} Scene"):
+                        prompt = details['prompt']
+                        # Here you could integrate with image generation API
+                        st.write(f"Generated scene description for {bike_name}:")
+                        st.write(prompt)
+
+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()
+
+
+def main():
+    st.sidebar.markdown("### 🚲BikeAI🏆 Claude and GPT Multi-Agent Research AI")
+    
+    # Main navigation
+    tab_main = st.radio("Choose Action:", 
+                        ["💬 Chat", "📸 Media Gallery", "🔍 Search ArXiv", "📝 File Editor"],
+                        horizontal=True)
+    
+    if tab_main == "💬 Chat":
+        # Model Selection
+        model_choice = st.sidebar.radio(
+            "Choose AI Model:",
+            ["GPT-4o", "Claude-3", "GPT+Claude+Arxiv"]
+        )
+        
+        # Chat Interface
+        user_input = st.text_area("Message:", height=100)
+        
+        if st.button("Send ��"):
+            if user_input:
+                if model_choice == "GPT-4o":
+                    gpt_response = process_with_gpt(user_input)
+                elif model_choice == "Claude-3":
+                    claude_response = process_with_claude(user_input)
+                else:  # Both
+                    col1, col2, col3 = st.columns(3)
+                    with col2:
+                        st.subheader("Claude-3.5 Sonnet:")
+                        try:
+                            claude_response = process_with_claude(user_input)
+                        except:
+                            st.write('Claude 3.5 Sonnet out of tokens.')
+                    with col1:
+                        st.subheader("GPT-4o Omni:")
+                        try:
+                            gpt_response = process_with_gpt(user_input)  
+                        except:
+                            st.write('GPT 4o out of tokens')
+                    with col3:
+                        st.subheader("Arxiv and Mistral Research:")
+                        with st.spinner("Searching ArXiv..."):
+                            #results = search_arxiv(user_input)
+                            results = perform_ai_lookup(user_input)
+                            
+                            st.markdown(results)
+                            
+        # Display Chat History
+        st.subheader("Chat History 📜")
+        tab1, tab2 = st.tabs(["Claude History", "GPT-4o History"])
+        
+        with tab1:
+            for chat in st.session_state.chat_history:
+                st.text_area("You:", chat["user"], height=100)
+                st.text_area("Claude:", chat["claude"], height=200)
+                st.markdown(chat["claude"])
+        
+        with tab2:
+            for message in st.session_state.messages:
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"])
+    
+    elif tab_main == "📸 Media Gallery":
+        create_media_gallery()
+    
+    elif tab_main == "🔍 Search ArXiv":
+        query = st.text_input("Enter your research query:")
+        if query:
+            with st.spinner("Searching ArXiv..."):
+                results = search_arxiv(query)
+                st.markdown(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()
+
+# Speech Recognition HTML Component
+speech_recognition_html = """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Continuous Speech Demo</title>
+    <style>
+        body { 
+            font-family: sans-serif; 
+            padding: 20px; 
+            max-width: 800px;
+            margin: 0 auto;
+        }
+        button { 
+            padding: 10px 20px; 
+            margin: 10px 5px;
+            font-size: 16px;
+        }
+        #status { 
+            margin: 10px 0;
+            padding: 10px;
+            background: #e8f5e9;
+            border-radius: 4px;
+        }
+        #output {
+            white-space: pre-wrap;
+            padding: 15px;
+            background: #f5f5f5;
+            border-radius: 4px;
+            margin: 10px 0;
+            min-height: 100px;
+            max-height: 400px;
+            overflow-y: auto;
+        }
+        .controls {
+            margin: 10px 0;
+        }
+    </style>
+</head>
+<body>
+    <div class="controls">
+        <button id="start">Start Listening</button>
+        <button id="stop" disabled>Stop Listening</button>
+        <button id="clear">Clear Text</button>
+    </div>
+    <div id="status">Ready</div>
+    <div id="output"></div>
+
+    <script>
+        if (!('webkitSpeechRecognition' in window)) {
+            alert('Speech recognition not supported');
+        } else {
+            const recognition = new webkitSpeechRecognition();
+            const startButton = document.getElementById('start');
+            const stopButton = document.getElementById('stop');
+            const clearButton = document.getElementById('clear');
+            const status = document.getElementById('status');
+            const output = document.getElementById('output');
+            let fullTranscript = '';
+            let lastUpdateTime = Date.now();
+
+            // Configure recognition
+            recognition.continuous = true;
+            recognition.interimResults = true;
+
+            // Function to start recognition
+            const startRecognition = () => {
+                try {
+                    recognition.start();
+                    status.textContent = 'Listening...';
+                    startButton.disabled = true;
+                    stopButton.disabled = false;
+                } catch (e) {
+                    console.error(e);
+                    status.textContent = 'Error: ' + e.message;
+                }
+            };
+
+            // Auto-start on load
+            window.addEventListener('load', () => {
+                setTimeout(startRecognition, 1000);
+            });
+
+            startButton.onclick = startRecognition;
+
+            stopButton.onclick = () => {
+                recognition.stop();
+                status.textContent = 'Stopped';
+                startButton.disabled = false;
+                stopButton.disabled = true;
+            };
+
+            clearButton.onclick = () => {
+                fullTranscript = '';
+                output.textContent = '';
+                window.parent.postMessage({
+                    type: 'clear_transcript',
+                }, '*');
+            };
+
+            recognition.onresult = (event) => {
+                let interimTranscript = '';
+                let finalTranscript = '';
+
+                for (let i = event.resultIndex; i < event.results.length; i++) {
+                    const transcript = event.results[i][0].transcript;
+                    if (event.results[i].isFinal) {
+                        finalTranscript += transcript + '\\n';
+                    } else {
+                        interimTranscript += transcript;
+                    }
+                }
+
+                if (finalTranscript || (Date.now() - lastUpdateTime > 5000)) {
+                    if (finalTranscript) {
+                        fullTranscript += finalTranscript;
+                        // Send to Streamlit
+                        window.parent.postMessage({
+                            type: 'final_transcript',
+                            text: finalTranscript
+                        }, '*');
+                    }
+                    lastUpdateTime = Date.now();
+                }
+
+                output.textContent = fullTranscript + (interimTranscript ? '... ' + interimTranscript : '');
+                output.scrollTop = output.scrollHeight;
+            };
+
+            recognition.onend = () => {
+                if (!stopButton.disabled) {
+                    try {
+                        recognition.start();
+                        console.log('Restarted recognition');
+                    } catch (e) {
+                        console.error('Failed to restart recognition:', e);
+                        status.textContent = 'Error restarting: ' + e.message;
+                        startButton.disabled = false;
+                        stopButton.disabled = true;
+                    }
+                }
+            };
+
+            recognition.onerror = (event) => {
+                console.error('Recognition error:', event.error);
+                status.textContent = 'Error: ' + event.error;
+                
+                if (event.error === 'not-allowed' || event.error === 'service-not-allowed') {
+                    startButton.disabled = false;
+                    stopButton.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", "🎨 Scene Generator"])
+    
+    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)
+    
+    with tabs[3]:
+        for collection_name, bikes in bike_collections.items():
+            st.subheader(collection_name)
+            cols = st.columns(len(bikes))
+            
+            for idx, (bike_name, details) in enumerate(bikes.items()):
+                with cols[idx]:
+                    st.markdown(f"""
+                    <div class='bike-card'>
+                        <h3>{details['emoji']} {bike_name}</h3>
+                        <p>{details['prompt']}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+                    
+                    if st.button(f"Generate {bike_name} Scene"):
+                        prompt = details['prompt']
+                        # Here you could integrate with image generation API
+                        st.write(f"Generated scene description for {bike_name}:")
+                        st.write(prompt)
+
+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 process_transcription_with_ai(text):
+    """Process transcribed text with all three AI models."""
+    results = {
+        "claude": None,
+        "gpt": None,
+        "arxiv": None
+    }
+    
+    try:
+        results["claude"] = process_with_claude(text)
+    except Exception as e:
+        st.error(f"Claude processing error: {e}")
+        
+    try:
+        results["gpt"] = process_with_gpt(text)
+    except Exception as e:
+        st.error(f"GPT processing error: {e}")
+        
+    try:
+        results["arxiv"] = perform_ai_lookup(text)
+    except Exception as e:
+        st.error(f"Arxiv processing error: {e}")
+        
+    return results
+
+def handle_speech_recognition_component():
+    """Handle the speech recognition component and AI processing."""
+    st.subheader("Voice Recognition with Multi-Modal Output")
+    
+    # Initialize state for transcribed text
+    if "transcribed_text" not in st.session_state:
+        st.session_state.transcribed_text = ""
+    
+    # Render the React component
+    component = components.declare_component(
+        "speech_recognition",
+        path="frontend/build"  # Update this path to match your React component location
+    )
+    
+    # Handle component events
+    component_result = component()
+    if component_result:
+        if component_result.get("type") == "process_ai":
+            text = component_result.get("text", "").strip()
+            if text:
+                with st.spinner("Processing with AI models..."):
+                    results = process_transcription_with_ai(text)
+                    
+                    # Display results in columns
+                    col1, col2, col3 = st.columns(3)
+                    with col1:
+                        st.subheader("GPT-4o Results")
+                        if results["gpt"]:
+                            st.markdown(results["gpt"])
+                    
+                    with col2:
+                        st.subheader("Claude Results")
+                        if results["claude"]:
+                            st.markdown(results["claude"])
+                    
+                    with col3:
+                        st.subheader("Arxiv Results")
+                        if results["arxiv"]:
+                            st.markdown(results["arxiv"])
+                                 
+
+    
+def main():
+    st.sidebar.markdown("### 🚲BikeAI🏆 Claude and GPT Multi-Agent Research AI")
+    
+    # Main navigation
+    tab_main = st.radio("Choose Action:", 
+                        ["🎤 Voice Input", "💬 Chat", "📸 Media Gallery", "🔍 Search ArXiv", "📝 File Editor"],
+                        horizontal=True)
+    
+    if tab_main == "🎤 Voice Input":
+        handle_speech_recognition_component()
+          
+    if tab_main == "🎤 Voice Input":
+        st.subheader("Voice Recognition")
+        
+        # Display speech recognition component
+        speech_component = st.components.v1.html(speech_recognition_html, height=400)
+        
+        # Handle speech recognition output
+        if speech_component:
+            try:
+                data = speech_component
+                if isinstance(data, dict):
+                    if data.get('type') == 'final_transcript':
+                        text = data.get('text', '').strip()
+                        if text:
+                            st.session_state.last_voice_input = text
+                            
+                            # Process voice input with AI
+                            st.subheader("AI Response to Voice Input:")
+                            
+                            col1, col2, col3 = st.columns(3)
+                            with col2:
+                                st.write("Claude-3.5 Sonnet:")
+                                try:
+                                    claude_response = process_with_claude(text)
+                                except:
+                                    st.write('Claude 3.5 Sonnet out of tokens.')
+                            with col1:
+                                st.write("GPT-4o Omni:")
+                                try:
+                                    gpt_response = process_with_gpt(text)
+                                except:
+                                    st.write('GPT 4o out of tokens')
+                            with col3:
+                                st.write("Arxiv and Mistral Research:")
+                                with st.spinner("Searching ArXiv..."):
+                                    results = perform_ai_lookup(text)
+                                    st.markdown(results)
+                    
+                    elif data.get('type') == 'clear_transcript':
+                        st.session_state.last_voice_input = ""
+                        st.experimental_rerun()
+                        
+            except Exception as e:
+                st.error(f"Error processing voice input: {e}")
+        
+        # Display last voice input
+        if st.session_state.last_voice_input:
+            st.text_area("Last Voice Input:", st.session_state.last_voice_input, height=100)
+    
+    # [Rest of the main function remains the same]
+    elif tab_main == "💬 Chat":
+        # [Previous chat interface code]
+        pass
+    
+    elif tab_main == "📸 Media Gallery":
+        create_media_gallery()
+    
+    elif tab_main == "🔍 Search ArXiv":
+        query = st.text_input("Enter your research query:")
+        if query:
+            with st.spinner("Searching ArXiv..."):
+                results = search_arxiv(query)
+                st.markdown(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()