Update app.py

app.py

@@ -3,33 +3,16 @@ 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 os
 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 gradio_client import Client
 from PIL import Image
-from PyPDF2 import PdfReader
-from urllib.parse import quote
-from xml.etree import ElementTree as ET
-from openai import OpenAI
+import glob
+import streamlit.components.v1 as components
 
 # 1. Configuration and Setup
 Site_Name = '🚲BikeAI🏆 Claude and GPT Multi-Agent Research AI'
@@ -51,895 +34,89 @@ st.set_page_config(
 )
 
 # 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"
-}
+openai_client = openai.OpenAI(api_key=openai.api_key)
+claude_client = anthropic.Anthropic(api_key=anthropic_key)
 
 # Initialize session states
+if 'voice_transcript' not in st.session_state:
+    st.session_state.voice_transcript = ""
 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
+# 3. 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;
+                        finalTranscript += event.results[i][0].transcript + '\\n';
                     }
                 }
-
-                if (finalTranscript || (Date.now() - lastUpdateTime > 5000)) {
-                    if (finalTranscript) {
-                        fullTranscript += finalTranscript;
-                        // Send to Streamlit
-                        window.parent.postMessage({
-                            type: 'final_transcript',
-                            text: finalTranscript
-                        }, '*');
-                    }
-                    lastUpdateTime = Date.now();
+                if (finalTranscript) {
+                    fullTranscript += finalTranscript;
+                    window.parent.postMessage({
+                        type: 'final_transcript',
+                        text: finalTranscript
+                    }, '*');
                 }
-
-                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;
-                }
-            };
+            recognition.start();
         }
     </script>
-</body>
+</head>
+<body></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 process_with_gpt(text_input):
+    if text_input:
+        completion = openai_client.Completion.create(
+            model="gpt-4o-2024-05-13",
+            messages=[{"role": "user", "content": text_input}],
+            max_tokens=500,
+        )
+        return completion.choices[0].text
 
-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 process_with_claude(text_input):
+    if text_input:
+        response = claude_client.Completion.create(
+            model="claude-3-sonnet-20240229",
+            messages=[{"role": "user", "content": text_input}],
+            max_tokens=1000,
+        )
+        return response.content[0].text
 
-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 perform_ai_lookup(query):
+    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+    response = client.predict(
+        prompt=query,
+        api_name="/ask_llm"
+    )
+    return response
 
 def display_file_manager():
     """Display file management sidebar."""
     st.sidebar.title("📁 File Management")
-    
     all_files = glob.glob("*.md")
     all_files.sort(reverse=True)
 
@@ -949,189 +126,61 @@ def display_file_manager():
         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)
+        with zipfile.ZipFile("all_files.zip", 'w') as zipf:
+            for file in all_files:
+                zipf.write(file)
+        st.sidebar.markdown(f'<a href="all_files.zip" download>Download All Files</a>', 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)
+        st.sidebar.write(file)
 
-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>
-        '''
-        
+# Main Function
 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)
-    
+    tab_main = st.radio("Choose Action:", ["🎤 Voice Input", "💬 Chat", "🔍 Search ArXiv"], horizontal=True)
+
     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]
+        st.components.v1.html(speech_recognition_html, height=300)
+
+        if st.session_state.voice_transcript:
+            st.text_area("Transcript", st.session_state.voice_transcript, height=100)
+
+            if st.button("Search with GPT"):
+                st.subheader("GPT-4o Response")
+                gpt_response = process_with_gpt(st.session_state.voice_transcript)
+                st.write(gpt_response)
+
+            if st.button("Search with Claude"):
+                st.subheader("Claude Response")
+                claude_response = process_with_claude(st.session_state.voice_transcript)
+                st.write(claude_response)
+
+            if st.button("Search ArXiv"):
+                st.subheader("ArXiv Search Results")
+                arxiv_results = perform_ai_lookup(st.session_state.voice_transcript)
+                st.write(arxiv_results)
+
+            if st.button("Clear Transcript"):
+                st.session_state.voice_transcript = ""
+
     elif tab_main == "💬 Chat":
-        # [Previous chat interface code]
-        pass
-    
-    elif tab_main == "📸 Media Gallery":
-        create_media_gallery()
-    
+        st.subheader("Chat")
+        user_input = st.text_area("Your Message", height=100)
+        if st.button("Send"):
+            if user_input:
+                gpt_response = process_with_gpt(user_input)
+                st.write("GPT Response:", gpt_response)
+                claude_response = process_with_claude(user_input)
+                st.write("Claude Response:", claude_response)
+
     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!")
+            results = perform_ai_lookup(query)
+            st.write(results)
 
-    # Always show file manager in sidebar
     display_file_manager()
 
 if __name__ == "__main__":
-    main()
+    main()