Create backup11.app.py

backup11.app.py

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+import streamlit as st
+import anthropic, openai, base64, cv2, glob, json, math, os, pytz, random, re, requests, textract, time, zipfile
+import plotly.graph_objects as go
+import streamlit.components.v1 as components
+from datetime import datetime
+from audio_recorder_streamlit import audio_recorder
+from bs4 import BeautifulSoup
+from collections import defaultdict, deque, Counter
+from dotenv import load_dotenv
+from gradio_client import Client
+from huggingface_hub import InferenceClient
+from io import BytesIO
+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 extra_streamlit_components as stx
+from streamlit.runtime.scriptrunner import get_script_run_ctx
+import asyncio
+import edge_tts
+
+# 🎯 1. Core Configuration & Setup
+st.set_page_config(
+    page_title="🚲TalkingAIResearcher🏆",
+    page_icon="🚲🏆",
+    layout="wide",
+    initial_sidebar_state="auto",
+    menu_items={
+        'Get Help': 'https://huggingface.co/awacke1',
+        'Report a bug': 'https://huggingface.co/spaces/awacke1',
+        'About': "🚲TalkingAIResearcher🏆"
+    }
+)
+load_dotenv()
+
+# Add available English voices for Edge TTS
+EDGE_TTS_VOICES = [
+    "en-US-AriaNeural",  # Default voice
+    "en-US-GuyNeural", 
+    "en-US-JennyNeural",
+    "en-GB-SoniaNeural",
+    "en-GB-RyanNeural",
+    "en-AU-NatashaNeural",
+    "en-AU-WilliamNeural",
+    "en-CA-ClaraNeural",
+    "en-CA-LiamNeural"
+]
+
+# Initialize session state variables
+if 'tts_voice' not in st.session_state:
+    st.session_state['tts_voice'] = EDGE_TTS_VOICES[0]  # Default voice
+if 'audio_format' not in st.session_state:
+    st.session_state['audio_format'] = 'mp3'  # 🆕 Default audio format
+
+# 🔑 2. API Setup & Clients
+openai_api_key = os.getenv('OPENAI_API_KEY', "")
+anthropic_key = os.getenv('ANTHROPIC_API_KEY_3', "")
+xai_key = os.getenv('xai',"")
+if 'OPENAI_API_KEY' in st.secrets:
+    openai_api_key = st.secrets['OPENAI_API_KEY']
+if 'ANTHROPIC_API_KEY' in st.secrets:
+    anthropic_key = st.secrets["ANTHROPIC_API_KEY"]
+
+openai.api_key = openai_api_key
+claude_client = anthropic.Anthropic(api_key=anthropic_key)
+openai_client = OpenAI(api_key=openai.api_key, organization=os.getenv('OPENAI_ORG_ID'))
+HF_KEY = os.getenv('HF_KEY')
+API_URL = os.getenv('API_URL')
+
+# 📝 3. Session State Management
+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'] = ""
+if 'editing_file' not in st.session_state:
+    st.session_state['editing_file'] = None
+if 'edit_new_name' not in st.session_state:
+    st.session_state['edit_new_name'] = ""
+if 'edit_new_content' not in st.session_state:
+    st.session_state['edit_new_content'] = ""
+if 'viewing_prefix' not in st.session_state:
+    st.session_state['viewing_prefix'] = None
+if 'should_rerun' not in st.session_state:
+    st.session_state['should_rerun'] = False
+if 'old_val' not in st.session_state:
+    st.session_state['old_val'] = None
+if 'last_query' not in st.session_state:
+    st.session_state['last_query'] = ""  # 🆕 Store the last query for zip naming
+
+# 🎨 4. Custom CSS
+st.markdown("""
+<style>
+    .main { background: linear-gradient(to right, #1a1a1a, #2d2d2d); color: #fff; }
+    .stMarkdown { font-family: 'Helvetica Neue', sans-serif; }
+    .stButton>button {
+        margin-right: 0.5rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+FILE_EMOJIS = {
+    "md": "📝",
+    "mp3": "🎵",
+    "wav": "🔊"  # 🆕 Add emoji for WAV
+}
+
+# 🧠 5. High-Information Content Extraction
+def get_high_info_terms(text: str, top_n=10) -> list:
+    """Extract high-information terms from text, including key phrases."""
+    stop_words = set([
+        'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with',
+        'by', 'from', 'up', 'about', 'into', 'over', 'after', 'is', 'are', 'was', 'were',
+        'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would',
+        'should', 'could', 'might', 'must', 'shall', 'can', 'may', 'this', 'that', 'these',
+        'those', 'i', 'you', 'he', 'she', 'it', 'we', 'they', 'what', 'which', 'who',
+        'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most',
+        'other', 'some', 'such', 'than', 'too', 'very', 'just', 'there'
+    ])
+
+    key_phrases = [
+        'artificial intelligence', 'machine learning', 'deep learning', 'neural network',
+        'personal assistant', 'natural language', 'computer vision', 'data science',
+        'reinforcement learning', 'knowledge graph', 'semantic search', 'time series',
+        'large language model', 'transformer model', 'attention mechanism',
+        'autonomous system', 'edge computing', 'quantum computing', 'blockchain technology',
+        'cognitive science', 'human computer', 'decision making', 'arxiv search',
+        'research paper', 'scientific study', 'empirical analysis'
+    ]
+
+    # Extract bi-grams and uni-grams
+    words = re.findall(r'\b\w+(?:-\w+)*\b', text.lower())
+    bi_grams = [' '.join(pair) for pair in zip(words, words[1:])]
+    combined = words + bi_grams
+
+    # Filter out stop words and short words
+    filtered = [
+        term for term in combined
+        if term not in stop_words
+        and len(term.split()) <= 2  # Limit to uni-grams and bi-grams
+        and any(c.isalpha() for c in term)
+    ]
+
+    # Count frequencies
+    counter = Counter(filtered)
+    most_common = [term for term, freq in counter.most_common(top_n)]
+    return most_common
+
+def clean_text_for_filename(text: str) -> str:
+    """Remove punctuation and short filler words, return a compact string."""
+    text = text.lower()
+    text = re.sub(r'[^\w\s-]', '', text)
+    words = text.split()
+    stop_short = set(['the','and','for','with','this','that','from','just','very','then','been','only','also','about'])
+    filtered = [w for w in words if len(w)>3 and w not in stop_short]
+    return '_'.join(filtered)[:200]
+
+# 📁 6. File Operations
+def generate_filename(prompt, response, file_type="md"):
+    """
+    Generate filename with meaningful terms and short dense clips from prompt & response.
+    The filename should be about 150 chars total, include high-info terms, and a clipped snippet.
+    """
+    prefix = datetime.now().strftime("%y%m_%H%M") + "_"
+    combined = (prompt + " " + response).strip()
+    info_terms = get_high_info_terms(combined, top_n=10)
+    
+    # Include a short snippet from prompt and response
+    snippet = (prompt[:100] + " " + response[:100]).strip()
+    snippet_cleaned = clean_text_for_filename(snippet)
+    
+    # Combine info terms and snippet
+    name_parts = info_terms + [snippet_cleaned]
+    full_name = '_'.join(name_parts)
+
+    # Trim to ~150 chars
+    if len(full_name) > 150:
+        full_name = full_name[:150]
+    
+    filename = f"{prefix}{full_name}.{file_type}"
+    return filename
+
+def create_file(prompt, response, file_type="md"):
+    """Create file with intelligent naming"""
+    filename = generate_filename(prompt.strip(), response.strip(), file_type)
+    with open(filename, 'w', encoding='utf-8') as f:
+        f.write(prompt + "\n\n" + response)
+    return filename
+
+def get_download_link(file, file_type="zip"):
+    """Generate download link for file"""
+    with open(file, "rb") as f:
+        b64 = base64.b64encode(f.read()).decode()
+    if file_type == "zip":
+        return f'<a href="data:application/zip;base64,{b64}" download="{os.path.basename(file)}">📂 Download {os.path.basename(file)}</a>'
+    elif file_type == "mp3":
+        return f'<a href="data:audio/mpeg;base64,{b64}" download="{os.path.basename(file)}">🎵 Download {os.path.basename(file)}</a>'
+    elif file_type == "wav":
+        return f'<a href="data:audio/wav;base64,{b64}" download="{os.path.basename(file)}">🔊 Download {os.path.basename(file)}</a>'  # 🆕 WAV download link
+    elif file_type == "md":
+        return f'<a href="data:text/markdown;base64,{b64}" download="{os.path.basename(file)}">📝 Download {os.path.basename(file)}</a>'
+    else:
+        return f'<a href="data:application/octet-stream;base64,{b64}" download="{os.path.basename(file)}">Download {os.path.basename(file)}</a>'
+
+# 🔊 7. Audio Processing
+def clean_for_speech(text: str) -> str:
+    """Clean text for speech synthesis"""
+    text = text.replace("\n", " ")
+    text = text.replace("</s>", " ")
+    text = text.replace("#", "")
+    text = re.sub(r"\(https?:\/\/[^\)]+\)", "", text)
+    text = re.sub(r"\s+", " ", text).strip()
+    return text
+
+@st.cache_resource
+def speech_synthesis_html(result):
+    """Create HTML for speech synthesis"""
+    html_code = f"""
+    <html><body>
+    <script>
+    var msg = new SpeechSynthesisUtterance("{result.replace('"', '')}");
+    window.speechSynthesis.speak(msg);
+    </script>
+    </body></html>
+    """
+    components.html(html_code, height=0)
+
+async def edge_tts_generate_audio(text, voice="en-US-AriaNeural", rate=0, pitch=0, file_format="mp3"):
+    """Generate audio using Edge TTS"""
+    text = clean_for_speech(text)
+    if not text.strip():
+        return None
+    rate_str = f"{rate:+d}%"
+    pitch_str = f"{pitch:+d}Hz"
+    communicate = edge_tts.Communicate(text, voice, rate=rate_str, pitch=pitch_str)
+    out_fn = generate_filename(text, text, file_type=file_format)
+    await communicate.save(out_fn)
+    return out_fn
+
+def speak_with_edge_tts(text, voice="en-US-AriaNeural", rate=0, pitch=0, file_format="mp3"):
+    """Wrapper for edge TTS generation"""
+    return asyncio.run(edge_tts_generate_audio(text, voice, rate, pitch, file_format))
+
+def play_and_download_audio(file_path, file_type="mp3"):
+    """Play and provide download link for audio"""
+    if file_path and os.path.exists(file_path):
+        if file_type == "mp3":
+            st.audio(file_path)
+        elif file_type == "wav":
+            st.audio(file_path)
+        dl_link = get_download_link(file_path, file_type=file_type)
+        st.markdown(dl_link, unsafe_allow_html=True)
+
+# 🎬 8. Media Processing
+def process_image(image_path, user_prompt):
+    """Process image with GPT-4V"""
+    with open(image_path, "rb") as imgf:
+        image_data = imgf.read()
+    b64img = base64.b64encode(image_data).decode("utf-8")
+    resp = openai_client.chat.completions.create(
+        model=st.session_state["openai_model"],
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": [
+                {"type": "text", "text": user_prompt},
+                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64img}"}}
+            ]}
+        ],
+        temperature=0.0,
+    )
+    return resp.choices[0].message.content
+
+def process_audio_file(audio_path):
+    """Process audio with Whisper"""
+    with open(audio_path, "rb") as f:
+        transcription = openai_client.audio.transcriptions.create(model="whisper-1", file=f)
+    st.session_state.messages.append({"role": "user", "content": transcription.text})
+    return transcription.text
+
+def process_video(video_path, seconds_per_frame=1):
+    """Extract frames from video"""
+    vid = cv2.VideoCapture(video_path)
+    total = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = vid.get(cv2.CAP_PROP_FPS)
+    skip = int(fps*seconds_per_frame)
+    frames_b64 = []
+    for i in range(0, total, skip):
+        vid.set(cv2.CAP_PROP_POS_FRAMES, i)
+        ret, frame = vid.read()
+        if not ret: 
+            break
+        _, buf = cv2.imencode(".jpg", frame)
+        frames_b64.append(base64.b64encode(buf).decode("utf-8"))
+    vid.release()
+    return frames_b64
+
+def process_video_with_gpt(video_path, prompt):
+    """Analyze video frames with GPT-4V"""
+    frames = process_video(video_path)
+    resp = openai_client.chat.completions.create(
+        model=st.session_state["openai_model"],
+        messages=[
+            {"role":"system","content":"Analyze video frames."},
+            {"role":"user","content":[
+                {"type":"text","text":prompt},
+                *[{"type":"image_url","image_url":{"url":f"data:image/jpeg;base64,{fr}"}} for fr in frames]
+            ]}
+        ]
+    )
+    return resp.choices[0].message.content
+
+# 🤖 9. AI Model Integration
+
+def save_full_transcript(query, text):
+    """Save full transcript of Arxiv results as a file."""
+    create_file(query, text, "md")
+
+def parse_arxiv_refs(ref_text: str):
+    """
+    Parse papers by finding lines with two pipe characters as title lines.
+    Returns list of paper dictionaries with audio files.
+    """
+    if not ref_text:
+        return []
+
+    results = []
+    current_paper = {}
+    lines = ref_text.split('\n')
+    
+    for i, line in enumerate(lines):
+        # Check if this is a title line (contains exactly 2 pipe characters)
+        if line.count('|') == 2:
+            # If we have a previous paper, add it to results
+            if current_paper:
+                results.append(current_paper)
+                if len(results) >= 20:  # Limit to 20 papers
+                    break
+            
+            # Parse new paper header
+            try:
+                # Remove ** and split by |
+                header_parts = line.strip('* ').split('|')
+                date = header_parts[0].strip()
+                title = header_parts[1].strip()
+                # Extract arXiv URL if present
+                url_match = re.search(r'(https://arxiv.org/\S+)', line)
+                url = url_match.group(1) if url_match else f"paper_{len(results)}"
+                
+                current_paper = {
+                    'date': date,
+                    'title': title,
+                    'url': url,
+                    'authors': '',
+                    'summary': '',
+                    'content_start': i + 1  # Track where content begins
+                }
+            except Exception as e:
+                st.warning(f"Error parsing paper header: {str(e)}")
+                current_paper = {}
+                continue
+        
+        # If we have a current paper and this isn't a title line, add to content
+        elif current_paper:
+            if not current_paper['authors']:  # First line after title is authors
+                current_paper['authors'] = line.strip('* ')
+            else:  # Rest is summary
+                if current_paper['summary']:
+                    current_paper['summary'] += ' ' + line.strip()
+                else:
+                    current_paper['summary'] = line.strip()
+    
+    # Don't forget the last paper
+    if current_paper:
+        results.append(current_paper)
+    
+    return results[:20]  # Ensure we return maximum 20 papers
+
+def create_paper_audio_files(papers, input_question):
+    """
+    Create audio files for each paper's content and add file paths to paper dict.
+    Also, display each audio as it's generated.
+    """
+    # Collect all content for combined summary
+    combined_titles = []
+
+    for paper in papers:
+        try:
+            # Generate audio for full content only
+            full_text = f"{paper['title']} by {paper['authors']}. {paper['summary']}"
+            full_text = clean_for_speech(full_text)
+            # Determine file format based on user selection
+            file_format = st.session_state['audio_format']
+            full_file = speak_with_edge_tts(full_text, voice=st.session_state['tts_voice'], file_format=file_format)
+            paper['full_audio'] = full_file
+
+            # Display the audio immediately after generation
+            st.write(f"### {FILE_EMOJIS.get(file_format, '')} {os.path.basename(full_file)}")
+            play_and_download_audio(full_file, file_type=file_format)
+            
+            combined_titles.append(paper['title'])
+        
+        except Exception as e:
+            st.warning(f"Error generating audio for paper {paper['title']}: {str(e)}")
+            paper['full_audio'] = None
+
+    # After all individual audios, create a combined summary audio
+    if combined_titles:
+        combined_text = f"Here are the titles of the papers related to your query: {'; '.join(combined_titles)}. Your original question was: {input_question}"
+        file_format = st.session_state['audio_format']
+        combined_file = speak_with_edge_tts(combined_text, voice=st.session_state['tts_voice'], file_format=file_format)
+        st.write(f"### {FILE_EMOJIS.get(file_format, '')} Combined Summary Audio")
+        play_and_download_audio(combined_file, file_type=file_format)
+        papers.append({'title': 'Combined Summary', 'full_audio': combined_file})
+
+def display_papers(papers):
+    """
+    Display papers with their audio controls using URLs as unique keys.
+    """
+    st.write("## Research Papers")
+    papercount=0
+    for idx, paper in enumerate(papers):
+        papercount = papercount + 1
+        if (papercount<=20):
+            with st.expander(f"{papercount}. 📄 {paper['title']}", expanded=True):
+                st.markdown(f"**{paper['date']} | {paper['title']} | ⬇️**")
+                st.markdown(f"*{paper['authors']}*")
+                st.markdown(paper['summary'])
+                
+                # Single audio control for full content
+                if paper.get('full_audio'):
+                    st.write("📚 Paper Audio")
+                    file_ext = os.path.splitext(paper['full_audio'])[1].lower().strip('.')
+                    if file_ext == "mp3":
+                        st.audio(paper['full_audio'])
+                    elif file_ext == "wav":
+                        st.audio(paper['full_audio'])
+
+def perform_ai_lookup(q, vocal_summary=True, extended_refs=False, 
+                     titles_summary=True, full_audio=False):
+    """Perform Arxiv search with audio generation per paper."""
+    start = time.time()
+
+    # Query the HF RAG pipeline
+    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+    refs = client.predict(q, 20, "Semantic Search", 
+                         "mistralai/Mixtral-8x7B-Instruct-v0.1",
+                         api_name="/update_with_rag_md")[0]
+    r2 = client.predict(q, "mistralai/Mixtral-8x7B-Instruct-v0.1", 
+                       True, api_name="/ask_llm")
+
+    # Combine for final text output
+    result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
+    st.markdown(result)
+
+    # Parse and process papers
+    papers = parse_arxiv_refs(refs)
+    if papers:
+        create_paper_audio_files(papers, input_question=q)
+        display_papers(papers)
+    else:
+        st.warning("No papers found in the response.")
+
+    elapsed = time.time()-start
+    st.write(f"**Total Elapsed:** {elapsed:.2f} s")
+
+    # Save full transcript
+    create_file(q, result, "md")
+    return result
+
+def process_with_gpt(text):
+    """Process text with GPT-4"""
+    if not text: 
+        return
+    st.session_state.messages.append({"role":"user","content":text})
+    with st.chat_message("user"):
+        st.markdown(text)
+    with st.chat_message("assistant"):
+        c = openai_client.chat.completions.create(
+            model=st.session_state["openai_model"],
+            messages=st.session_state.messages,
+            stream=False
+        )
+        ans = c.choices[0].message.content
+        st.write("GPT-4o: " + ans)
+        create_file(text, ans, "md")
+        st.session_state.messages.append({"role":"assistant","content":ans})
+    return ans
+
+def process_with_claude(text):
+    """Process text with Claude"""
+    if not text: 
+        return
+    with st.chat_message("user"):
+        st.markdown(text)
+    with st.chat_message("assistant"):
+        r = claude_client.messages.create(
+            model="claude-3-sonnet-20240229",
+            max_tokens=1000,
+            messages=[{"role":"user","content":text}]
+        )
+        ans = r.content[0].text
+        st.write("Claude-3.5: " + ans)
+        create_file(text, ans, "md")
+        st.session_state.chat_history.append({"user":text,"claude":ans})
+    return ans
+
+# 📂 10. File Management
+def create_zip_of_files(md_files, mp3_files, wav_files, input_question):
+    """Create zip with intelligent naming based on top 10 common words."""
+    # Exclude 'readme.md'
+    md_files = [f for f in md_files if os.path.basename(f).lower() != 'readme.md']
+    all_files = md_files + mp3_files + wav_files
+    if not all_files:
+        return None
+
+    # Collect content for high-info term extraction
+    all_content = []
+    for f in all_files:
+        if f.endswith('.md'):
+            with open(f, 'r', encoding='utf-8') as file:
+                all_content.append(file.read())
+        elif f.endswith('.mp3') or f.endswith('.wav'):
+            # Replace underscores with spaces and extract basename without extension
+            basename = os.path.splitext(os.path.basename(f))[0]
+            words = basename.replace('_', ' ')
+            all_content.append(words)
+    
+    # Include the input question
+    all_content.append(input_question)
+    
+    combined_content = " ".join(all_content)
+    info_terms = get_high_info_terms(combined_content, top_n=10)
+    
+    timestamp = datetime.now().strftime("%y%m_%H%M")
+    name_text = '_'.join(term.replace(' ', '-') for term in info_terms[:10])
+    zip_name = f"{timestamp}_{name_text}.zip"
+    
+    with zipfile.ZipFile(zip_name,'w') as z:
+        for f in all_files:
+            z.write(f)
+    
+    return zip_name
+
+def load_files_for_sidebar():
+    """Load and group files for sidebar display"""
+    md_files = glob.glob("*.md")
+    mp3_files = glob.glob("*.mp3")
+    wav_files = glob.glob("*.wav")  # 🆕 Load WAV files
+
+    md_files = [f for f in md_files if os.path.basename(f).lower() != 'readme.md']
+    all_files = md_files + mp3_files + wav_files
+
+    groups = defaultdict(list)
+    for f in all_files:
+        # Treat underscores as spaces and split into words
+        words = os.path.basename(f).replace('_', ' ').split()
+        # Extract keywords from filename
+        keywords = get_high_info_terms(' '.join(words), top_n=5)
+        group_name = '_'.join(keywords) if keywords else 'Miscellaneous'
+        groups[group_name].append(f)
+
+    # Sort groups based on latest file modification time
+    sorted_groups = sorted(groups.items(), key=lambda x: max(os.path.getmtime(f) for f in x[1]), reverse=True)
+    return sorted_groups
+
+def extract_keywords_from_md(files):
+    """Extract keywords from markdown files"""
+    text = ""
+    for f in files:
+        if f.endswith(".md"):
+            c = open(f,'r',encoding='utf-8').read()
+            text += " " + c
+    return get_high_info_terms(text, top_n=5)
+
+def display_file_manager_sidebar(groups_sorted):
+    """Display file manager in sidebar"""
+    st.sidebar.title("🎵 Audio & Docs Manager")
+
+    all_md = []
+    all_mp3 = []
+    all_wav = []  # 🆕 List to hold WAV files
+    for group_name, files in groups_sorted:
+        for f in files:
+            if f.endswith(".md"):
+                all_md.append(f)
+            elif f.endswith(".mp3"):
+                all_mp3.append(f)
+            elif f.endswith(".wav"):
+                all_wav.append(f)  # 🆕 Append WAV files
+
+    top_bar = st.sidebar.columns(4)  # 🆕 Adjusted columns to accommodate WAV
+    with top_bar[0]:
+        if st.button("🗑 DelAllMD"):
+            for f in all_md:
+                os.remove(f)
+            st.session_state.should_rerun = True
+    with top_bar[1]:
+        if st.button("🗑 DelAllMP3"):
+            for f in all_mp3:
+                os.remove(f)
+            st.session_state.should_rerun = True
+    with top_bar[2]:
+        if st.button("🗑 DelAllWAV"):
+            for f in all_wav:
+                os.remove(f)
+            st.session_state.should_rerun = True
+    with top_bar[3]:
+        if st.button("⬇️ ZipAll"):
+            zip_name = create_zip_of_files(all_md, all_mp3, all_wav, input_question=st.session_state.get('last_query', ''))
+            if zip_name:
+                st.sidebar.markdown(get_download_link(zip_name, file_type="zip"), unsafe_allow_html=True)
+
+    for group_name, files in groups_sorted:
+        keywords_str = group_name.replace('_', ' ') if group_name else "No Keywords"
+        with st.sidebar.expander(f"{FILE_EMOJIS.get('md', '')} {group_name} Files ({len(files)}) - KW: {keywords_str}", expanded=True):
+            c1,c2 = st.columns(2)
+            with c1:
+                if st.button("👀ViewGrp", key="view_group_"+group_name):
+                    st.session_state.viewing_prefix = group_name
+            with c2:
+                if st.button("🗑DelGrp", key="del_group_"+group_name):
+                    for f in files:
+                        os.remove(f)
+                    st.success(f"Deleted group {group_name}!")
+                    st.session_state.should_rerun = True
+
+            for f in files:
+                fname = os.path.basename(f)
+                ctime = datetime.fromtimestamp(os.path.getmtime(f)).strftime("%Y-%m-%d %H:%M:%S")
+                st.write(f"**{fname}** - {ctime}")
+
+# 🎯 11. Main Application
+def main():
+    st.sidebar.markdown("### 🚲BikeAI🏆 Multi-Agent Research")
+    
+    # Add voice selector to sidebar
+    st.sidebar.markdown("### 🎤 Voice Settings")
+    selected_voice = st.sidebar.selectbox(
+        "Select TTS Voice:",
+        options=EDGE_TTS_VOICES,
+        index=EDGE_TTS_VOICES.index(st.session_state['tts_voice'])
+    )
+    
+    # Add audio format selector to sidebar
+    st.sidebar.markdown("### 🔊 Audio Format")
+    selected_format = st.sidebar.radio(
+        "Choose Audio Format:",
+        options=["MP3", "WAV"],
+        index=0  # Default to MP3
+    )
+    
+    # Update session state if voice or format changes
+    if selected_voice != st.session_state['tts_voice']:
+        st.session_state['tts_voice'] = selected_voice
+        st.rerun()
+    if selected_format.lower() != st.session_state['audio_format']:
+        st.session_state['audio_format'] = selected_format.lower()
+        st.rerun()
+
+    tab_main = st.radio("Action:",["🎤 Voice","📸 Media","🔍 ArXiv","📝 Editor"],horizontal=True)
+
+    mycomponent = components.declare_component("mycomponent", path="mycomponent")
+    val = mycomponent(my_input_value="Hello")
+
+    # Show input in a text box for editing if detected
+    if val:
+        val_stripped = val.replace('\\n', ' ')
+        edited_input = st.text_area("✏️ Edit Input:", value=val_stripped, height=100)
+        #edited_input = edited_input.replace('\n', ' ')
+        
+        run_option = st.selectbox("Model:", ["Arxiv", "GPT-4o", "Claude-3.5"])
+        col1, col2 = st.columns(2)
+        with col1:
+            autorun = st.checkbox("⚙ AutoRun", value=True)
+        with col2:
+            full_audio = st.checkbox("📚FullAudio", value=False, 
+                                     help="Generate full audio response")
+
+        input_changed = (val != st.session_state.old_val)
+
+        if autorun and input_changed:
+            st.session_state.old_val = val
+            st.session_state.last_query = edited_input  # Store the last query for zip naming
+            if run_option == "Arxiv":
+                perform_ai_lookup(edited_input, vocal_summary=True, extended_refs=False, 
+                                  titles_summary=True, full_audio=full_audio)
+            else:
+                if run_option == "GPT-4o":
+                    process_with_gpt(edited_input)
+                elif run_option == "Claude-3.5":
+                    process_with_claude(edited_input)
+        else:
+            if st.button("▶ Run"):
+                st.session_state.old_val = val
+                st.session_state.last_query = edited_input  # Store the last query for zip naming
+                if run_option == "Arxiv":
+                    perform_ai_lookup(edited_input, vocal_summary=True, extended_refs=False, 
+                                      titles_summary=True, full_audio=full_audio)
+                else:
+                    if run_option == "GPT-4o":
+                        process_with_gpt(edited_input)
+                    elif run_option == "Claude-3.5":
+                        process_with_claude(edited_input)
+
+    if tab_main == "🔍 ArXiv":
+        st.subheader("🔍 Query ArXiv")
+        q = st.text_input("🔍 Query:")
+
+        st.markdown("### 🎛 Options")
+        vocal_summary = st.checkbox("🎙ShortAudio", value=True)
+        extended_refs = st.checkbox("📜LongRefs", value=False)
+        titles_summary = st.checkbox("🔖TitlesOnly", value=True)
+        full_audio = st.checkbox("📚FullAudio", value=False,
+                                 help="Full audio of results")
+        full_transcript = st.checkbox("🧾FullTranscript", value=False,
+                                      help="Generate a full transcript file")
+
+        if q and st.button("🔍Run"):
+            st.session_state.last_query = q  # Store the last query for zip naming
+            result = perform_ai_lookup(q, vocal_summary=vocal_summary, extended_refs=extended_refs, 
+                                       titles_summary=titles_summary, full_audio=full_audio)
+            if full_transcript:
+                save_full_transcript(q, result)
+
+        st.markdown("### Change Prompt & Re-Run")
+        q_new = st.text_input("🔄 Modify Query:")
+        if q_new and st.button("🔄 Re-Run with Modified Query"):
+            st.session_state.last_query = q_new  # Update last query
+            result = perform_ai_lookup(q_new, vocal_summary=vocal_summary, extended_refs=extended_refs, 
+                                       titles_summary=titles_summary, full_audio=full_audio)
+            if full_transcript:
+                save_full_transcript(q_new, result)
+
+    elif tab_main == "🎤 Voice":
+        st.subheader("🎤 Voice Input")
+        user_text = st.text_area("💬 Message:", height=100)
+        user_text = user_text.strip().replace('\n', ' ')
+        if st.button("📨 Send"):
+            process_with_gpt(user_text)
+        st.subheader("📜 Chat History")
+        t1,t2=st.tabs(["Claude History","GPT-4o History"])
+        with t1:
+            for c in st.session_state.chat_history:
+                st.write("**You:**", c["user"])
+                st.write("**Claude:**", c["claude"])
+        with t2:
+            for m in st.session_state.messages:
+                with st.chat_message(m["role"]):
+                    st.markdown(m["content"])
+
+    elif tab_main == "📸 Media":
+        st.header("📸 Images & 🎥 Videos")
+        tabs = st.tabs(["🖼 Images", "🎥 Video"])
+        with tabs[0]:
+            imgs = glob.glob("*.png")+glob.glob("*.jpg")
+            if imgs:
+                c = st.slider("Cols",1,5,3)
+                cols = st.columns(c)
+                for i,f in enumerate(imgs):
+                    with cols[i%c]:
+                        st.image(Image.open(f),use_container_width=True)
+                        if st.button(f"👀 Analyze {os.path.basename(f)}", key=f"analyze_{f}"):
+                            a = process_image(f,"Describe this image.")
+                            st.markdown(a)
+            else:
+                st.write("No images found.")
+        with tabs[1]:
+            vids = glob.glob("*.mp4")
+            if vids:
+                for v in vids:
+                    with st.expander(f"🎥 {os.path.basename(v)}"):
+                        st.video(v)
+                        if st.button(f"Analyze {os.path.basename(v)}", key=f"analyze_{v}"):
+                            a = process_video_with_gpt(v,"Describe video.")
+                            st.markdown(a)
+            else:
+                st.write("No videos found.")
+
+    elif tab_main == "📝 Editor":
+        if getattr(st.session_state,'current_file',None):
+            st.subheader(f"Editing: {st.session_state.current_file}")
+            new_text = st.text_area("✏️ Content:", st.session_state.file_content, height=300)
+            if st.button("💾 Save"):
+                with open(st.session_state.current_file,'w',encoding='utf-8') as f:
+                    f.write(new_text)
+                st.success("Updated!")
+                st.session_state.should_rerun = True
+        else:
+            st.write("Select a file from the sidebar to edit.")
+
+    # Load and display files in the sidebar
+    groups_sorted = load_files_for_sidebar()
+    display_file_manager_sidebar(groups_sorted)
+
+    if st.session_state.viewing_prefix and any(st.session_state.viewing_prefix == group for group, _ in groups_sorted):
+        st.write("---")
+        st.write(f"**Viewing Group:** {st.session_state.viewing_prefix}")
+        for group_name, files in groups_sorted:
+            if group_name == st.session_state.viewing_prefix:
+                for f in files:
+                    fname = os.path.basename(f)
+                    ext = os.path.splitext(fname)[1].lower().strip('.')
+                    st.write(f"### {fname}")
+                    if ext == "md":
+                        content = open(f,'r',encoding='utf-8').read()
+                        st.markdown(content)
+                    elif ext == "mp3":
+                        st.audio(f)
+                    elif ext == "wav":
+                        st.audio(f)  # 🆕 Handle WAV files
+                    else:
+                        st.markdown(get_download_link(f), unsafe_allow_html=True)
+                break
+        if st.button("❌ Close"):
+            st.session_state.viewing_prefix = None
+
+    markdownPapers = """
+    
+    # Levels of AGI
+
+## 1. Performance (rows) x Generality (columns)
+- **Narrow**  
+  - *clearly scoped or set of tasks*  
+- **General**  
+  - *wide range of non-physical tasks, including metacognitive abilities like learning new skills*  
+
+## 2. Levels of AGI
+
+### 2.1 Level 0: No AI
+- **Narrow Non-AI**  
+  - Calculator software; compiler  
+- **General Non-AI**  
+  - Human-in-the-loop computing, e.g., Amazon Mechanical Turk  
+
+### 2.2 Level 1: Emerging  
+*equal to or somewhat better than an unskilled human*  
+- **Emerging Narrow AI**  
+  - GOFAI; simple rule-based systems  
+  - Example: SHRDLU  
+    - *Reference:* Winograd, T. (1971). **Procedures as a Representation for Data in a Computer Program for Understanding Natural Language**. MIT AI Technical Report. [Link](https://dspace.mit.edu/handle/1721.1/7095)  
+- **Emerging AGI**  
+  - ChatGPT (OpenAI, 2023)  
+  - Bard (Anil et al., 2023)  
+    - *Reference:* Anil, R., et al. (2023). **Bard: Google’s AI Chatbot**. [arXiv](https://arxiv.org/abs/2303.12712)  
+  - LLaMA 2 (Touvron et al., 2023)  
+    - *Reference:* Touvron, H., et al. (2023). **LLaMA 2: Open and Efficient Foundation Language Models**. [arXiv](https://arxiv.org/abs/2307.09288)  
+
+### 2.3 Level 2: Competent  
+*at least 50th percentile of skilled adults*  
+- **Competent Narrow AI**  
+  - Toxicity detectors such as Jigsaw  
+    - *Reference:* Das, S., et al. (2022). **Toxicity Detection at Scale with Jigsaw**. [arXiv](https://arxiv.org/abs/2204.06905)  
+  - Smart Speakers (Apple, Amazon, Google)  
+  - VQA systems (PaLI)  
+    - *Reference:* Chen, T., et al. (2023). **PaLI: Pathways Language and Image model**. [arXiv](https://arxiv.org/abs/2301.01298)  
+  - Watson (IBM)  
+  - SOTA LLMs for subsets of tasks  
+- **Competent AGI**  
+  - Not yet achieved  
+
+### 2.4 Level 3: Expert  
+*at least 90th percentile of skilled adults*  
+- **Expert Narrow AI**  
+  - Spelling & grammar checkers (Grammarly, 2023)  
+  - Generative image models  
+    - Example: Imagen  
+      - *Reference:* Saharia, C., et al. (2022). **Imagen: Photorealistic Text-to-Image Diffusion Models**. [arXiv](https://arxiv.org/abs/2205.11487)  
+    - Example: DALL·E 2  
+      - *Reference:* Ramesh, A., et al. (2022). **Hierarchical Text-Conditional Image Generation with CLIP Latents**. [arXiv](https://arxiv.org/abs/2204.06125)  
+- **Expert AGI**  
+  - Not yet achieved  
+
+### 2.5 Level 4: Virtuoso  
+*at least 99th percentile of skilled adults*  
+- **Virtuoso Narrow AI**  
+  - Deep Blue  
+    - *Reference:* Campbell, M., et al. (2002). **Deep Blue**. IBM Journal of Research and Development. [Link](https://research.ibm.com/publications/deep-blue)  
+  - AlphaGo  
+    - *Reference:* Silver, D., et al. (2016, 2017). **Mastering the Game of Go with Deep Neural Networks and Tree Search**. [Nature](https://www.nature.com/articles/nature16961)  
+- **Virtuoso AGI**  
+  - Not yet achieved  
+
+### 2.6 Level 5: Superhuman  
+*outperforms 100% of humans*  
+- **Superhuman Narrow AI**  
+  - AlphaFold  
+    - *Reference:* Jumper, J., et al. (2021). **Highly Accurate Protein Structure Prediction with AlphaFold**. [Nature](https://www.nature.com/articles/s41586-021-03819-2)  
+  - AlphaZero  
+    - *Reference:* Silver, D., et al. (2018). **A General Reinforcement Learning Algorithm that Masters Chess, Shogi, and Go through Self-Play**. [Science](https://www.science.org/doi/10.1126/science.aar6404)  
+  - StockFish  
+    - *Reference:* Stockfish (2023). **Stockfish Chess Engine**. [Website](https://stockfishchess.org)  
+- **Artificial Superintelligence (ASI)**  
+  - Not yet achieved  
+
+
+# 🧬 Innovative Architecture of AlphaFold2: A Hybrid System
+
+## 1. 🔢 Input Sequence  
+- The process starts with an **input sequence** (protein sequence).  
+
+## 2. 🗄️ Database Searches  
+- **Genetic database search** 🔍  
+  - Searches genetic databases to retrieve related sequences.  
+- **Structure database search** 🔍  
+  - Searches structural databases for template structures.  
+- **Pairing** 🤝  
+  - Aligns sequences and structures for further analysis.  
+
+## 3. 🧩 MSA (Multiple Sequence Alignment)  
+- **MSA representation** 📊 (r,c)  
+  - Representation of multiple aligned sequences used as input.  
+
+## 4. 📑 Templates  
+- Template structures are paired to assist the model.  
+
+## 5. 🔄 Evoformer (48 blocks)  
+- A **deep learning module** that refines representations:  
+  - **MSA representation** 🧱  
+  - **Pair representation** 🧱 (r,c)  
+
+## 6. 🧱 Structure Module (8 blocks)  
+- Converts the representations into:  
+  - **Single representation** (r,c)  
+  - **Pair representation** (r,c)  
+
+## 7. 🧬 3D Structure Prediction  
+- The structure module predicts the **3D protein structure**.  
+- **Confidence levels**:  
+  - 🔵 *High confidence*  
+  - 🟠 *Low confidence*  
+
+## 8. ♻️ Recycling (Three Times)  
+- The model **recycles** its output up to three times to refine the prediction.  
+
+## 9. 📚 Reference  
+**Jumper, J., et al. (2021).** Highly Accurate Protein Structure Prediction with AlphaFold. *Nature.*  
+🔗 [Nature Publication Link](https://www.nature.com/articles/s41586-021-03819-2)  
+
+    """
+    st.sidebar.markdown(markdownPapers)
+    
+    if st.session_state.should_rerun:
+        st.session_state.should_rerun = False
+        st.rerun()
+
+if __name__=="__main__":
+    main()