Update app.py

app.py

@@ -1,10 +1,15 @@
+# requirements.txt additions:
+"""
+streamlit-marquee
+"""
+
+# app.py
 import streamlit as st
-import anthropic, openai, base64, cv2, glob, json, math, os, pytz, random, re, requests, textract, time, zipfile
+import anthropic, openai, base64, cv2, glob, json, math, os, pytz, random, re, requests, 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
@@ -16,541 +21,60 @@ 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
+from streamlit_marquee import st_marquee
 
-# 🎯 1. Core Configuration & Setup
+# Core 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
+# Initialize session state
+if 'tts_voice' not in st.session_state:
+    st.session_state['tts_voice'] = "en-US-AriaNeural"
+if 'audio_format' not in st.session_state:
+    st.session_state['audio_format'] = 'mp3'
+if 'scroll_text' not in st.session_state:
+    st.session_state['scroll_text'] = ''
+
 EDGE_TTS_VOICES = [
-    "en-US-AriaNeural",  # Default voice
-    "en-US-GuyNeural", 
+    "en-US-AriaNeural",
+    "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
+    "wav": "🔊",
+    "txt": "📄",
+    "pdf": "📑",
+    "json": "📊",
+    "csv": "📈",
+    "zip": "📦"
 }
 
-# 🧠 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.")
+def get_cached_audio_b64(file_path):
+    """Cache audio file as base64"""
+    with open(file_path, "rb") as f:
+        return base64.b64encode(f.read()).decode()
 
-    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 beautify_filename(filename):
+    """Make filename more readable"""
+    name = os.path.splitext(filename)[0]
+    name = name.replace('_', ' ').replace('.', ' ')
+    return name
 
 def load_files_for_sidebar():
-    """Load and group files for sidebar display based on first 9 characters of filename"""
+    """Load and group files by timestamp prefix"""
     md_files = glob.glob("*.md")
-    mp3_files = glob.glob("*.mp3")
+    mp3_files = glob.glob("*.mp3") 
     wav_files = glob.glob("*.wav")
 
     md_files = [f for f in md_files if os.path.basename(f).lower() != 'readme.md']
@@ -558,398 +82,115 @@ def load_files_for_sidebar():
 
     groups = defaultdict(list)
     for f in all_files:
-        # Get first 9 characters of filename (timestamp) as group name
         basename = os.path.basename(f)
         group_name = basename[:9] if len(basename) >= 9 else 'Other'
         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)
+    return sorted(groups.items(), 
+                 key=lambda x: max(os.path.getmtime(f) for f in x[1]), 
+                 reverse=True)
 
 def display_file_manager_sidebar(groups_sorted):
-    """Display file manager in sidebar with timestamp-based groups"""
-    st.sidebar.title("🎵 Audio & Docs Manager")
+    """Enhanced sidebar with audio players and beautified names"""
+    st.sidebar.title("📚 File Manager")
 
-    all_md = []
-    all_mp3 = []
-    all_wav = []
-    for group_name, files in groups_sorted:
+    all_md, all_mp3, all_wav = [], [], []
+    for _, 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)
-
-    top_bar = st.sidebar.columns(4)
-    with top_bar[0]:
-        if st.button("🗑 DelAllMD"):
-            for f in all_md:
-                os.remove(f)
+            if f.endswith(".md"): all_md.append(f)
+            elif f.endswith(".mp3"): all_mp3.append(f)
+            elif f.endswith(".wav"): all_wav.append(f)
+
+    # File management buttons
+    cols = st.sidebar.columns(4)
+    with cols[0]:
+        if st.button("🗑️ MD"): 
+            [os.remove(f) for f in all_md]
             st.session_state.should_rerun = True
-    with top_bar[1]:
-        if st.button("🗑 DelAllMP3"):
-            for f in all_mp3:
-                os.remove(f)
+    with cols[1]:
+        if st.button("🗑️ MP3"):
+            [os.remove(f) for f in all_mp3]
             st.session_state.should_rerun = True
-    with top_bar[2]:
-        if st.button("🗑 DelAllWAV"):
-            for f in all_wav:
-                os.remove(f)
+    with cols[2]:
+        if st.button("🗑️ WAV"):
+            [os.remove(f) for f in all_wav]
             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', ''))
+    with cols[3]:
+        if st.button("📦 Zip"):
+            zip_name = create_zip_of_files(all_md, all_mp3, all_wav, 
+                                         st.session_state.get('last_query', ''))
             if zip_name:
-                st.sidebar.markdown(get_download_link(zip_name, file_type="zip"), unsafe_allow_html=True)
+                st.sidebar.markdown(get_download_link(zip_name), unsafe_allow_html=True)
 
+    # Display file groups
     for group_name, files in groups_sorted:
         timestamp_dt = datetime.strptime(group_name, "%y%m_%H%M") if len(group_name) == 9 else None
         group_label = timestamp_dt.strftime("%Y-%m-%d %H:%M") if timestamp_dt else group_name
         
         with st.sidebar.expander(f"📁 {group_label} ({len(files)})", expanded=True):
-            c1,c2 = st.columns(2)
+            c1, c2 = st.columns(2)
             with c1:
-                if st.button("👀ViewGrp", key="view_group_"+group_name):
+                if st.button("👀", key=f"view_{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}!")
+                if st.button("🗑️", key=f"del_{group_name}"):
+                    [os.remove(f) for f in files]
                     st.session_state.should_rerun = True
 
             for f in files:
-                fname = os.path.basename(f)
-                ext = os.path.splitext(fname)[1].lower()
-                emoji = FILE_EMOJIS.get(ext.strip('.'), '')
-                ctime = datetime.fromtimestamp(os.path.getmtime(f)).strftime("%H:%M:%S")
-                st.write(f"{emoji} **{fname}** - {ctime}")
+                ext = os.path.splitext(f)[1].lower().strip('.')
+                emoji = FILE_EMOJIS.get(ext, '📄')
+                pretty_name = beautify_filename(os.path.basename(f))
+                st.write(f"{emoji} **{pretty_name}**")
+                
+                if ext in ['mp3', 'wav']:
+                    audio_b64 = get_cached_audio_b64(f)
+                    st.audio(f)
+                    cols = st.columns([3,1])
+                    with cols[1]:
+                        if st.button("🔄", key=f"loop_{f}"):
+                            components.html(
+                                f'''
+                                <audio id="player_{f}" loop>
+                                    <source src="data:audio/{ext};base64,{audio_b64}">
+                                </audio>
+                                <script>
+                                    document.getElementById("player_{f}").play();
+                                </script>
+                                ''',
+                                height=0
+                            )
 
-# 🎯 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 scrolling banner
+    st_marquee(
+        text=" | ".join(st.session_state.get('scroll_text', '🚀 Welcome to TalkingAIResearcher').split('\n')),
+        font_size=20,
     )
-    
-    # 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()
+    # Rest of the main UI code...
+    # (Keep existing main() implementation but with beautified filenames)
+
+# Compressed sidebar markdown
+sidebar_md = """
+# 🧠 AGI Levels
+L0 ❌ No AI
+L1 🌱 Emerging (ChatGPT, Bard)
+L2 💪 Competent (Watson)
+L3 🎯 Expert (DALL·E)
+L4 🏆 Virtuoso (AlphaGo)
+L5 🚀 Superhuman (AlphaFold)
+
+# 🧬 AlphaFold2
+1. 🧬 Input Seq
+2. 🔍 DB Search
+3. 🧩 MSA
+4. 📑 Templates  
+5. 🔄 Evoformer
+6. 🧱 Structure
+7. 🎯 3D Predict
+8. ♻️ Recycle x3
+"""
 
 if __name__=="__main__":
-    main()
+    main()