Update app.py

app.py

@@ -1,26 +1,5 @@
 import streamlit as st
-
-# Must be first Streamlit command
-st.set_page_config(
-    page_title="ARIA Research Assistant",
-    page_icon="🔬",
-    layout="wide",
-    initial_sidebar_state="auto"
-)
-
-import anthropic
-import openai
-import base64
-import cv2
-import glob
-import json
-import os
-import pytz
-import random
-import re
-import requests
-import time
-import zipfile
+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
@@ -30,53 +9,187 @@ from collections import defaultdict, deque
 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
 
-# Load environment variables
+# 🎯 1. Core Configuration & Setup
+st.set_page_config(
+    page_title="🚲BikeAI🏆 Claude/GPT Research",
+    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': "🚲BikeAI🏆 Claude/GPT Research AI"
+    }
+)
 load_dotenv()
 
-# API Setup & Clients
-openai_api_key = os.getenv('OPENAI_API_KEY', st.secrets.get('OPENAI_API_KEY', ''))
-anthropic_key = os.getenv('ANTHROPIC_API_KEY_3', st.secrets.get('ANTHROPIC_API_KEY', ''))
-xai_key = os.getenv('xai', '')
+# 🔑 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')
 
-# Session State Management
+# 📝 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 'current_audio' not in st.session_state:
-    st.session_state['current_audio'] = None
 
-# Styling
+# 🎨 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;
-        background-color: #4CAF50;
-        color: white;
-        padding: 0.5rem 1rem;
-        border-radius: 5px;
-        border: none;
     }
 </style>
 """, unsafe_allow_html=True)
 
-# Audio Functions
+FILE_EMOJIS = {
+    "md": "📝",
+    "mp3": "🎵",
+}
+
+# 🧠 5. High-Information Content Extraction
+def get_high_info_terms(text: str) -> 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'
+    ]
+
+    # Identify key phrases
+    preserved_phrases = []
+    lower_text = text.lower()
+    for phrase in key_phrases:
+        if phrase in lower_text:
+            preserved_phrases.append(phrase)
+            text = text.replace(phrase, '')
+
+    # Extract individual words
+    words = re.findall(r'\b\w+(?:-\w+)*\b', text)
+    high_info_words = [
+        word.lower() for word in words 
+        if len(word) > 3
+        and word.lower() not in stop_words
+        and not word.isdigit()
+        and any(c.isalpha() for c in word)
+    ]
+
+    all_terms = preserved_phrases + high_info_words
+    seen = set()
+    unique_terms = []
+    for term in all_terms:
+        if term not in seen:
+            seen.add(term)
+            unique_terms.append(term)
+
+    max_terms = 5
+    return unique_terms[:max_terms]
+
+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)
+    
+    # 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
+    # Prioritize info terms in front
+    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):
+    """Generate download link for file"""
+    with open(file, "rb") as f:
+        b64 = base64.b64encode(f.read()).decode()
+    return f'<a href="data:file/zip;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", " ")
@@ -86,101 +199,456 @@ def clean_for_speech(text: str) -> str:
     text = re.sub(r"\s+", " ", text).strip()
     return text
 
-def get_audio_html(audio_path):
-    """Create HTML for autoplaying audio"""
-    try:
-        with open(audio_path, "rb") as audio_file:
-            audio_bytes = audio_file.read()
-            audio_b64 = base64.b64encode(audio_bytes).decode()
-            return f'''
-                <audio controls autoplay>
-                    <source src="data:audio/mpeg;base64,{audio_b64}" type="audio/mpeg">
-                </audio>
-                <a href="data:audio/mpeg;base64,{audio_b64}" 
-                   download="{os.path.basename(audio_path)}">
-                   Download {os.path.basename(audio_path)}
-                </a>
-            '''
-    except Exception as e:
-        return f"Error loading audio: {str(e)}"
-
-async def generate_audio(text, voice="en-US-AriaNeural"):
+@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):
     """Generate audio using Edge TTS"""
+    text = clean_for_speech(text)
     if not text.strip():
         return None
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    output_file = f"response_{timestamp}.mp3"
-    communicate = edge_tts.Communicate(text, voice)
-    await communicate.save(output_file)
-    return output_file
-
-# Core Search Function
-def perform_ai_lookup(query, vocal_summary=True, full_audio=False):
-    """Perform search with automatic audio generation"""
-    try:
-        client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-        refs = client.predict(
-            query, 
-            20,
-            "Semantic Search",
-            "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            api_name="/update_with_rag_md"
-        )[0]
-        summary = client.predict(
-            query,
-            "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            True,
-            api_name="/ask_llm"
-        )
+    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, "mp3")
+    await communicate.save(out_fn)
+    return out_fn
+
+def speak_with_edge_tts(text, voice="en-US-AriaNeural", rate=0, pitch=0):
+    """Wrapper for edge TTS generation"""
+    return asyncio.run(edge_tts_generate_audio(text, voice, rate, pitch))
+
+def play_and_download_audio(file_path):
+    """Play and provide download link for audio"""
+    if file_path and os.path.exists(file_path):
+        st.audio(file_path)
+        dl_link = f'<a href="data:audio/mpeg;base64,{base64.b64encode(open(file_path,"rb").read()).decode()}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}</a>'
+        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(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 perform_ai_lookup(q, vocal_summary=True, extended_refs=False, titles_summary=True, full_audio=False):
+    """Perform Arxiv search and generate audio summaries"""
+    start = time.time()
+    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")
+
+    result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
+
+    st.markdown(result)
+
+    # Generate full audio version if requested
+    if full_audio:
+        complete_text = f"Complete response for query: {q}. {clean_for_speech(r2)} {clean_for_speech(refs)}"
+        audio_file_full = speak_with_edge_tts(complete_text)
+        st.write("### 📚 Full Audio")
+        play_and_download_audio(audio_file_full)
 
-        result = f"### 🔎 Search Results\n\n{summary}\n\n### References\n\n{refs}"
-        st.markdown(result)
+    if vocal_summary:
+        main_text = clean_for_speech(r2)
+        audio_file_main = speak_with_edge_tts(main_text)
+        st.write("### 🎙 Short Audio")
+        play_and_download_audio(audio_file_main)
 
-        # Generate and play audio
-        if full_audio:
-            audio_file = asyncio.run(generate_audio(summary))
-            if audio_file:
-                st.markdown(get_audio_html(audio_file), unsafe_allow_html=True)
+    if extended_refs:
+        summaries_text = "Extended references: " + refs.replace('"','')
+        summaries_text = clean_for_speech(summaries_text)
+        audio_file_refs = speak_with_edge_tts(summaries_text)
+        st.write("### 📜 Long Refs")
+        play_and_download_audio(audio_file_refs)
 
-        return result
+    if titles_summary:
+        titles = []
+        for line in refs.split('\n'):
+            m = re.search(r"\[([^\]]+)\]", line)
+            if m:
+                titles.append(m.group(1))
+        if titles:
+            titles_text = "Titles: " + ", ".join(titles)
+            titles_text = clean_for_speech(titles_text)
+            audio_file_titles = speak_with_edge_tts(titles_text)
+            st.write("### 🔖 Titles")
+            play_and_download_audio(audio_file_titles)
 
-    except Exception as e:
-        st.error(f"Error in search: {str(e)}")
+    elapsed = time.time()-start
+    st.write(f"**Total Elapsed:** {elapsed:.2f} s")
+
+    # Always create a file with the result
+    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):
+    """Create zip with intelligent naming"""
+    md_files = [f for f in md_files if os.path.basename(f).lower() != 'readme.md']
+    all_files = md_files + mp3_files
+    if not all_files:
         return None
 
-def main():
-    st.sidebar.markdown("### Research Assistant")
+    # 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'):
+            all_content.append(os.path.basename(f))
+    
+    combined_content = " ".join(all_content)
+    info_terms = get_high_info_terms(combined_content)
+    
+    timestamp = datetime.now().strftime("%y%m_%H%M")
+    name_text = '_'.join(term.replace(' ', '-') for term in info_terms[:3])
+    zip_name = f"{timestamp}_{name_text}.zip"
     
-    # Voice component
+    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")
+
+    md_files = [f for f in md_files if os.path.basename(f).lower() != 'readme.md']
+    all_files = md_files + mp3_files
+
+    groups = defaultdict(list)
+    for f in all_files:
+        fname = os.path.basename(f)
+        prefix = fname[:10]
+        groups[prefix].append(f)
+
+    for prefix in groups:
+        groups[prefix].sort(key=lambda x: os.path.getmtime(x), reverse=True)
+
+    sorted_prefixes = sorted(groups.keys(), 
+                           key=lambda pre: max(os.path.getmtime(x) for x in groups[pre]), 
+                           reverse=True)
+    return groups, sorted_prefixes
+
+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)
+
+def display_file_manager_sidebar(groups, sorted_prefixes):
+    """Display file manager in sidebar"""
+    st.sidebar.title("🎵 Audio & Docs Manager")
+
+    all_md = []
+    all_mp3 = []
+    for prefix in groups:
+        for f in groups[prefix]:
+            if f.endswith(".md"):
+                all_md.append(f)
+            elif f.endswith(".mp3"):
+                all_mp3.append(f)
+
+    top_bar = st.sidebar.columns(3)
+    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("⬇️ ZipAll"):
+            z = create_zip_of_files(all_md, all_mp3)
+            if z:
+                st.sidebar.markdown(get_download_link(z),unsafe_allow_html=True)
+
+    for prefix in sorted_prefixes:
+        files = groups[prefix]
+        kw = extract_keywords_from_md(files)
+        keywords_str = " ".join(kw) if kw else "No Keywords"
+        with st.sidebar.expander(f"{prefix} Files ({len(files)}) - KW: {keywords_str}", expanded=True):
+            c1,c2 = st.columns(2)
+            with c1:
+                if st.button("👀ViewGrp", key="view_group_"+prefix):
+                    st.session_state.viewing_prefix = prefix
+            with c2:
+                if st.button("🗑DelGrp", key="del_group_"+prefix):
+                    for f in files:
+                        os.remove(f)
+                    st.success(f"Deleted group {prefix}!")
+                    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")
+    tab_main = st.radio("Action:",["🎤 Voice","📸 Media","🔍 ArXiv","📝 Editor"],horizontal=True)
+
     mycomponent = components.declare_component("mycomponent", path="mycomponent")
     val = mycomponent(my_input_value="Hello")
 
-    # Handle voice input
+    # 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)
-        
-        col1, col2 = st.columns([3,1])
+        run_option = st.selectbox("Model:", ["Arxiv", "GPT-4o", "Claude-3.5"])
+        col1, col2 = st.columns(2)
         with col1:
-            model = st.selectbox("Model:", ["Arxiv", "GPT-4", "Claude"])
+            autorun = st.checkbox("⚙ AutoRun", value=False)
         with col2:
-            autorun = st.checkbox("⚙ AutoRun", value=True)
+            full_audio = st.checkbox("📚FullAudio", value=False, 
+                                     help="Generate full audio response")
 
-        # Check for changes and autorun
         input_changed = (val != st.session_state.old_val)
+
         if autorun and input_changed:
             st.session_state.old_val = val
-            if edited_input:
-                perform_ai_lookup(edited_input, vocal_summary=True, full_audio=True)
+            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
+                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"):
+            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"):
+            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:
-            if st.button("🔍 Search"):
-                perform_ai_lookup(edited_input, vocal_summary=True, full_audio=True)
+            st.write("Select a file from the sidebar to edit.")
+
+    groups, sorted_prefixes = load_files_for_sidebar()
+    display_file_manager_sidebar(groups, sorted_prefixes)
+
+    if st.session_state.viewing_prefix and st.session_state.viewing_prefix in groups:
+        st.write("---")
+        st.write(f"**Viewing Group:** {st.session_state.viewing_prefix}")
+        for f in groups[st.session_state.viewing_prefix]:
+            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)
+            else:
+                st.markdown(get_download_link(f), unsafe_allow_html=True)
+        if st.button("❌ Close"):
+            st.session_state.viewing_prefix = None
 
-    # Manual search tab
-    st.markdown("### 🔍 Direct Search")
-    query = st.text_input("Enter search query:")
-    if query and st.button("Search"):
-        perform_ai_lookup(query, vocal_summary=True, full_audio=True)
+    if st.session_state.should_rerun:
+        st.session_state.should_rerun = False
+        st.rerun()
 
-if __name__ == "__main__":
-    main()
+if __name__=="__main__":
+    main()