Update app.py

app.py

@@ -1,20 +1,26 @@
 import streamlit as st
 
-# Must be the very first Streamlit command
+# Must be first Streamlit command
 st.set_page_config(
-    page_title="🚲BikeAI🏆 Claude/GPT Research",
-    page_icon="🚲🏆",
+    page_title="ARIA Research Assistant",
+    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"
-    }
+    initial_sidebar_state="auto"
 )
 
-# Now import everything else
-import anthropic, openai, base64, cv2, glob, json, math, os, pytz, random, re, requests, time, zipfile
+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 plotly.graph_objects as go
 import streamlit.components.v1 as components
 from datetime import datetime
@@ -24,81 +30,53 @@ 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
 load_dotenv()
 
-# 🔑 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"]
+# 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', '')
 
 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'] = []
+# Session State Management
 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
 
-# 🎨 4. Custom CSS
+# Styling
 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;
-    }
-    .audio-player {
-        margin: 1rem 0;
-        padding: 1rem;
-        border-radius: 10px;
-        background: #f5f5f5;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        background-color: #4CAF50;
+        color: white;
+        padding: 0.5rem 1rem;
+        border-radius: 5px;
+        border: none;
     }
 </style>
 """, unsafe_allow_html=True)
 
-FILE_EMOJIS = {
-    "md": "📝",
-    "mp3": "🎵",
-}
-
+# Audio Functions
 def clean_for_speech(text: str) -> str:
     """Clean text for speech synthesis"""
     text = text.replace("\n", " ")
@@ -108,520 +86,101 @@ def clean_for_speech(text: str) -> str:
     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):
+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"):
     """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, "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)
-
-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)
-
-    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)
-
-    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)
-
-    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)
-
-    elapsed = time.time()-start
-    st.write(f"**Total Elapsed:** {elapsed:.2f} s")
-
-    # Create file with 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
+    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"
         )
-        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
-
-def generate_filename(prompt, response, file_type="md"):
-    """Generate filename with timestamp and cleaned text."""
-    timestamp = datetime.now().strftime("%y%m_%H%M")
-    safe_text = re.sub(r'[^\w\s-]', '', prompt[:50])
-    return f"{timestamp}_{safe_text}.{file_type}"
-
-def create_file(prompt, response, file_type="md"):
-    """Create file with content."""
-    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>'
-
-import streamlit as st
-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
-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="🚲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()
-
-# 🔑 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
-
-# 🎨 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;
-    }
-    .audio-player {
-        margin: 1rem 0;
-        padding: 1rem;
-        border-radius: 10px;
-        background: #f5f5f5;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-    }
-</style>
-""", unsafe_allow_html=True)
 
-FILE_EMOJIS = {
-    "md": "📝",
-    "mp3": "🎵",
-}
+        result = f"### 🔎 Search Results\n\n{summary}\n\n### References\n\n{refs}"
+        st.markdown(result)
 
-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
+        # 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)
 
-@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)
+        return result
 
-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():
+    except Exception as e:
+        st.error(f"Error in search: {str(e)}")
         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, "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)
-
-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)
-
-    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)
-
-    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)
-
-    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)
-
-    elapsed = time.time()-start
-    st.write(f"**Total Elapsed:** {elapsed:.2f} s")
-
-    # Create file with 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
-
-def generate_filename(prompt, response, file_type="md"):
-    """Generate filename with timestamp and cleaned text."""
-    timestamp = datetime.now().strftime("%y%m_%H%M")
-    safe_text = re.sub(r'[^\w\s-]', '', prompt[:50])
-    return f"{timestamp}_{safe_text}.{file_type}"
-
-def create_file(prompt, response, file_type="md"):
-    """Create file with content."""
-    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>'
 
 def main():
-    st.sidebar.markdown("### 🚲BikeAI🏆 Multi-Agent Research")
-    tab_main = st.radio("Action:",["🎤 Voice","📸 Media","🔍 ArXiv","📝 Editor"],horizontal=True)
-
+    st.sidebar.markdown("### Research Assistant")
+    
+    # Voice component
     mycomponent = components.declare_component("mycomponent", path="mycomponent")
     val = mycomponent(my_input_value="Hello")
 
-    # Show input in text box for editing if detected
+    # Handle voice input
     if val:
         val_stripped = val.replace('\n', ' ')
         edited_input = st.text_area("✏️ Edit Input:", value=val_stripped, height=100)
-        run_option = st.selectbox("Model:", ["Arxiv", "GPT-4o", "Claude-3.5"])
-        col1, col2 = st.columns(2)
+        
+        col1, col2 = st.columns([3,1])
         with col1:
-            autorun = st.checkbox("⚙ AutoRun", value=True)
+            model = st.selectbox("Model:", ["Arxiv", "GPT-4", "Claude"])
         with col2:
-            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.")
+            autorun = st.checkbox("⚙ AutoRun", value=True)
 
-    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
+        # 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)
         else:
-            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
-
-    if st.session_state.should_rerun:
-        st.session_state.should_rerun = False
-        st.rerun()
+            if st.button("🔍 Search"):
+                perform_ai_lookup(edited_input, vocal_summary=True, full_audio=True)
+
+    # 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 __name__ == "__main__":
     main()