Create app.py

app.py

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+import streamlit as st
+import anthropic, openai, base64, cv2, glob, json, math, os, pytz, random, re, requests, textract, time, zipfile
+import plotly.graph_objects as go
+import streamlit.components.v1 as components
+from datetime import datetime
+from audio_recorder_streamlit import audio_recorder
+from bs4 import BeautifulSoup
+from collections import defaultdict, deque
+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
+import io
+import sys
+import subprocess
+
+# 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', "")
+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;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+FILE_EMOJIS = {
+    "md": "📝",
+    "mp3": "🎵",
+}
+
+# 5. High-Information Content Extraction
+def get_high_info_terms(text: str) -> list:
+    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'
+    ]
+
+    preserved_phrases = []
+    lower_text = text.lower()
+    for phrase in key_phrases:
+        if phrase in lower_text:
+            preserved_phrases.append(phrase)
+            text = text.replace(phrase, '')
+
+    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]
+
+# 6. Filename Generation
+def generate_filename(content, file_type="md"):
+    prefix = datetime.now().strftime("%y%m_%H%M") + "_"
+    info_terms = get_high_info_terms(content)
+    name_text = '_'.join(term.replace(' ', '-') for term in info_terms) if info_terms else 'file'
+    
+    max_length = 100
+    if len(name_text) > max_length:
+        name_text = name_text[:max_length]
+    
+    filename = f"{prefix}{name_text}.{file_type}"
+    return filename
+
+# 7. Audio Processing
+def clean_for_speech(text: str) -> str:
+    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):
+    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):
+    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, "mp3")
+    await communicate.save(out_fn)
+    return out_fn
+
+def speak_with_edge_tts(text, voice="en-US-AriaNeural", rate=0, pitch=0):
+    return asyncio.run(edge_tts_generate_audio(text, voice, rate, pitch))
+
+def play_and_download_audio(file_path):
+    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):
+    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):
+    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):
+    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):
+    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
+
+# Execution context for code blocks
+context = {}
+
+# 9. Updated create_file function with error handling
+def create_file(filename, prompt, response, should_save=True):
+    if not should_save:
+        return
+
+    base_filename, ext = os.path.splitext(filename)
+    combined_content = ""
+
+    combined_content += "# Prompt 📝\n" + prompt + "\n\n"
+    combined_content += "# Response 💬\n" + response + "\n\n"
+
+    resources = re.findall(r"```([\s\S]*?)```", response)
+    for resource in resources:
+        if "python" in resource.lower():
+            cleaned_code = re.sub(r'^\s*python', '', resource, flags=re.IGNORECASE | re.MULTILINE)
+
+            combined_content += "# Code Results 🚀\n"
+
+            original_stdout = sys.stdout
+            sys.stdout = io.StringIO()
+            try:
+                exec(cleaned_code, context)
+                code_output = sys.stdout.getvalue()
+                combined_content += f"```\n{code_output}\n```\n\n"
+                realtimeEvalResponse = "# Code Results 🚀\n" + "```" + code_output + "```\n\n"
+                st.code(realtimeEvalResponse)
+            except Exception as e:
+                combined_content += f"```python\nError executing Python code: {e}\n```\n\n"
+            sys.stdout = original_stdout
+        else:
+            combined_content += "# Resource 🛠️\n" + "```" + resource + "```\n\n"
+
+    if should_save:
+        with open(f"{base_filename}.md", 'w') as file:
+            file.write(combined_content)
+            st.code(combined_content)
+
+    with open(f"{base_filename}.md", 'rb') as file:
+        encoded_file = base64.b64encode(file.read()).decode()
+        href = f'<a href="data:file/markdown;base64,{encoded_file}" download="{filename}">Download File 📄</a>'
+        st.markdown(href, unsafe_allow_html=True)
+
+def generate_code_from_paper(title, summary, instructions):
+    code_prompt = f"""
+You are a coding assistant. 
+Given the paper titled: "{title}"
+Summary: "{summary}"
+
+The user wants to implement the following steps in Python code. Provide a minimal, self-contained Python code snippet that:
+
+1. Uses only standard libraries if possible. If a library is required, include a code snippet that uses subprocess to install it (like `subprocess.run(['pip','install','somepackage'])`).
+2. Implement the requested functionality as simple functions and variables, minimal code.
+3. Include error handling: if a file is missing, print an error message. Wrap code in a `try/except` block.
+4. Output should be minimal, just the code block (no extra explanations), enclosed in triple backticks.
+
+User instructions: "{instructions}"
+"""
+
+    try:
+        completion = openai_client.chat.completions.create(
+            model=st.session_state["openai_model"],
+            messages=[
+                {"role": "system", "content": "You are a helpful coding assistant."},
+                {"role": "user", "content": code_prompt}
+            ],
+            temperature=0.0
+        )
+        generated_code = completion.choices[0].message.content
+        return generated_code
+    except Exception as e:
+        st.error(f"Error generating code: {e}")
+        return ""
+
+# 10. AI Model Integration
+def perform_ai_lookup(q, vocal_summary=True, extended_refs=False, titles_summary=True, full_audio=False):
+    start = time.time()
+    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+    r = client.predict(q,20,"Semantic Search","mistralai/Mixtral-8x7B-Instruct-v0.1",api_name="/update_with_rag_md")
+    refs = r[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)
+
+    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("### 📚 Complete Audio Response")
+        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("### 🎙️ Vocal Summary (Short Answer)")
+        play_and_download_audio(audio_file_main)
+
+    if extended_refs:
+        summaries_text = "Here are the summaries from the references: " + refs.replace('"','')
+        summaries_text = clean_for_speech(summaries_text)
+        audio_file_refs = speak_with_edge_tts(summaries_text)
+        st.write("### 📜 Extended References & Summaries")
+        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 = "Here are the titles of the papers: " + ", ".join(titles)
+            titles_text = clean_for_speech(titles_text)
+            audio_file_titles = speak_with_edge_tts(titles_text)
+            st.write("### 🔖 Paper Titles")
+            play_and_download_audio(audio_file_titles)
+
+    elapsed = time.time()-start
+    st.write(f"**Total Elapsed:** {elapsed:.2f} s")
+
+    filename = generate_filename(result, "md")
+    create_file(filename, q, result, should_save=True)
+
+    # Parse out papers
+    papers_raw = refs.strip().split("[Title]")
+    papers = []
+    for p in papers_raw:
+        p = p.strip()
+        if not p:
+            continue
+        lines = p.split("\n")
+        title_line = lines[0].strip() if lines else ""
+        summary_line = ""
+        link_line = ""
+        pdf_line = ""
+        for line in lines[1:]:
+            line = line.strip()
+            if line.startswith("Summary:"):
+                summary_line = line.replace("Summary:", "").strip()
+            elif line.startswith("Link:"):
+                link_line = line.replace("Link:", "").strip()
+            elif line.startswith("PDF:"):
+                pdf_line = line.replace("PDF:", "").strip()
+
+        if title_line and summary_line:
+            papers.append({
+                "title": title_line,
+                "summary": summary_line,
+                "link": link_line,
+                "pdf": pdf_line
+            })
+
+    st.write("## Code Interpreter Options for Each Paper")
+    for i, paper in enumerate(papers):
+        st.write(f"**Paper {i+1}:** {paper['title']}")
+        st.write(f"**Summary:** {paper['summary']}")
+        if paper['link']:
+            st.write(f"[Arxiv Link]({paper['link']})")
+        if paper['pdf']:
+            st.write(f"[PDF]({paper['pdf']})")
+
+        # UI for generating code steps
+        with st.expander("Generate Python Code Steps"):
+            instructions = st.text_area(
+                f"Enter instructions for Python code implementation for this paper:",
+                height=100, key=f"code_task_{i}"
+            )
+            if st.button(f"Generate Python Code Steps for Paper {i+1}", key=f"gen_code_{i}"):
+                if instructions.strip():
+                    generated_code = generate_code_from_paper(paper['title'], paper['summary'], instructions)
+                    if generated_code.strip():
+                        st.write("### Generated Code")
+                        st.code(generated_code, language="python")
+
+                        # Attempt to run the generated code
+                        if '```' in generated_code:
+                            # Extract code blocks
+                            code_blocks = re.findall(r"```([\s\S]*?)```", generated_code)
+                            for cb in code_blocks:
+                                # Try executing cb
+                                original_stdout = sys.stdout
+                                sys.stdout = io.StringIO()
+                                try:
+                                    exec(cb, {})
+                                    exec_output = sys.stdout.getvalue()
+                                    if exec_output.strip():
+                                        st.write("### Code Output")
+                                        st.write(exec_output)
+                                        # TTS on code output
+                                        audio_file = speak_with_edge_tts(exec_output)
+                                        if audio_file:
+                                            play_and_download_audio(audio_file)
+                                except Exception as e:
+                                    st.error(f"Error executing code: {e}")
+                                finally:
+                                    sys.stdout = original_stdout
+                    else:
+                        st.error("No code was generated.")
+                else:
+                    st.warning("Please provide instructions before generating code.")
+
+    return result
+
+def process_with_gpt(text):
+    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"):
+        try:
+            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
+        except Exception as e:
+            ans = f"Error calling GPT-4 API: {e}"
+
+        st.write("GPT-4o: " + ans)
+        filename = generate_filename(ans.strip() if ans.strip() else text.strip(), "md")
+        create_file(filename, text, ans, should_save=True)
+        st.session_state.messages.append({"role":"assistant","content":ans})
+    return ans
+
+def process_with_claude(text):
+    if not text: return
+    with st.chat_message("user"):
+        st.markdown(text)
+    with st.chat_message("assistant"):
+        try:
+            r = claude_client.messages.create(
+                model="claude-3-sonnet-20240229",
+                max_tokens=1000,
+                messages=[{"role":"user","content":text}]
+            )
+            ans = r.content[0].text
+        except Exception as e:
+            ans = f"Error calling Claude API: {e}"
+
+        st.write("Claude-3.5: " + ans)
+        filename = generate_filename(ans.strip() if ans.strip() else text.strip(), "md")
+        create_file(filename, text, ans, should_save=True)
+        st.session_state.chat_history.append({"user":text,"claude":ans})
+    return ans
+
+# 11. File Management
+def create_zip_of_files(md_files, mp3_files):
+    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
+
+    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"
+    
+    with zipfile.ZipFile(zip_name,'w') as z:
+        for f in all_files:
+            z.write(f)
+    
+    return zip_name
+
+def load_files_for_sidebar():
+    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):
+    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):
+    st.sidebar.title("🎵 Audio & Document 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("🗑 Del All MD"):
+            for f in all_md:
+                os.remove(f)
+            st.session_state.should_rerun = True
+    with top_bar[1]:
+        if st.button("🗑 Del All MP3"):
+            for f in all_mp3:
+                os.remove(f)
+            st.session_state.should_rerun = True
+    with top_bar[2]:
+        if st.button("⬇️ Zip All"):
+            z = create_zip_of_files(all_md, all_mp3)
+            if z:
+                with open(z, "rb") as f:
+                    b64 = base64.b64encode(f.read()).decode()
+                dl_link = f'<a href="data:file/zip;base64,{b64}" download="{os.path.basename(z)}">📂 Download {os.path.basename(z)}</a>'
+                st.sidebar.markdown(dl_link,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)}) - Keywords: {keywords_str}", expanded=True):
+            c1,c2 = st.columns(2)
+            with c1:
+                if st.button("👀View Group", key="view_group_"+prefix):
+                    st.session_state.viewing_prefix = prefix
+            with c2:
+                if st.button("🗑Del Group", key="del_group_"+prefix):
+                    for f in files:
+                        os.remove(f)
+                    st.success(f"Deleted all files in group {prefix} successfully!")
+                    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}")
+
+# 12. Main Application
+def main():
+    st.sidebar.markdown("### 🚲BikeAI🏆 Multi-Agent Research AI")
+    tab_main = st.radio("Action:",["🎤 Voice Input","📸 Media Gallery","🔍 Search ArXiv","📝 File 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 your detected input:", value=val_stripped, height=100)
+        run_option = st.selectbox("Select AI Model:", ["Arxiv", "GPT-4o", "Claude-3.5"])
+        col1, col2 = st.columns(2)
+        with col1:
+            autorun = st.checkbox("AutoRun on input change", value=False)
+        with col2:
+            full_audio = st.checkbox("Generate Complete Audio", value=False, 
+                                   help="Generate audio for the complete response including all papers and summaries")
+
+        input_changed = (val != st.session_state.old_val)
+
+        if autorun and input_changed:
+            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)
+        else:
+            if st.button("Process Input"):
+                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 == "🔍 Search ArXiv":
+        st.subheader("🔍 Search ArXiv")
+        q = st.text_input("Research query:")
+
+        st.markdown("### 🎛️ Audio Generation Options")
+        vocal_summary = st.checkbox("🎙️ Vocal Summary (Short Answer)", value=True)
+        extended_refs = st.checkbox("📜 Extended References & Summaries (Long)", value=False)
+        titles_summary = st.checkbox("🔖 Paper Titles Only", value=True)
+        full_audio = st.checkbox("📚 Generate Complete Audio Response", value=False,
+                               help="Generate audio for the complete response including all papers and summaries")
+
+        if q and st.button("Run ArXiv Query"):
+            perform_ai_lookup(q, vocal_summary=vocal_summary, extended_refs=extended_refs, 
+                            titles_summary=titles_summary, full_audio=full_audio)
+
+    elif tab_main == "🎤 Voice Input":
+        st.subheader("🎤 Voice Recognition")
+        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 Gallery":
+        st.header("🎬 Media Gallery - Images and 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 == "📝 File 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.")
+
+    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:
+                with open(f, "rb") as file:
+                    b64 = base64.b64encode(file.read()).decode()
+                dl_link = f'<a href="data:file/{ext};base64,{b64}" download="{fname}">Download {fname}</a>'
+                st.markdown(dl_link, unsafe_allow_html=True)
+        if st.button("Close Group View"):
+            st.session_state.viewing_prefix = None
+
+    if st.session_state.should_rerun:
+        st.session_state.should_rerun = False
+        st.rerun()
+
+if __name__=="__main__":
+    main()