Update app.py

app.py

@@ -1,9 +1,11 @@
 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
+from collections import defaultdict, deque
 from dotenv import load_dotenv
 from gradio_client import Client
 from huggingface_hub import InferenceClient
@@ -11,13 +13,14 @@ 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
 
-# 🧹 Clean up the environment and load keys
+# 🎯 1. Core Configuration & Setup
 st.set_page_config(
     page_title="🚲BikeAI🏆 Claude/GPT Research",
     page_icon="🚲🏆",
@@ -31,349 +34,616 @@ st.set_page_config(
 )
 load_dotenv()
 
+# 🔑 2. API Setup & Clients
 openai_api_key = os.getenv('OPENAI_API_KEY', "")
-anthropic_key = os.getenv('ANTHROPIC_API_KEY', "")
-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"]
+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')
 
-claude_client = anthropic.Anthropic(api_key=anthropic_key)
-# For GPT-4o calls
-openai_client = openai.ChatCompletion
-
-# 🧠 Session State
-for var in ['transcript_history','chat_history','openai_model','messages','last_voice_input',
-            'editing_file','edit_new_name','edit_new_content','viewing_prefix','should_rerun',
-            'old_val']:
-    if var not in st.session_state:
-        st.session_state[var] = [] if var.endswith('history') else None if var.startswith('view') else ""
-
-if not st.session_state.openai_model:
-    st.session_state.openai_model = "gpt-4-0613"  # Update to a stable GPT-4 model if needed
-
-# 🎨 Custom CSS
+# 📝 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; }
+    .stButton>button {
+        margin-right: 0.5rem;
+    }
 </style>
 """, unsafe_allow_html=True)
 
-# 🏷️ Helper for extracting high-info terms
+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'
+        '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'
     ])
-    text_lower = text.lower()
-    words = re.findall(r'\b\w+(?:-\w+)*\b', text_lower)
-    meaningful = [w for w in words if len(w)>3 and w not in stop_words and any(c.isalpha() for c in w)]
-    # Deduplicate while preserving order
-    seen = set()
-    uniq = [w for w in meaningful if not (w in seen or seen.add(w))]
-    return uniq[:5]
 
-# 🍱 Improved filename generation includes prompt & response terms
+    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"):
-    # Combine prompt & response for naming. The prompt terms have priority.
-    prompt_terms = get_high_info_terms(prompt)
-    response_terms = get_high_info_terms(response)
-    combined_terms = prompt_terms + [t for t in response_terms if t not in prompt_terms]
-    name_text = '_'.join(t.replace(' ','-') for t in combined_terms) or 'file'
-    # Limit length
-    name_text = name_text[:100]
-    prefix = datetime.now().strftime("%y%m_%H%M_")
-    return f"{prefix}{name_text}.{file_type}"
-
-# 🗣️ Clean text for speech
+    """
+    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:
-    text = text.replace("\n", " ").replace("</s>", " ").replace("#","")
+    """Clean text for speech synthesis"""
+    text = text.replace("\n", " ")
+    text = text.replace("</s>", " ")
+    text = text.replace("#", "")
     text = re.sub(r"\(https?:\/\/[^\)]+\)", "", text)
-    return re.sub(r"\s+", " ", text).strip()
+    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):
+    """Generate audio using Edge TTS"""
     text = clean_for_speech(text)
-    if not text.strip(): return None
+    if not text.strip():
+        return None
     rate_str = f"{rate:+d}%"
     pitch_str = f"{pitch:+d}Hz"
-    com = edge_tts.Communicate(text, voice, rate=rate_str, pitch=pitch_str)
+    communicate = edge_tts.Communicate(text, voice, rate=rate_str, pitch=pitch_str)
     out_fn = generate_filename(text, text, "mp3")
-    await com.save(out_fn)
+    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)
-        enc = base64.b64encode(open(file_path,"rb").read()).decode()
-        st.markdown(f'<a href="data:audio/mpeg;base64,{enc}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}</a>', unsafe_allow_html=True)
-
-# 🧰 Code execution environment
-context = {}
-
-# 🛠️ Executes Python code blocks safely, returning combined output
-def execute_python_blocks(response):
-    combined = ""
-    code_blocks = re.findall(r'```(?:python\s*)?([\s\S]*?)```', response, re.IGNORECASE)
-    for code_block in code_blocks:
-        old_stdout = sys.stdout
-        sys.stdout = io.StringIO()
-        try:
-            exec(code_block, context)
-            code_output = sys.stdout.getvalue()
-            combined += f"# Code Execution Output\n```\n{code_output}\n```\n\n"
-            st.code(code_output)
-        except Exception as e:
-            combined += f"# Execution Error\n```python\n{e}\n```\n\n"
-        finally:
-            sys.stdout = old_stdout
-    return combined
-
-# 🗃️ Creates & saves a file with prompt/response and executed code results
-def create_file(filename, prompt, response):
-    base, ext = os.path.splitext(filename)
-    content = f"# Prompt 📝\n{prompt}\n\n# Response 💬\n{response}\n\n"
-    # Execute code in response
-    exec_results = execute_python_blocks(response)
-    content += exec_results
-    # Save
-    with open(f"{base}.md", 'w', encoding='utf-8') as file:
-        file.write(content)
-    # Download link
-    with open(f"{base}.md", 'rb') as file:
-        encoded = base64.b64encode(file.read()).decode()
-    href = f'<a href="data:file/markdown;base64,{encoded}" download="{filename}">Download File 📄</a>'
-    st.markdown(href, unsafe_allow_html=True)
-
-# 🎨 Unified AI call helper
-def call_model(model_type, text):
-    # model_type: "Arxiv", "GPT-4", or "Claude"
-    # Returns (answer, filename)
-    if model_type == "Claude":
-        try:
-            r = claude_client.completions.create(
-                prompt=anthropic.HUMAN_PROMPT + text + anthropic.AI_PROMPT, 
-                model="claude-instant-1", 
-                max_tokens_to_sample=1000
-            )
-            ans = r.completion.strip()
-        except Exception as e:
-            ans = f"Error calling Claude: {e}"
-    elif model_type == "Arxiv":
-        # ArXiv RAG flow
-        client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-        refs = client.predict(text,20,"Semantic Search","mistralai/Mixtral-8x7B-Instruct-v0.1",api_name="/update_with_rag_md")[0]
-        r2 = client.predict(text,"mistralai/Mixtral-8x7B-Instruct-v0.1",True,api_name="/ask_llm")
-        ans = f"### 🔎 {text}\n\n{r2}\n\n{refs}"
-    else:
-        # GPT-4o call
-        try:
-            c = openai_client.create(
-                model=st.session_state.openai_model,
-                messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":text}]
-            )
-            ans = c.choices[0].message.content.strip()
-        except Exception as e:
-            ans = f"Error calling GPT-4: {e}"
-    filename = generate_filename(text, ans, "md")
-    create_file(filename, text, ans)
-    return ans
-
-# 🎶 Audio response options
-def handle_audio_generation(q, ans, vocal_summary=True, extended_refs=False, titles_summary=True, full_audio=False):
-    # This is used only for ArXiv results
-    if not ans.startswith("### 🔎"):
-        return
-    # Extract sections for audio generation
-    # The main short answer is r2: We'll approximate by splitting at double-newline.
-    parts = ans.split("\n\n")
-    if len(parts)>2:
-        short_answer = parts[1]
-        refs = parts[-1]
-    else:
-        short_answer = ans
-        refs = ""
-
+        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(short_answer)} {clean_for_speech(refs)}"
-        f_all = speak_with_edge_tts(complete_text)
-        st.write("### 📚 Complete Audio Response")
-        play_and_download_audio(f_all)
+        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_audio = speak_with_edge_tts(short_answer)
-        st.write("### 🎙️ Vocal Summary")
-        play_and_download_audio(main_audio)
+        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")
+
+    # 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
 
-    if extended_refs and refs.strip():
-        ref_audio = speak_with_edge_tts("Here are the extended references: " + refs)
-        st.write("### 📜 Extended References & Summaries")
-        play_and_download_audio(ref_audio)
+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
 
-    if titles_summary and refs.strip():
-        titles = [m.group(1) for line in refs.split('\n') for m in [re.search(r"\[([^\]]+)\]", line)] if m]
-        if titles:
-            titles_text = "Here are the titles of the papers: " + ", ".join(titles)
-            t_audio = speak_with_edge_tts(titles_text)
-            st.write("### 🔖 Paper Titles")
-            play_and_download_audio(t_audio)
+# 📂 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
 
-# 🏦 File Management
-def create_zip_of_files():
-    md_files = glob.glob("*.md")
-    mp3_files = glob.glob("*.mp3")
-    if not (md_files or mp3_files): return None
-    all_files = md_files+mp3_files
-    # Derive name from their content
-    text_combined = ""
+    # Collect content for high-info term extraction
+    all_content = []
     for f in all_files:
-        if f.endswith(".md"):
-            text_combined += open(f,'r',encoding='utf-8').read()
-    terms = get_high_info_terms(text_combined)
-    name_text = '_'.join(terms[:3]) or 'archive'
-    zip_name = f"{datetime.now().strftime('%y%m_%H%M')}_{name_text}.zip"
+        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 = glob.glob("*.md")
-    mp3 = glob.glob("*.mp3")
-    allf = md+mp3
+    """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 allf:
-        prefix = os.path.basename(f)[:10]
+    for f in all_files:
+        fname = os.path.basename(f)
+        prefix = fname[:10]
         groups[prefix].append(f)
-    sorted_pref = sorted(groups.keys(), key=lambda pre: max(os.path.getmtime(x) for x in groups[pre]), reverse=True)
-    return groups, sorted_pref
 
-def display_file_manager_sidebar():
-    st.sidebar.title("🎵 Audio & Document Manager")
-    groups, sorted_pref = load_files_for_sidebar()
+    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
 
-    all_md = [f for f in glob.glob("*.md") if os.path.basename(f).lower()!='readme.md']
-    all_mp3 = glob.glob("*.mp3")
+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("🗑 Del All MD"):
-            for f in all_md: os.remove(f)
+        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("🗑 Del All MP3"):
-            for f in all_mp3: os.remove(f)
+        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("⬇️ Zip All"):
-            z = create_zip_of_files()
+        if st.button("⬇️ ZipAll"):
+            z = create_zip_of_files(all_md, all_mp3)
             if z:
-                with open(z,"rb") as f:
-                    b64 = base64.b64encode(f.read()).decode()
-                st.sidebar.markdown(f'<a href="data:file/zip;base64,{b64}" download="{os.path.basename(z)}">📂 Download {os.path.basename(z)}</a>', unsafe_allow_html=True)
+                st.sidebar.markdown(get_download_link(z),unsafe_allow_html=True)
 
-    for prefix in sorted_pref:
+    for prefix in sorted_prefixes:
         files = groups[prefix]
-        # Extract simple keywords
-        txt = ""
-        for f in files:
-            if f.endswith(".md"):
-                txt+=open(f,'r',encoding='utf-8').read()+" "
-        kw = get_high_info_terms(txt)
-        kw_str = " ".join(kw) if kw else "No Keywords"
-        with st.sidebar.expander(f"{prefix} Files ({len(files)}) - {kw_str}", expanded=True):
+        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("👀View Group", key="view_"+prefix):
+                if st.button("👀ViewGrp", key="view_group_"+prefix):
                     st.session_state.viewing_prefix = prefix
             with c2:
-                if st.button("🗑Del Group", key="del_"+prefix):
-                    for f in files: os.remove(f)
-                    st.success(f"Deleted group {prefix}")
+                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"**{os.path.basename(f)}** - {ctime}")
-
-    # Viewing group
-    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]:
-            ext = f.split('.')[-1].lower()
-            st.write(f"### {os.path.basename(f)}")
-            if ext == "md":
-                c = open(f,'r',encoding='utf-8').read()
-                st.markdown(c)
-            elif ext == "mp3":
-                st.audio(f)
-            else:
-                with open(f,"rb") as fil:
-                    enc = base64.b64encode(fil.read()).decode()
-                st.markdown(f'<a href="data:file/{ext};base64,{enc}" download="{os.path.basename(f)}">Download {os.path.basename(f)}</a>', unsafe_allow_html=True)
-        if st.button("Close Group View"):
-            st.session_state.viewing_prefix = None
+                st.write(f"**{fname}** - {ctime}")
 
+# 🎯 11. Main Application
 def main():
-    st.sidebar.markdown("### 🚲BikeAI🏆 Multi-Agent Research AI")
-    tab_main = st.radio("Action:",["🎤 Voice Input","🔍 Search ArXiv","📝 File Editor"],horizontal=True)
+    st.sidebar.markdown("### 🚲BikeAI🏆 Multi-Agent Research")
+    tab_main = st.radio("Action:",["🎤 Voice","📸 Media","🔍 ArXiv","📝 Editor"],horizontal=True)
 
-    # A small custom component hook (if used)
     mycomponent = components.declare_component("mycomponent", path="mycomponent")
     val = mycomponent(my_input_value="Hello")
 
-    # If we have component input, show controls
+    # Show input in a text box for editing if detected
     if val:
-        edited_input = st.text_area("Edit your detected input:", value=val.strip().replace('\n',' '), height=100)
-        run_option = st.selectbox("Select AI Model:", ["Arxiv", "GPT-4", "Claude"])
+        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)
         with col1:
-            autorun = st.checkbox("AutoRun on input change", value=False)
+            autorun = st.checkbox("⚙ AutoRun", value=False)
         with col2:
-            full_audio = st.checkbox("Generate Complete Audio", value=False)
+            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) or st.button("Process Input"):
+        if autorun and input_changed:
             st.session_state.old_val = val
-            ans = call_model("Arxiv" if run_option=="Arxiv" else ("Claude" if run_option=="Claude" else "GPT-4"), edited_input)
-            if run_option=="Arxiv":
-                # Audio generation for Arxiv
-                handle_audio_generation(edited_input, ans, True, False, True, full_audio)
-
-    if tab_main == "🔍 Search ArXiv":
-        q = st.text_input("Research query:")
-        st.markdown("### 🎛️ Audio Generation Options")
-        vocal_summary = st.checkbox("Short Answer Audio", True)
-        extended_refs = st.checkbox("Extended References Audio", False)
-        titles_summary = st.checkbox("Paper Titles Audio", True)
-        full_audio = st.checkbox("Full Audio Response", False)
-
-        if q and st.button("Run ArXiv Query"):
-            ans = call_model("Arxiv", q)
-            handle_audio_generation(q, ans, vocal_summary, extended_refs, titles_summary, full_audio)
-
-    elif tab_main == "🎤 Voice Input":
-        user_text = st.text_area("Message:", height=100)
-        user_text = user_text.strip()
-        if st.button("Send 📨"):
-            ans = call_model("GPT-4", user_text)
-            st.session_state.messages.append({"role":"user","content":user_text})
-            st.session_state.messages.append({"role":"assistant","content":ans})
+            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")
-        for m in st.session_state.messages:
-            with st.chat_message(m["role"]):
-                st.markdown(m["content"])
-
-    elif tab_main == "📝 File Editor":
-        # For simplicity, user selects from sidebar
-        st.write("Select a file from the sidebar to edit.")
+        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.")
+
+    groups, sorted_prefixes = load_files_for_sidebar()
+    display_file_manager_sidebar(groups, sorted_prefixes)
 
-    # Display File Manager
-    display_file_manager_sidebar()
+    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