app.py

import gradio as gr
from qa_engine import load_index, build_chain
from clipper import clip
from index_builder import build_index
from logging_config import logger
import os
import json
import time
import subprocess

# Global variables
store = None
qa_chain = None
SOURCE_AUDIO = None
model_name = "phi3"  # Default to phi3 which is local
index_loaded = False

# --- load at startup (may not exist on first run) ---
try:
    if os.path.exists("data"):
        store, segments = load_index("data")
        if store:
            qa_chain = build_chain(store, model_name)
            SOURCE_AUDIO = "downloads/audio.mp3"
            index_loaded = True
            logger.info("Successfully loaded existing index")
except Exception as e:
    logger.warning("No existing index found or error loading index: %s. Upload a media file to build one.", str(e))
    store = qa_chain = None
    SOURCE_AUDIO = None
    index_loaded = False


def _fmt(sec: float) -> str:
    h = int(sec // 3600)
    m = int((sec % 3600) // 60)
    s = int(sec % 60)
    return f"{h:02d}:{m:02d}:{s:02d}"


def update_progress(progress: int, message: str):
    """Helper to update progress bar"""
    return f"<script>updateProgress({progress}, '{message}')</script>"


def handle(question: str):
    global qa_chain, store, SOURCE_AUDIO
    
    logger.info(f"Handling question: {question}")
    
    if not store:
        msg = "⚠️ No vector store found. Please upload a media file first."
        logger.warning(msg)
        return None, msg, update_progress(0, "Waiting for input...")
        
    if not qa_chain:
        msg = "⚠️ QA chain not initialized. Please select a model and try again."
        logger.warning(msg)
        return None, msg, update_progress(0, "Waiting for input...")
    
    if not question.strip():
        msg = "⚠️ Please enter a question."
        logger.warning(msg)
        return None, msg, update_progress(0, "Waiting for input...")

    try:
        # Update progress
        logger.info("Processing question...")
        yield None, "Processing your question...", update_progress(20, "Analyzing question...")
        
        # Query the QA chain
        logger.info(f"Querying QA chain with question: {question}")
        result = qa_chain({"question": question}, return_only_outputs=True)
        logger.info(f"QA chain result: {result}")
        
        # Extract the answer and source documents
        answer = result.get("answer", "No answer found.")
        source_docs = result.get("source_documents", [])
        logger.info(f"Found {len(source_docs)} source documents")
        
        if not source_docs:
            msg = "ℹ️ No relevant content found in the audio."
            logger.info(msg)
            yield None, msg, update_progress(100, "No results found")
            return
            
        # Get the first document's metadata for timestamp
        metadata = source_docs[0].metadata
        logger.info(f"Source document metadata: {metadata}")
        
        start_time = float(metadata.get("start", 0))
        end_time = start_time + 30  # 30-second clip
        
        # Format timestamp
        start_str = f"{int(start_time // 60)}:{int(start_time % 60):02d}"
        end_str = f"{int(end_time // 60)}:{int(end_time % 60):02d}"
        
        logger.info(f"Extracting clip from {start_str} to {end_str}...")
        yield None, f"Extracting clip from {start_str} to {end_str}...", update_progress(75, "Extracting audio...")
        
        try:
            logger.info(f"Calling clip() with source: {SOURCE_AUDIO}, start: {start_time}, end: {end_time}")
            clip_path = clip(SOURCE_AUDIO, start_time, end_time)
            logger.info(f"Clip created at: {clip_path}")
            
            if not clip_path or not os.path.exists(clip_path):
                error_msg = f"Failed to create clip at {clip_path}"
                logger.error(error_msg)
                raise FileNotFoundError(error_msg)
                
            success_msg = f"🎧 Clip from {start_str} to {end_str}"
            logger.info(success_msg)
            yield clip_path, success_msg, update_progress(100, "Done!")
            
        except Exception as e:
            error_msg = f"❌ Error creating audio clip: {str(e)}"
            logger.error(error_msg, exc_info=True)
            yield None, error_msg, update_progress(0, "Error creating clip")

    except Exception as e:
        error_msg = f"❌ Error processing question: {str(e)}"
        logger.error(error_msg, exc_info=True)
        yield None, error_msg, update_progress(0, "Error occurred")


def upload_media(file, progress=gr.Progress()):
    """Build index from uploaded media and refresh QA chain."""
    global SOURCE_AUDIO, qa_chain, store, model_name
    
    if file is None:
        logger.error("No file was uploaded")
        return "❌ Error: No file was uploaded."
    
    try:
        progress(0.1, desc="Starting upload...")
        
        # Get the actual file path
        file_path = file.name if hasattr(file, 'name') else str(file)
        logger.info(f"Processing uploaded file: {file_path}")
        
        # Ensure the file exists
        if not os.path.exists(file_path):
            error_msg = f"File not found at path: {file_path}"
            logger.error(error_msg)
            return f"❌ Error: {error_msg}"
            
        # Convert to MP3 if needed
        if not file_path.lower().endswith('.mp3'):
            progress(0.2, desc="Converting to MP3 format...")
            logger.info("Converting file to MP3 format...")
            base_name = os.path.splitext(file_path)[0]
            audio_path = f"{base_name}.mp3"
            
            try:
                # Use ffmpeg to convert to MP3
                cmd = [
                    'ffmpeg',
                    '-i', file_path,  # Input file
                    '-q:a', '0',     # Best quality
                    '-map', 'a',     # Only audio
                    '-y',            # Overwrite output file if it exists
                    audio_path       # Output file
                ]
                result = subprocess.run(cmd, capture_output=True, text=True)
                
                if result.returncode != 0:
                    error_msg = f"Failed to convert file to MP3: {result.stderr}"
                    logger.error(error_msg)
                    return f"❌ Error: {error_msg}"
                    
                file_path = audio_path
                logger.info(f"Successfully converted to MP3: {file_path}")
                
            except Exception as e:
                error_msg = f"Error during MP3 conversion: {str(e)}"
                logger.error(error_msg, exc_info=True)
                return f"❌ {error_msg}"
        
        # Set the global audio source
        SOURCE_AUDIO = file_path
        
        # Create data directory if it doesn't exist
        data_dir = "data"
        os.makedirs(data_dir, exist_ok=True)
        
        # Build the index
        progress(0.4, desc="Transcribing audio with Whisper (this may take a few minutes)...")
        logger.info("Starting transcription and index building...")
        
        try:
            # Build the index from the audio file
            store = build_index(file_path, data_dir)
            
            if not store:
                error_msg = "Failed to build index - no documents were processed"
                logger.error(error_msg)
                return f"❌ {error_msg}"
            
            # Initialize QA chain with the model and store
            progress(0.9, desc="Initializing QA system...")
            logger.info("Initializing QA chain...")
            
            qa_chain = build_chain(store, model_name)
            
            if not qa_chain:
                error_msg = "Failed to initialize QA chain"
                logger.error(error_msg)
                return f"❌ {error_msg}"
            
            progress(1.0, desc="Ready!")
            success_msg = f"✅ Ready! Successfully processed {os.path.basename(file_path)}"
            logger.info(success_msg)
            return success_msg
            
        except Exception as e:
            error_msg = f"Error during index building: {str(e)}"
            logger.error(error_msg, exc_info=True)
            return f"❌ {error_msg}"
        
    except Exception as e:
        error_msg = f"Unexpected error: {str(e)}"
        logger.error(error_msg, exc_info=True)
        return f"❌ {error_msg}"


def tail_log(n: int = 200):
    """Return last n log entries pretty-printed JSON."""
    path = os.path.join(os.path.dirname(__file__), "langchain_debug.jsonl")
    if not os.path.exists(path):
        return "{}"  # empty JSON
    with open(path, "r", encoding="utf-8") as f:
        raw = f.readlines()[-n:]
    objs = []
    for ln in raw:
        try:
            objs.append(json.loads(ln))
        except json.JSONDecodeError:
            continue
    return "\n\n".join(json.dumps(o, indent=2) for o in objs)


with gr.Blocks() as demo:
    # Enable queue for async operations and generators
    demo.queue()
    with gr.Tab("Ask"):
        gr.Markdown("# ClipQuery: Upload any audio/video and ask questions about it. ")
        gr.Markdown("### The clip will be extracted from the point in the media where the answer most likely occurs.")
        
        with gr.Row():
            with gr.Column(scale=3):
                # Model selection
                model_dd = gr.Dropdown(
                    ["flan-t5-base (HuggingFace)", "phi3 (Local - requires Ollama)", "tinyllama (Local - requires Ollama)"],
                    label="Select Model",
                    value="phi3 (Local - requires Ollama)"
                )
            with gr.Column(scale=2):
                # Hugging Face Token input (initially hidden)
                hf_token = gr.Textbox(
                    label="Hugging Face Token (required for flan-t5-base)",
                    type="password",
                    visible=False,
                    placeholder="Enter your Hugging Face token..."
                )
        
        def toggle_token_visibility(model_name):
            return gr.update(visible="flan-t5-base" in model_name)
        
        model_dd.change(
            fn=toggle_token_visibility,
            inputs=model_dd,
            outputs=hf_token
        )
        
        # Initial token visibility check
        toggle_token_visibility(model_dd.value)
        
        uploader = gr.File(label="Upload audio/video", file_types=["audio", "video"])
        status = gr.Markdown()
        inp = gr.Textbox(label="Ask a question")
        out_audio = gr.Audio()
        ts_label = gr.Markdown()
        
        # Progress tracker
        with gr.Row():
            progress = gr.HTML("""
            <div style='width: 100%; margin: 10px 0;'>
                <div style='display: flex; justify-content: space-between; margin-bottom: 5px;'>
                    <span id='status'>Ready</span>
                    <span id='progress'>0%</span>
                </div>
                <div style='height: 20px; background: #f0f0f0; border-radius: 10px; overflow: hidden;'>
                    <div id='progress-bar' style='height: 100%; width: 0%; background: #4CAF50; transition: width 0.3s;'></div>
                </div>
            </div>
            """)
            
        # JavaScript for progress updates
        js = """
        function updateProgress(progress, message) {
            const bar = document.getElementById('progress-bar');
            const percent = document.getElementById('progress');
            const status = document.getElementById('status');
            
            // Ensure progress is a number and has a default
            const progressValue = Number(progress) || 0;
            
            bar.style.width = progressValue + '%';
            percent.textContent = progressValue + '%';
            status.textContent = message || 'Processing...';
            
            if (progressValue >= 100) {
                bar.style.background = '#4CAF50';
                status.textContent = 'Done!';
            } else if (progressValue >= 75) {
                bar.style.background = '#2196F3';
            } else if (progressValue >= 50) {
                bar.style.background = '#FFC107';
            } else if (progressValue >= 25) {
                bar.style.background = '#FF9800';
            } else {
                bar.style.background = '#f44336';
            }
        }
        // Initialize on load
        document.addEventListener('DOMContentLoaded', function() {
            updateProgress(0, 'Ready');
        });
        """
        demo.load(fn=None, inputs=None, outputs=None, js=js)

        def _on_model_change(label, token):
            global model_name, qa_chain, store
            
            name = label.split()[0]  # drop suffix
            if name == model_name:
                return ""  # No change needed
                
            # Check if this is a local model that needs Ollama
            if name in ('phi3', 'tinyllama'):
                try:
                    import requests
                    response = requests.get('http://localhost:11434', timeout=5)
                    if response.status_code != 200:
                        raise ConnectionError("Ollama server not running. Please start it first.")
                except Exception as e:
                    return f"❌ Error: {str(e)}. Please make sure Ollama is running."
            
            if store is None and name != "flan-t5-base":
                return "⚠️ Please upload a media file before changing models."
                
            try:
                if name == "flan-t5-base" and not token:
                    return "⚠️ Please enter your Hugging Face token to use flan-t5-base. Get one at https://huggingface.co/settings/tokens"
                
                # Only pass the token if using flan-t5-base
                hf_token = token if name == "flan-t5-base" else None
                qa_chain = build_chain(store, name, hf_token)
                model_name = name  # Update the current model name
                return f"✅ Switched to {label}"
            except Exception as e:
                return f"❌ Failed to switch model: {str(e)}"
        model_dd.change(
            fn=_on_model_change,
            inputs=[model_dd, hf_token],
            outputs=status
        )

        uploader.change(
            fn=upload_media,
            inputs=uploader,
            outputs=status,
            api_name="upload_media"
        )
        inp.submit(
            fn=handle,
            inputs=inp,
            outputs=[out_audio, ts_label, progress],
            show_progress=False
        )

    with gr.Tab("Debug Log"):
        log_box = gr.Textbox(label="Application Logs", lines=25, max_lines=25, interactive=False)
        refresh_btn = gr.Button("Refresh Logs")
        
        def refresh_logs():
            from logging_config import get_logs
            logs = get_logs()
            return f"""
            ===== LATEST LOGS =====
            {logs[-5000:] if len(logs) > 5000 else logs}
            ======================
            """
            
        refresh_btn.click(refresh_logs, None, log_box)
        demo.load(refresh_logs, None, log_box, every=5)

if __name__ == "__main__":
    demo.launch()