app.py

"""
Ask The Right Question - AI-powered Q&A Assistant

Continuous listening mode: Start listening, and questions automatically
generate as the talk progresses.
"""

import os
import tempfile
import threading
import numpy as np
import gradio as gr
from dotenv import load_dotenv

from src.transcription import transcribe_audio
from src.context import ConversationContext
from src.streaming_agent import generate_questions_sync, format_questions_html, format_activity_log

# Load environment variables
load_dotenv()

# Constants
SAMPLE_RATE = 16000
CHUNK_DURATION = 20  # Process audio every 20 seconds (longer = better context for Whisper)
OVERLAP_DURATION = 2  # Keep 2 seconds overlap between chunks to prevent word cutoffs
MIN_WORDS_FOR_QUESTIONS = 50  # Start generating questions after this many words
WORDS_BETWEEN_GENERATIONS = 50  # Generate new questions every N new words
MAX_WORDS_CAP = 500  # Stop processing new content after this many words (rate limiting)


def get_initial_state():
    """Create fresh initial state."""
    return {
        "context": ConversationContext(),
        "questions": [],
        "last_word_count": 0,
        "audio_buffer": np.array([], dtype=np.float32),
        "activity_log": [],
        "transcript": "",
        "questions_html": format_questions_html([]),
        "log_html": format_activity_log([]),
        "speaker_name": "",
        "generation_in_progress": False,  # Track if background generation is running
        "transcription_in_progress": False  # Track if background transcription is running
    }


def transcribe_audio_background(audio_data: np.ndarray, sample_rate: int, state: dict):
    """
    Transcribe audio in background thread.
    Updates state dict directly (shared reference).
    After transcription, may trigger question generation.
    """
    try:
        import wave

        # Save to temp file for Whisper
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
            with wave.open(f.name, 'wb') as wav:
                wav.setnchannels(1)
                wav.setsampwidth(2)
                wav.setframerate(sample_rate)
                wav.writeframes((audio_data * 32767).astype(np.int16).tobytes())
            temp_path = f.name

        # Transcribe
        new_text = transcribe_audio(temp_path)
        os.unlink(temp_path)

        if new_text.strip():
            ctx = state["context"]
            ctx.add_transcript(new_text)
            state["transcript"] = ctx.get_full_transcript()

            current_word_count = len(state["transcript"].split())
            words_since_last = current_word_count - state.get("last_word_count", 0)

            # Add transcription to activity log
            state["activity_log"].append({"type": "transcribe", "words": current_word_count})
            state["log_html"] = format_activity_log(state["activity_log"])

            # Check if we should generate questions (and not already generating)
            should_generate = (
                current_word_count >= MIN_WORDS_FOR_QUESTIONS and
                current_word_count < MAX_WORDS_CAP and
                words_since_last >= WORDS_BETWEEN_GENERATIONS and
                not state.get("generation_in_progress", False)
            )

            if should_generate:
                state["last_word_count"] = current_word_count
                state["generation_in_progress"] = True

                # Start question generation in another thread
                thread = threading.Thread(
                    target=generate_questions_background,
                    args=(state,),
                    daemon=True
                )
                thread.start()

    except Exception as e:
        state["activity_log"].append({"type": "error", "msg": f"Transcription error: {str(e)}"})
        state["log_html"] = format_activity_log(state["activity_log"])
    finally:
        state["transcription_in_progress"] = False


def generate_questions_background(state: dict):
    """
    Generate questions in background thread.
    Updates state dict directly (shared reference).
    """
    try:
        ctx = state["context"]
        speaker = state.get("speaker_name", "")
        activity_log = state.get("activity_log", [])

        questions, activity_log, questions_html, log_html = generate_questions_sync(
            context=ctx,
            existing_questions=state.get("questions", []),
            activity_log=activity_log,
            speaker_name=speaker
        )

        # Update state with results
        state["questions"] = questions
        state["questions_html"] = questions_html
        state["activity_log"] = activity_log
        state["log_html"] = log_html

    except Exception as e:
        state["activity_log"].append({"type": "error", "msg": str(e)})
        state["log_html"] = format_activity_log(state["activity_log"])
    finally:
        state["generation_in_progress"] = False


def process_streaming_audio(
    audio_chunk,
    state: dict
) -> tuple[str, str, str, str, dict]:
    """
    Process streaming audio chunk.

    Args:
        audio_chunk: Tuple of (sample_rate, audio_data) from gr.Audio streaming
        state: Session state

    Returns:
        Tuple of (transcript, agent_log, questions, status, state)
    """
    if audio_chunk is None:
        return (
            state.get("transcript", ""),
            state.get("log_html", format_activity_log([])),
            state.get("questions_html", format_questions_html([])),
            '<div class="status-bar">Waiting for audio...</div>',
            state
        )

    sample_rate, audio_data = audio_chunk

    # Initialize state if needed
    if "context" not in state:
        state = get_initial_state()

    activity_log = state.get("activity_log", [])

    # Convert to float32 if needed
    if audio_data.dtype != np.float32:
        if audio_data.dtype == np.int16:
            audio_data = audio_data.astype(np.float32) / 32768.0
        elif audio_data.dtype == np.int32:
            audio_data = audio_data.astype(np.float32) / 2147483648.0
        else:
            audio_data = audio_data.astype(np.float32)

    # Handle stereo
    if len(audio_data.shape) > 1:
        audio_data = audio_data.mean(axis=1)

    # Accumulate audio
    state["audio_buffer"] = np.concatenate([state.get("audio_buffer", np.array([], dtype=np.float32)), audio_data])

    # Calculate status info
    samples_needed = sample_rate * CHUNK_DURATION
    buffer_samples = len(state["audio_buffer"])
    buffer_pct = int(buffer_samples / samples_needed * 100)
    word_count = len(state.get("transcript", "").split())
    is_transcribing = state.get("transcription_in_progress", False)
    is_generating = state.get("generation_in_progress", False)

    # Build status parts
    status_parts = [f"🎤 Buffer: {buffer_pct}%", f"📝 {word_count} words"]
    if is_transcribing:
        status_parts.append("🔄 Transcribing...")
    if is_generating:
        status_parts.append("⚙️ Generating...")

    status = f'<div class="status-bar">{" | ".join(status_parts)}</div>'

    # Check if we have enough audio to process (CHUNK_DURATION seconds)
    if buffer_samples < samples_needed:
        return (
            state.get("transcript", ""),
            state.get("log_html", format_activity_log(activity_log)),
            state.get("questions_html", format_questions_html(state.get("questions", []))),
            status,
            state
        )

    # Check if transcription is already running (sequential queue)
    if is_transcribing:
        # Keep buffering, don't start new transcription yet
        return (
            state.get("transcript", ""),
            state.get("log_html", format_activity_log(activity_log)),
            state.get("questions_html", format_questions_html(state.get("questions", []))),
            status,
            state
        )

    # Process the buffered audio with overlap
    audio_to_process = state["audio_buffer"][:samples_needed].copy()  # Copy for thread safety
    # Keep last OVERLAP_DURATION seconds for next chunk to prevent word cutoffs
    overlap_samples = sample_rate * OVERLAP_DURATION
    state["audio_buffer"] = state["audio_buffer"][samples_needed - overlap_samples:]

    # Start background transcription (non-blocking)
    state["transcription_in_progress"] = True
    thread = threading.Thread(
        target=transcribe_audio_background,
        args=(audio_to_process, sample_rate, state),
        daemon=True
    )
    thread.start()

    # Update status to show transcribing started
    status_parts = [f"🎤 Buffer: {int(overlap_samples / samples_needed * 100)}%", f"📝 {word_count} words", "🔄 Transcribing..."]
    if is_generating:
        status_parts.append("⚙️ Generating...")
    status = f'<div class="status-bar">{" | ".join(status_parts)}</div>'

    return (
        state.get("transcript", ""),
        state.get("log_html", format_activity_log(activity_log)),
        state.get("questions_html", format_questions_html(state.get("questions", []))),
        status,
        state
    )


def set_speaker_name(name: str, state: dict) -> dict:
    """Store speaker name in state."""
    if "context" not in state:
        state = get_initial_state()
    state["speaker_name"] = name
    return state


def add_manual_context(text: str, state: dict) -> tuple[str, str, str, str, dict]:
    """Add manually typed context and potentially trigger question generation."""
    if not text.strip():
        return (
            state.get("transcript", ""),
            state.get("log_html", format_activity_log([])),
            state.get("questions_html", format_questions_html([])),
            '<div class="status-bar">No text provided</div>',
            state
        )

    # Initialize state if needed
    if "context" not in state:
        state = get_initial_state()

    ctx = state["context"]
    activity_log = state.get("activity_log", [])

    ctx.add_transcript(text)
    state["transcript"] = ctx.get_full_transcript()

    current_word_count = len(state["transcript"].split())

    # Add to activity log
    activity_log.append({"type": "transcribe", "words": current_word_count})
    state["activity_log"] = activity_log

    # Check if word cap reached
    if current_word_count >= MAX_WORDS_CAP:
        state["log_html"] = format_activity_log(activity_log)
        return (
            state["transcript"],
            state["log_html"],
            state.get("questions_html", format_questions_html([])),
            f'<div class="status-bar" style="border-color:#f59e0b;">⚠️ Word limit reached ({MAX_WORDS_CAP} words). Clear session to continue.</div>',
            state
        )

    status = f'<div class="status-bar">Added text. Total: {current_word_count} words</div>'

    # Generate questions if enough content and not already generating
    if current_word_count >= MIN_WORDS_FOR_QUESTIONS and not state.get("generation_in_progress", False):
        state["last_word_count"] = current_word_count
        state["generation_in_progress"] = True

        # Start background thread for question generation (non-blocking)
        thread = threading.Thread(
            target=generate_questions_background,
            args=(state,),
            daemon=True
        )
        thread.start()

        status = f'<div class="status-bar">Added text. Total: {current_word_count} words | ⚙️ Generating questions...</div>'
    elif state.get("generation_in_progress", False):
        state["log_html"] = format_activity_log(activity_log)
        status = f'<div class="status-bar">Added text. Total: {current_word_count} words | ⚙️ Already generating...</div>'
    else:
        state["log_html"] = format_activity_log(activity_log)
        need_more = MIN_WORDS_FOR_QUESTIONS - current_word_count
        status = f'<div class="status-bar">Added text. Need {need_more} more words to start generating.</div>'

    return (
        state["transcript"],
        state["log_html"],
        state.get("questions_html", format_questions_html([])),
        status,
        state
    )


def clear_session(state: dict) -> tuple[str, str, str, str, dict]:
    """Clear all session data."""
    new_state = get_initial_state()
    return (
        "",
        format_activity_log([]),
        format_questions_html([]),
        '<div class="status-bar">Session cleared. Ready to listen.</div>',
        new_state
    )


# Custom CSS
custom_css = """
.header {
    text-align: center;
    padding: 15px;
    background: linear-gradient(135deg, #2d4a6f 0%, #1e3a5f 100%);
    color: white;
    border-radius: 10px;
    margin-bottom: 15px;
}
.header { position: relative; }
.header h1 { margin: 0; font-size: 24px; color: white !important; }
.header p { margin: 5px 0 0 0; opacity: 0.9; font-size: 14px; color: white !important; }
.header .info-btn {
    display: inline-block;
    margin-top: 12px;
    background: rgba(255,255,255,0.2);
    border: 1px solid rgba(255,255,255,0.4);
    color: white;
    padding: 6px 16px;
    border-radius: 20px;
    cursor: pointer;
    font-size: 13px;
    font-weight: 500;
    animation: pulse 2s ease-in-out infinite;
    box-shadow: 0 0 0 0 rgba(255,255,255,0.4);
    transition: background 0.2s;
}
.header .info-btn:hover {
    background: rgba(255,255,255,0.3);
    animation: none;
}
@keyframes pulse {
    0% { box-shadow: 0 0 0 0 rgba(255,255,255,0.5); }
    50% { box-shadow: 0 0 0 8px rgba(255,255,255,0); }
    100% { box-shadow: 0 0 0 0 rgba(255,255,255,0); }
}
.info-modal {
    display: none;
    position: fixed;
    top: 0; left: 0; right: 0; bottom: 0;
    background: rgba(0,0,0,0.5);
    z-index: 1000;
    justify-content: center;
    align-items: center;
}
.info-modal.show { display: flex; }
.info-modal-content {
    background: white;
    padding: 25px;
    border-radius: 12px;
    max-width: 600px;
    max-height: 80vh;
    overflow-y: auto;
    position: relative;
    margin: 20px;
    line-height: 1.6;
}
.info-modal-content h2 { margin-top: 0; color: #667eea; }
.info-modal-content h3 { color: #764ba2; margin-top: 20px; }
.info-modal-content table { width: 100%; border-collapse: collapse; margin: 15px 0; font-size: 14px; }
.info-modal-content th, .info-modal-content td { padding: 8px 10px; text-align: left; border-bottom: 1px solid #eee; }
.info-modal-content th { background: #f8f9fa; font-weight: 600; }
.info-modal .close-btn {
    position: absolute;
    top: 10px;
    right: 15px;
    background: none;
    border: none;
    font-size: 24px;
    cursor: pointer;
    color: #666;
}
.info-modal .close-btn:hover { color: #333; }
.status-bar {
    background: #f0f9ff;
    padding: 8px 12px;
    border-radius: 6px;
    font-size: 13px;
    border-left: 3px solid #3b82f6;
}
"""

# Build the Gradio interface
with gr.Blocks(css=custom_css, title="Ask The Right Question") as demo:
    # Session state
    state = gr.State(get_initial_state())

    # Header with info button
    gr.HTML("""
    <div class="header">
        <h1>Ask The Right Question</h1>
        <p>Start listening and questions will generate automatically as the talk progresses</p>
        <br>
        <button class="info-btn" onclick="document.getElementById('infoModal').classList.add('show')">The problem we solve</button>
    </div>

    <!-- Info Modal -->
    <div id="infoModal" class="info-modal" onclick="if(event.target===this) this.classList.remove('show')">
        <div class="info-modal-content">
            <button class="close-btn" onclick="document.getElementById('infoModal').classList.remove('show')">&times;</button>
            <h2>Ask The Right Question</h2>
            <p><strong>AI-powered assistant that helps you ask insightful questions during Q&A sessions at conferences, classes, and talks.</strong></p>

            <h3>The Problem</h3>
            <p>You're at a conference or talk. The speaker opens the floor for questions. Your mind goes blank. You want to ask something meaningful but coming up with good questions on the spot is hard.</p>

            <h3>How It Works</h3>
            <ol>
                <li><strong>Listen</strong> - Record audio or paste text from the talk</li>
                <li><strong>Research</strong> - AI researches speaker background and fact-checks claims</li>
                <li><strong>Generate</strong> - Get thoughtful questions with explanations of why they're valuable</li>
            </ol>

            <h3>Question Types</h3>
            <table>
                <tr><th>Type</th><th>Purpose</th></tr>
                <tr><td>CLARIFICATION</td><td>Seeks to understand better</td></tr>
                <tr><td>DEPTH</td><td>Explores a topic more deeply</td></tr>
                <tr><td>CONNECTION</td><td>Links to other fields or ideas</td></tr>
                <tr><td>CHALLENGE</td><td>Probes assumptions respectfully</td></tr>
                <tr><td>PRACTICAL</td><td>Asks about real-world application</td></tr>
                <tr><td>FORWARD</td><td>Explores future implications</td></tr>
            </table>

            <h3>Usage Limits</h3>
            <p style="font-size:14px; color:#666;">To manage API costs, each session is limited to <strong>500 words</strong> of transcript. Questions generate every ~50 new words. Click "Clear & Reset" to start a new session.</p>

            <p style="margin-top:20px; font-size:13px; color:#666;">Built for HuggingFace MCP Hackathon | Claude + Whisper + Tavily</p>
        </div>
    </div>
    """)

    # Status bar
    status_display = gr.HTML(
        value='<div class="status-bar">Ready to listen. Click the microphone or paste text to begin.</div>'
    )

    with gr.Row():
        # Left column - Input & Transcript
        with gr.Column(scale=1):
            gr.Markdown("### Input")

            with gr.Tab("Live Audio"):
                audio_input = gr.Audio(
                    sources=["microphone"],
                    type="numpy",
                    streaming=True,
                    label="Click to start listening"
                )
                gr.Markdown("*Audio is processed every 20 seconds*")

            with gr.Tab("Paste Text"):
                manual_text = gr.Textbox(
                    label="Paste transcript or notes",
                    placeholder="Paste content from the talk here...",
                    lines=4
                )
                add_text_btn = gr.Button("Add Context", variant="secondary")

            speaker_name = gr.Textbox(
                label="Speaker Name (optional)",
                placeholder="e.g., Dr. Jane Smith - enables background research"
            )

            gr.Markdown("### Transcript")
            transcript_display = gr.Textbox(
                label="Accumulated Content",
                lines=10,
                interactive=False,
                placeholder="Content will appear here as you listen..."
            )

            clear_btn = gr.Button("Clear & Reset", variant="stop")

        # Middle column - Agent Activity
        with gr.Column(scale=1):
            gr.Markdown("### Agent Activity")
            agent_log = gr.HTML(
                value=format_activity_log([])
            )

        # Right column - Questions
        with gr.Column(scale=1):
            gr.Markdown("### Questions (auto-generated)")
            questions_output = gr.HTML(
                value=format_questions_html([])
            )

    # Footer
    gr.Markdown("""
    ---
    **How it works:**
    1. Click the microphone to start continuous listening (or paste text)
    2. As content accumulates (50+ words), questions automatically generate
    3. New questions appear every ~30 words of new content
    4. Add speaker name for personalized background research

    **Question types:** CLARIFY | DEPTH | CONNECT | CHALLENGE | PRACTICAL | FORWARD

    ---
    Built for HuggingFace MCP Hackathon | Claude + Whisper + Tavily
    """)

    # Event handlers
    audio_input.stream(
        fn=process_streaming_audio,
        inputs=[audio_input, state],
        outputs=[transcript_display, agent_log, questions_output, status_display, state]
    )

    speaker_name.change(
        fn=set_speaker_name,
        inputs=[speaker_name, state],
        outputs=[state]
    )

    add_text_btn.click(
        fn=add_manual_context,
        inputs=[manual_text, state],
        outputs=[transcript_display, agent_log, questions_output, status_display, state]
    ).then(
        fn=lambda: "",
        outputs=[manual_text]
    )

    clear_btn.click(
        fn=clear_session,
        inputs=[state],
        outputs=[transcript_display, agent_log, questions_output, status_display, state]
    )


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