app.py

# app_gradio.py
# ──────────────────────────────────────────────────────────
# Webcam → geometric detector → static WAV alert (with cooldown)
# Live console logs of per-frame latency + status.
#
# EDITED: This version uses a more robust method for audio playback
# in Gradio by dynamically creating the Audio component.
# ──────────────────────────────────────────────────────────
import time
import os
import yaml
import logging
import numpy as np
import gradio as gr
import soundfile as sf
from dotenv import load_dotenv

# This is a mock factory and detector for demonstration.
# Replace with your actual import.
from src.detection.factory import get_detector

# ───────────────────────────── logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s │ %(message)s",
    datefmt="%H:%M:%S",
)

# ───────────────────────────── config / detector
load_dotenv()
with open("config.yaml") as f:
    CFG = yaml.safe_load(f)

detector = get_detector(CFG)

# ───────────────────────────── Alert Manager Class <--- CHANGE
# Encapsulating the alert logic makes the code much cleaner.
# It handles its own state (last alert time) internally.
class AlertManager:
    def __init__(self, config):
        self.cooldown_seconds = config.get("alert_cooldown_seconds", 5)
        self.last_alert_time = 0
        self.alert_data = None
        self.sample_rate = None
        # --- NEW: State variable to track if an alert is active ---
        self.is_alert_active = False
        self._load_sound(config.get("alert_sound_path"))

    def _load_sound(self, wav_path):
        if not wav_path:
            logging.warning("No 'alert_sound_path' found in config.")
            return
        try:
            # Load as int16 to avoid the Gradio conversion warning
            data, sr = sf.read(wav_path, dtype="int16")
            self.alert_data = data
            self.sample_rate = sr
            logging.info(f"Loaded alert sound: {wav_path} ({len(self.alert_data)/self.sample_rate:.2f}s)")
        except Exception as e:
            logging.error(f"Failed to load alert sound: {e}")
            self.alert_data = None

    def trigger_alert(self, level, lighting):
        """
        Checks conditions and returns audio payload if a new alert should fire.
        This is now stateful.
        """
        # --- NEW LOGIC: Part 1 ---
        # If an alert is currently active, we do nothing until the user is 'Awake'.
        if self.is_alert_active:
            if level == "Awake":
                logging.info("✅ Alert state reset. User is Awake. Re-arming system.")
                self.is_alert_active = False
            return None # Important: Return None to prevent any sound

        # --- ORIGINAL LOGIC (with a small change) ---
        # If no alert is active, check for conditions to fire a new one.
        is_drowsy = level != "Awake"
        is_good_light = lighting != "Low"
        # The time-based cooldown is still useful to prevent flickering alerts.
        is_ready = (time.monotonic() - self.last_alert_time) > self.cooldown_seconds

        if self.alert_data is not None and is_drowsy and is_good_light and is_ready:
            self.last_alert_time = time.monotonic()
            # --- NEW LOGIC: Part 2 ---
            # Set the alert to active so it doesn't fire again immediately.
            self.is_alert_active = True
            logging.info("🔊 Drowsiness detected! Firing alert and setting state to active.")
            return (self.sample_rate, self.alert_data.copy())

        return None
# Initialize the alert manager
alert_manager = AlertManager(CFG["alerting"])

# ───────────────────────────── frame processing <--- MAJOR CHANGE
# Simplified by the AlertManager. No longer needs to pass 'last_alert_ts' back and forth.
def process_live_frame(frame):
    if frame is None:
        return (
            np.zeros((480, 640, 3), dtype=np.uint8),
            "Status: Inactive",
            None # No audio output
        )

    t0 = time.perf_counter()

    try:
        # Assuming your detector returns (processed_image, indicators_dict)
        processed, indic = detector.process_frame(frame)
    except Exception as e:
        logging.error(f"Error processing frame: {e}")
        processed = np.zeros_like(frame)
        indic = {"drowsiness_level": "Error", "lighting": "Unknown", "details": {"Score": 0.0}}

    level = indic.get("drowsiness_level", "Awake")
    lighting = indic.get("lighting", "Good")
    score = indic.get("details", {}).get("Score", 0.0)

    dt_ms = (time.perf_counter() - t0) * 1000.0
    logging.info(f"{dt_ms:6.1f} ms │ {lighting:<4} │ {level:<14} │ score={score:.2f}")

    status_txt = (
        f"Lighting: {lighting}\n"
        + ("Detection paused – low light." if lighting == "Low"
           else f"Status: {level}\nScore: {score:.2f}")
    )

    # Check for an alert and get the audio payload if ready
    audio_payload = alert_manager.trigger_alert(level, lighting)

    # This is the key: return a new gr.Audio component when an alert fires.
    # Otherwise, return None to clear the component on the frontend.
    if audio_payload:
        return processed, status_txt, gr.Audio(value=audio_payload, autoplay=True)
    else:
        return processed, status_txt, None

# ───────────────────────────── UI <--- CHANGE
with gr.Blocks(title="Drive Paddy – Drowsiness Detection") as app:
    gr.Markdown("# 🚗 **Drive Paddy** – Robust Alert Demo")
    gr.Markdown("Webcam-based drowsiness detection · console shows real-time logs.")

    with gr.Row():
        with gr.Column(scale=2):
            cam = gr.Image(sources=["webcam"], streaming=True, label="Live Camera Feed")
        with gr.Column(scale=1):
            out_img = gr.Image(label="Processed Feed")
            out_text = gr.Textbox(label="Live Status", lines=3, interactive=False)
            
            # This audio component now acts as a placeholder.
            # We make it invisible because we don't need to show the player controls.
            # The backend will dynamically send a new, playable component to it.
            out_audio = gr.Audio(
                label="Alert",
                autoplay=True,
                visible=False, # Hiding the component for a cleaner UX
            )

    # The gr.State for managing the timestamp is no longer needed, simplifying the stream call.
    cam.stream(
        fn=process_live_frame,
        inputs=[cam],
        outputs=[out_img, out_text, out_audio] # The output now targets the placeholder
    )

if __name__ == "__main__":
    logging.info("Launching Gradio app…")
    app.launch(debug=True)