#!/usr/bin/env python3
"""
scripts/data_watcher.py

Watches the data/ directories and auto-triggers detector training when new images arrive.
Requires: pip install watchdog requests
"""
import os
import time
import json
import requests
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler

API_URL = os.environ.get('BASE_URL', 'http://localhost:5000')
SETTINGS_FILE = 'settings.json'

class NewImageHandler(FileSystemEventHandler):
    def on_created(self, event):
        if event.is_directory:
            return
        ext = os.path.splitext(event.src_path)[1].lower()
        if ext in ('.jpg', '.jpeg', '.png'):
            print(f"[Watcher] New image detected: {event.src_path}")
            self.trigger_training()

    def trigger_training(self):
        # Load last training params
        try:
            with open(SETTINGS_FILE, 'r') as f:
                params = json.load(f)
        except Exception:
            print("[Watcher] Could not read settings.json; skipping auto-retrain")
            return
        if not params.get('auto_retrain', False):
            print("[Watcher] auto_retrain disabled in settings; skipping")
            return
        try:
            resp = requests.post(f"{API_URL}/api/train/detector", json=params)
            print(f"[Watcher] Auto-retrain triggered: {resp.status_code}, {resp.text}")
        except Exception as e:
            print(f"[Watcher] Error triggering retrain: {e}")

if __name__ == '__main__':
    paths = [
        'data/train/real',
        'data/train/morph',
        'data/val/real',
        'data/val/morph'
    ]
    event_handler = NewImageHandler()
    observer = Observer()
    for p in paths:
        if os.path.isdir(p):
            observer.schedule(event_handler, p, recursive=False)
            print(f"[Watcher] Monitoring {p}" )
    observer.start()
    try:
        while True:
            time.sleep(1)
    except KeyboardInterrupt:
        observer.stop()
    observer.join()