"""
Flask Web Application for YOLOv12 Face Detection
Supports image upload, video upload, and live webcam streaming
"""

import base64
import logging
import os
from datetime import datetime
from pathlib import Path

import cv2
import numpy as np
from dotenv import load_dotenv
from flask import Flask, jsonify, render_template, request, send_file
from flask_limiter import Limiter
from flask_limiter.errors import RateLimitExceeded
from flask_limiter.util import get_remote_address
from flask_sqlalchemy import SQLAlchemy
from flask_sqlalchemy.model import Model
from sqlalchemy.exc import OperationalError
from werkzeug.utils import secure_filename

from face_detection_yolov12 import YOLOv12FaceDetector, detect_from_video

# Initialize Flask app
load_dotenv()

app = Flask(__name__, template_folder="../web/templates")

# Configuration
app.config["SQLALCHEMY_DATABASE_URI"] = os.getenv("DB_URL")
app.config["SECRET_KEY"] = os.getenv("SECRET_KEY", "fallback_secret_key_neu_khong_co")
app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False
app.config["SQLALCHEMY_ENGINE_OPTIONS"] = {"pool_recycle": 280}  # Giữ kết nối MySQL ổn định

db = SQLAlchemy(app)
PROJECT_ROOT = Path(__file__).parent.parent
UPLOAD_FOLDER = PROJECT_ROOT / "data" / "uploads"
MODELS_DIR = PROJECT_ROOT / "models"
ALLOWED_EXTENSIONS = {"jpg", "jpeg", "png", "gif", "mp4", "avi", "mov", "mkv"}
MAX_FILE_SIZE = 500 * 1024 * 1024  # 500MB
ALLOWED_MODELS = {
    "yolov12n-face.pt",
    "yolov12s-face.pt",
    "yolov12m-face.pt",
    "yolov12l-face.pt",
}
UPLOAD_FOLDER.mkdir(exist_ok=True)

app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
app.config["MAX_CONTENT_LENGTH"] = MAX_FILE_SIZE

limiter = Limiter(
    key_func=get_remote_address,
    app=app,
    default_limits=["10000 per day", "1000 per hour"],
    storage_uri="memory://",
)

# Model cache
detector_cache = {}


class Feedback(db.Model):  # type: ignore
    __tablename__ = "feedbacks"
    id = db.Column(db.Integer, primary_key=True)
    filename = db.Column(db.String(255))
    model_name = db.Column(db.String(50))
    rating = db.Column(db.Integer)
    comment = db.Column(db.Text)
    created_at = db.Column(db.DateTime, default=datetime.utcnow)


with app.app_context():
    try:
        db.create_all()
        print("Connect to MySQL successfully!")
    except Exception as e:
        print(f"Connection error: {e}")


def get_detector(model_name):
    """Get or create detector instance (cached)"""
    safe_name = secure_filename(model_name)

    if safe_name not in ALLOWED_MODELS:
        logging.error(f"Attempt to load unsupported model: {safe_name}")
        raise ValueError(f"Unsupported model: {safe_name}")

    if safe_name not in detector_cache:
        model_path = MODELS_DIR / safe_name

        try:
            final_path = model_path.resolve()
            safe_root = MODELS_DIR.resolve()
            if not str(final_path).startswith(str(safe_root)):
                logging.error(f"Security Alert: Symlink attack detected! {final_path}")
                raise ValueError("Invalid model path (Symlink violation)")

        except Exception as e:
            logging.error(f"Error resolving model path: {str(e)}")
            raise FileNotFoundError(f"Model path error: {str(e)}")

        if not final_path.exists():
            logging.error(f"Model file not found: {final_path}")
            raise FileNotFoundError(f"Model not found: {final_path}")

        detector_cache[safe_name] = YOLOv12FaceDetector(str(final_path))

    return detector_cache[safe_name]


def allowed_file(filename):
    """Check if file extension is allowed"""
    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS


def is_image(filename):
    """Check if file is image"""
    ext = filename.rsplit(".", 1)[1].lower()
    return ext in {"jpg", "jpeg", "png", "gif"}


def is_video(filename):
    """Check if file is video"""
    ext = filename.rsplit(".", 1)[1].lower()
    return ext in {"mp4", "avi", "mov", "mkv"}


@app.route("/")
def index():
    """Main page"""
    return render_template("index.html")


@app.route("/api/detect-image", methods=["POST"])
def detect_image():
    """Detect faces in uploaded image"""
    try:
        if "file" not in request.files:
            return jsonify({"error": "No file provided"}), 400

        file = request.files["file"]
        if file.filename == "":
            return jsonify({"error": "No file selected"}), 400

        if not allowed_file(file.filename) or not is_image(file.filename):
            return jsonify({"error": "Only image files allowed"}), 400

        # Get parameters
        model = request.form.get("model", "yolov12l-face.pt")
        blur = request.form.get("blur") == "true"

        if model not in ALLOWED_MODELS:
            app.logger.info(f"Invalid model '{model}' requested. Fallback to default.")
            model = "yolov12l-face.pt"

        # Get detector
        detector = get_detector(model)

        # Read image directly from file object
        image_data = file.read()
        nparr = np.frombuffer(image_data, np.uint8)
        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

        # For webcam frames, use optimized detection with reduced resolution
        is_webcam = "webcam" in file.filename.lower()
        if is_webcam:
            # Use optimized detection for speed
            detections = detector.detect_faces_optimized(image, conf_threshold=0.32, max_width=480)
        else:
            # Use standard detection for uploaded files
            detections = detector.detect_faces(image, conf_threshold=0.32)

        # Process image: Draw or Blur
        if blur:
            result_image = detector.blur_faces(image, detections)
        else:
            result_image = detector.draw_faces(image, detections, show_confidence=True)

        if result_image is None:
            return jsonify({"error": "Failed to process image"}), 500

        # Extract crops for gallery
        crops_base64 = []
        if len(detections) > 0:
            crops = detector.get_face_crops(image, detections)
            for crop in crops:
                _, buffer = cv2.imencode(".jpg", crop)
                crops_base64.append(base64.b64encode(buffer).decode())

        # Convert result to base64 for display
        _, buffer = cv2.imencode(".jpg", result_image)
        img_base64 = base64.b64encode(buffer).decode()

        # Prepare response
        response = {
            "success": True,
            "image": f"data:image/jpeg;base64,{img_base64}",
            "detections": {
                "count": len(detections),
                "faces": [
                    {
                        "id": i + 1,
                        "confidence": f"{det['confidence']:.2%}",
                        "width": det["w"],
                        "height": det["h"],
                        "position": f"({det['x1']}, {det['y1']})",
                    }
                    for i, det in enumerate(detections)
                ],
                "crops": crops_base64,
            },
        }

        return jsonify(response)

    except Exception:
        logging.exception("Error during image detection")
        return jsonify({"error": "Internal server error during image detection"}), 500


@app.route("/api/detect-video", methods=["POST"])
@limiter.limit("5 per hour")
def detect_video():
    """Detect faces in uploaded video"""
    try:
        if "file" not in request.files:
            return jsonify({"error": "No file provided"}), 400

        file = request.files["file"]
        if file.filename == "":
            return jsonify({"error": "No file selected"}), 400

        if not allowed_file(file.filename) or not is_video(file.filename):
            return jsonify({"error": "Only video files allowed"}), 400

        # Get model selection
        model = request.form.get("model", "yolov12m-face.pt")
        if model not in ALLOWED_MODELS:
            app.logger.info(f"Invalid model '{model}' requested. Fallback to default.")
            model = "yolov12m-face.pt"

        # Save uploaded file
        filename = secure_filename(file.filename)
        input_path = UPLOAD_FOLDER / f"input_{filename}"
        output_path = UPLOAD_FOLDER / f"output_{filename}"
        file.save(input_path)

        # Detect faces in video
        detect_from_video(
            video_path=str(input_path),
            model_path=str(MODELS_DIR / model),
            output_path=str(output_path),
            conf_threshold=0.32,
        )

        # Return file info
        response = {
            "success": True,
            "message": "Video processing complete",
            "output_file": output_path.name,
            "download_url": f"/api/download/{output_path.name}",
        }

        return jsonify(response)

    except Exception:
        # Log the full exception server-side without exposing details to the client
        app.logger.exception("Error while processing video detection request")
        return jsonify({"error": "Internal server error"}), 500


@app.route("/api/download/<filename>", methods=["GET"])
def download_file(filename):
    """Download processed file"""
    try:
        filepath = UPLOAD_FOLDER / secure_filename(filename)
        if not filepath.exists():
            return jsonify({"error": "File not found"}), 404

        return send_file(filepath, as_attachment=True)
    except Exception as e:
        # Log the full exception server-side without exposing details to the client
        app.logger.exception("Error while processing download request for %s", filename)
        return jsonify({"error": "Internal server error"}), 500


@app.route("/api/models", methods=["GET"])
def get_models():
    """Get ALL available models for dropdown selection"""
    models = {
        "nano": {
            "name": "yolov12n-face.pt",
            "label": "Nano (n) - Fastest",
            "description": "Real-time speed, best for CPU/Webcam",
            "size": "Smallest",
        },
        "small": {
            "name": "yolov12s-face.pt",
            "label": "Small (s) - Balanced",
            "description": "Good balance of speed and accuracy",
            "size": "Small",
        },
        "medium": {
            "name": "yolov12m-face.pt",
            "label": "Medium (m) - High Precision",
            "description": "High accuracy, requires decent GPU",
            "size": "Medium",
        },
        "large": {
            "name": "yolov12l-face.pt",
            "label": "Large (l) - Max Accuracy",
            "description": "Best detection quality, slowest speed",
            "size": "Large",
        },
    }

    available = {}
    for key, info in models.items():
        model_path = MODELS_DIR / info["name"]
        if model_path.exists():
            available[key] = info

    order = ["nano", "small", "medium", "large"]
    sorted_available = {k: available[k] for k in order if k in available}

    return jsonify(sorted_available)


@app.route("/api/feedback", methods=["POST"])
@limiter.limit("5 per minute")
def submit_feedback():
    try:
        data = request.json

        new_fb = Feedback(
            filename=data.get("filename"),
            model_name=data.get("model"),
            rating=data.get("rating"),
            comment=data.get("comment", ""),
        )
        db.session.add(new_fb)
        db.session.commit()
        return jsonify({"success": True, "message": "Rating saved!"})
    except Exception as e:
        app.logger.error(f"DB Error: {e}")
        return jsonify({"error": "Database error"}), 500


@app.route("/api/health", methods=["GET"])
def health_check():
    """Health check endpoint"""
    return jsonify({"status": "healthy", "service": "Face Detection API"})


@app.errorhandler(413)
def request_entity_too_large(error):
    """Handle file size exceeded"""
    return jsonify({"error": "File too large. Maximum 500MB allowed"}), 413


@app.errorhandler(500)
def internal_error(error):
    """Handle internal server error"""
    return jsonify({"error": "Internal server error"}), 500


@app.errorhandler(RateLimitExceeded)
def handle_rate_limit_error(e):
    """Handle rate limit exceeded errors"""
    app.logger.warning(f"Rate limit exceeded: {e.description}")
    return (
        jsonify(
            {
                "error": "Too many requests",
                "message": f"Too fast! Please wait a moment. ({e.description})",
            }
        ),
        429,
    )


if __name__ == "__main__":
    print("\n" + "=" * 70)
    print("🌐 Starting YOLOv12 Face Detection Web Server")
    print("=" * 70)
    print("\n📍 Server: http://localhost:7860")
    print("📁 Upload folder: ", UPLOAD_FOLDER)
    print("🔧 Models folder: ", MODELS_DIR)
    print("\n🎯 Available endpoints:")
    print("   GET  /                    - Web interface")
    print("   POST /api/detect-image    - Detect faces in image")
    print("   POST /api/detect-video    - Detect faces in video")
    print("   GET  /api/models          - Get available models")
    print("   GET  /api/health          - Health check")
    print("\n" + "=" * 70 + "\n")

    # Determine debug mode from environment (default: disabled)
    debug_mode = os.getenv("FLASK_ENV") == "development"

    # Run Flask app
    app.run(host="0.0.0.0", port=7860, debug=debug_mode, use_reloader=False)