"""
Native Desktop GUI for Supermarket Shelf Product Recognition
Opens a desktop OpenCV window displaying image.jpg with bounding boxes only.
Users can click on any product directly on screen to inspect price, stock,
and feature matching inliers in real-time.
"""

import os
import sys
import cv2
import numpy as np

current_dir = os.path.dirname(os.path.abspath(__file__))
if current_dir not in sys.path:
    sys.path.insert(0, current_dir)

from pipeline import ShelfRecognitionPipeline


def main():
    print("=" * 60)
    print("STARTING NATIVE DESKTOP INTERFACE")
    print("Image: image.jpg | Mode: Hybrid (Paper Method)")
    print("Instructions: Click on ANY product to identify & view info.")
    print("Press 'q' or 'ESC' on the window to exit.")
    print("=" * 60)

    pipeline = ShelfRecognitionPipeline()
    init_res = pipeline.set_active_shelf_image("image.jpg")

    if init_res.get("status") != "success":
        print("[Error] Failed to load image.jpg")
        return

    boxes = init_res["boxes"]
    base_image = pipeline.current_image.copy()

    # Draw ONLY bounding boxes (no class names, no confidence labels)
    display_image = pipeline.detector.draw_detections(base_image, boxes, draw_labels=False)

    window_name = "Smart Shelf - Click Any Product (Press Q to quit)"
    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
    cv2.resizeWindow(window_name, 1280, 850)

    def on_mouse(event, x, y, flags, param):
        if event == cv2.EVENT_LBUTTONDOWN:
            print(f"\n[Click detected at coordinates: ({x}, {y})]")
            res = pipeline.process_click(x, y, method="hybrid")

            if res.get("status") == "success":
                prod = res["inventory"]
                cls_info = res["classification"]
                box_info = res["target_box"]

                print("-" * 55)
                print(f"📦 Product Identified : {prod['product_name']}")
                print(f"🏷️  SKU ID             : {prod['sku_id']}")
                print(f"💰 Price              : ¥{prod['price']}")
                print(f"📊 In Stock (On-Shelf): {prod['number_on']} units")
                print(f"🚚 Received / Sold    : {prod['number_in']} in / {prod['number_out']} out")
                print(f"📅 Expiration Date    : {prod['expiration_date']}")
                print(f"🎯 Match Confidence   : {cls_info['confidence'] * 100:.1f}% (Score: {cls_info['score']})")
                print("-" * 55)

                # Update main window with selected box highlight (in red)
                selected_display = pipeline.detector.draw_detections(
                    base_image, boxes, selected_box_id=box_info["id"], draw_labels=False
                )
                cv2.imshow(window_name, selected_display)

                # Show visual match comparison window if available
                if res.get("match_visualization") is not None:
                    match_win = "Feature Matcher (Shelf Crop vs Stock Template)"
                    cv2.namedWindow(match_win, cv2.WINDOW_NORMAL)
                    cv2.resizeWindow(match_win, 800, 400)
                    cv2.imshow(match_win, res["match_visualization"])
            else:
                print(f"[Info] {res.get('message', 'No product found at coordinates.')}")

    cv2.setMouseCallback(window_name, on_mouse)

    while True:
        cv2.imshow(window_name, display_image)
        key = cv2.waitKey(20) & 0xFF
        if key in [ord('q'), ord('Q'), 27]:
            break

    cv2.destroyAllWindows()


if __name__ == "__main__":
    main()