# app.py
import gradio as gr
import cv2
from ultralytics import YOLO
import tempfile

# Charger le modèle YOLO (chemin relatif)
model = YOLO("./yolo12m.pt")

# Classes à détecter
target_classes = ["backpack", "suitcase", "handbag"]
class_name_to_id = {name: idx for idx, name in model.names.items()}
target_ids = [class_name_to_id[c] for c in target_classes]

def process_video(video_path):
    cap = cv2.VideoCapture(video_path)
    ret, frame = cap.read()
    if not ret:
        return "Erreur lecture vidéo"

    H, W = frame.shape[:2]
    line_y = int(H * 0.6)
    tolerance = 25

    counted_ids = set()
    class_counts = {c: 0 for c in target_classes}
    total_count = 0

    # Vidéo temporaire de sortie
    temp_out = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(temp_out.name, fourcc, 20, (W, H))

    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        results = model.track(frame, persist=True, classes=target_ids, verbose=False)

        if results[0].boxes.id is not None:
            ids = results[0].boxes.id.int().cpu().tolist()
            clss = results[0].boxes.cls.int().cpu().tolist()
            boxes = results[0].boxes.xyxy.cpu().tolist()

            for obj_id, cls, box in zip(ids, clss, boxes):
                x1, y1, x2, y2 = map(int, box)
                center_x = (x1 + x2) // 2
                center_y = (y1 + y2) // 2
                class_name = model.names[cls]

                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
                cv2.putText(frame, f"{class_name} ID:{obj_id}", (x1, y1 - 10),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

                if obj_id not in counted_ids and (line_y - tolerance < center_y < line_y + tolerance):
                    counted_ids.add(obj_id)
                    total_count += 1
                    class_counts[class_name] += 1

        # Affichage des résultats
        y_offset = 30
        for name in target_classes:
            cv2.putText(frame, f"{name}: {class_counts[name]}", (20, y_offset),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
            y_offset += 30
        cv2.putText(frame, f"Total bagages : {total_count}", (20, y_offset + 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
        cv2.line(frame, (0, line_y), (W, line_y), (0, 0, 255), 2)

        out.write(frame)

    cap.release()
    out.release()
    return temp_out.name

# Interface Gradio
demo = gr.Interface(
    fn=process_video,
    inputs=gr.Video(label="Importer une vidéo de bagages"),
    outputs=gr.Video(label="Vidéo annotée avec comptage"),
    title="🎒 Compteur intelligent de bagages",
    description="Détecte et compte les sacs, valises, et sacs à dos avec YOLO."
)

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