Create app.py

app.py

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+import gradio as gr
+import cv2
+from PIL import Image
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+
+REPO_ID = "harpreetsahota/car-dd-segmentation-yolov11"
+FILENAME = "best.pt"
+
+# Télécharge best.pt depuis Hugging Face (cache automatique)
+weights_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+
+# Charge le modèle YOLO segmentation
+model = YOLO(weights_path)
+
+def predict(img: Image.Image, conf=0.25, imgsz=1280):
+    # Inference
+    results = model(img, conf=conf, imgsz=imgsz, verbose=False)
+
+    # Image annotée (masks + boxes + labels) -> numpy BGR
+    annotated_bgr = results[0].plot()
+
+    # BGR -> RGB -> PIL
+    annotated_rgb = cv2.cvtColor(annotated_bgr, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(annotated_rgb)
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil", label="Image voiture"),
+        gr.Slider(0.05, 0.9, value=0.25, step=0.05, label="Confidence (conf)"),
+        gr.Slider(640, 1536, value=1280, step=64, label="Taille image (imgsz)")
+    ],
+    outputs=gr.Image(type="pil", label="Résultat (segmentation YOLO)"),
+    title="Car Damage Segmentation (YOLO)",
+)
+
+if __name__ == "__main__":
+    demo.launch()