Upload 3 files

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import cv2  # ✅ fix: lowercase
+
+# Load the trained YOLO model
+model = YOLO('best.pt')
+
+# Define the prediction function
+def detect_objects(img):
+    # Run inference
+    results = model(img)
+    # Get the plotted image with bounding boxes and convert BGR to RGB
+    annotated_img = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
+    return Image.fromarray(annotated_img)
+
+# Create the Gradio interface
+app = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Car Defects Object Detection using YOLO",
+    description="Upload an image and the model will detect objects."
+)
+
+# Launch the app
+# Launch app
+if __name__ == "__main__":
+    app.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3cb4502b47062bb31e9add52b064a77cd6caa25ee1be41795fead5b4f136138e
+size 87659251

requirements.txt

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+ultralytics
+gradio
+numpy
+Pillow
+opencv-python