Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+
+# Load YOLO model
+model = YOLO("best.pt")  # keep best.pt in the same folder
+
+def predict(image):
+    # Run YOLO inference
+    results = model.predict(image, save=False)
+    
+    # Plot results (draw bounding boxes)
+    result_image = Image.fromarray(results[0].plot()[:, :, ::-1])  # BGR → RGB
+    
+    # Collect labels and confidence scores
+    labels = []
+    for box in results[0].boxes:
+        cls = results[0].names[int(box.cls)]
+        conf = float(box.conf)
+        labels.append(f"{cls}: {conf:.2f}")
+    
+    return result_image, "\n".join(labels) if labels else "No objects detected"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), gr.Textbox(label="Detections")],
+    title="YOLOv8 Object Detection",
+    description="Upload an image to detect objects with bounding boxes and confidence scores."
+)
+
+if __name__ == "__main__":
+    demo.launch()