Update app.py

app.py

@@ -72,7 +72,6 @@ def alpr_color_inference(image):
     if not vehicle_boxes:
         color_text = "No vehicle detected"
         cropped_img = img
-        vehicle_img = img
     else:
         largest_vehicle = max(vehicle_boxes, key=lambda box: (box.xyxy[0][2] - box.xyxy[0][0]) * (box.xyxy[0][3] - box.xyxy[0][1]))
         x1, y1, x2, y2 = map(int, largest_vehicle.xyxy[0].tolist())
@@ -84,25 +83,26 @@ def alpr_color_inference(image):
             pred_idx = torch.argmax(probs).item()
             pred_class = class_names[pred_idx]
             confidence = probs[pred_idx].item()
-        vehicle_img = Image.fromarray(cv2.rectangle(np.array(img), (x1, y1), (x2, y2), (255, 0, 0), 3))
+        draw.rectangle((x1, y1, x2, y2), outline="blue", width=3)
+        draw.text((x1, max(y1 - 10, 0)), f"{pred_class} ({confidence*100:.1f}%)", fill="blue")
         color_text = f"{pred_class} ({confidence*100:.1f}%)"
 
     detection_results = (f"Detected {len(results)} license plate(s): {', '.join(plate_texts)}"
                          if results else "No license plate detected 😔.")
 
-    return annotated_img, vehicle_img, cropped_img, f"{detection_results}\nVehicle Color: {color_text}"
+    return annotated_img, cropped_img, cropped_img, f"{detection_results}\nVehicle Color: {color_text}"
 
 # ------------------ Gradio UI ------------------
 with gr.Blocks() as demo:
     gr.Markdown("# License Plate + Vehicle Color Detection")
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(type="pil", label="Upload an image")
+            image_input = gr.Image(type="pil", label="Upload an image", show_clear_button=True)
             submit_btn = gr.Button("Run Detection")
         with gr.Column():
-            plate_output = gr.Image(label="License Plate Detection")
-            vehicle_output = gr.Image(label="Detected Vehicle in Original")
-            cropped_output = gr.Image(label="Cropped Vehicle Region")
+            plate_output = gr.Image(label="Combined Detection Output")
+            vehicle_output = gr.Image(label="(Optional) Cropped Vehicle")
+            cropped_output = gr.Image(label="(Optional) Cropped Vehicle Region")
             result_text = gr.Markdown(label="Results")
 
     submit_btn.click(