Update app.py

app.py

@@ -36,7 +36,7 @@ label_to_class_text = {0: 'range',
 def predict_image(image):
     # Convert PIL Image to numpy array (OpenCV uses numpy arrays)
     image = np.array(image)
-
+    temp_image =image
     # Resize, preprocess, and reshape the input image
     img_size = 960
     resized_image = cv2.resize(image, (img_size, img_size)) / 255.0
@@ -64,9 +64,9 @@ def predict_image(image):
         ])
 
         if prob > 0.2:
-            cv2.rectangle(image, (x1, y1), (x2, y2), (255, 255, 0), 2)
+            cv2.rectangle(temp_image, (x1, y1), (x2, y2), (255, 255, 0), 2)
             label_text = f"{classes} {round(prob, 2)}"
-            cv2.putText(image, label_text, (x1, y1), 0, 0.5, (0, 255, 0), 2)
+            cv2.putText(temp_image, label_text, (x1, y1), 0, 0.5, (0, 255, 0), 2)
 
             # Store prediction info in a JSON-compatible format
             predictions.append({
@@ -81,7 +81,7 @@ def predict_image(image):
             })
 
     # Convert the processed image back to PIL Image for Gradio
-    pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    pil_image = Image.fromarray(cv2.cvtColor(temp_image, cv2.COLOR_BGR2RGB))
 
     return pil_image, json.dumps(predictions, indent=4)