Update app.py

app.py

@@ -110,11 +110,15 @@ def predict(image_path, model, device, margin=0.4, input_size=224):
             input_blob = torch.from_numpy(np.transpose(face.astype(np.float32), (2, 0, 1))).unsqueeze(0).to(device)
             output = F.softmax(model(input_blob), dim=-1).cpu().numpy()
             ages = np.arange(0, 101)
-            predicted_age = (output * ages).sum(axis=-1)
+            predicted_age = (output * ages).sum(axis=-1).item()  # Convert to native Python datatype
 
             # draw results
             age_text = f'{int(predicted_age)}'
-            age_data.append({'age': int(predicted_age), 'text': age_text, 'face_coordinates': (startX, startY)})
+            age_data.append({
+                'age': int(predicted_age),  # Ensure this is a native Python int, not numpy int64
+                'text': age_text,
+                'face_coordinates': (int(startX), int(startY))  # Convert to native Python datatype
+            })
             
             # Optionally, draw bounding boxes and age labels on the image
             # cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)