Update app.py

app.py

@@ -54,6 +54,18 @@ def normalized(a, axis=-1, order=2):
     l2[l2 == 0] = 1
     return a / np.expand_dims(l2, axis)
 
+def convert_numpy_types(data):
+    if isinstance(data, dict):
+        return {key: convert_numpy_types(value) for key, value in data.items()}
+    elif isinstance(data, list):
+        return [convert_numpy_types(item) for item in data]
+    elif isinstance(data, np.float64):
+        return float(data)
+    elif isinstance(data, np.int64):
+        return int(data)
+    else:
+        return data
+
 def predict(image):
     model = MLP(768)  # CLIP embedding dim is 768 for CLIP ViT L 14
     pthpath = "https://huggingface.co/haor/aesthetics/resolve/main/sac%2Blogos%2Bava1-l14-linearMSE.pth"
@@ -84,7 +96,7 @@ def predict(image):
         "sha1": sha1,
         "laplacian_variance": laplacian_variance
     }
-    return result
+    return convert_numpy_types(result)
 
 title = "CLIP Aesthetic Score"
 description = "Upload an image to predict its aesthetic score using the CLIP model and calculate other image metrics."