Update app.py

app.py

@@ -23,9 +23,7 @@ class_names = ["arrowheads", "axehammerstones", "scrapers"]
 
 IMG_SIZE = (224, 224)  # change this to your model's expected size
 
-def preprocess_input(x):
-    # Adjust according to your model preprocessing
-    return x / 255.0
+from tensorflow.keras.applications.efficientnet import preprocess_input
 
 def predict(img):
     """Predict function for Gradio"""
@@ -34,7 +32,7 @@ def predict(img):
     x = preprocess_input(x)
     x = np.expand_dims(x, 0)
     preds = model.predict(x)[0]
-    top = np.argmax(preds)
+    # top = np.argmax(preds)
     return {class_names[i]: float(preds[i]) for i in range(len(preds))}
 
 # Gradio interface