Update app.py

app.py

@@ -18,20 +18,15 @@ import tensorflow as tf
 model = tf.keras.models.load_model("stone_artifact_classifier.h5", compile=False, custom_objects={"RandomHeight": tf.keras.layers.RandomHeight, "RandomWidth": tf.keras.layers.RandomWidth})
 #model = tf.keras.models.load_model("stone_artifact_classifier.h5", compile=False)
 
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 # Replace with your classes
 class_names = ["arrowheads", "axehammerstones", "scrapers"]
 
-from tensorflow.keras.applications.efficientnet import preprocess_input
+IMG_SIZE = (224, 224)  # change this to your model's expected size
 
+from tensorflow.keras.applications.efficientnet import preprocess_input
 
 def predict(img):
     """Predict function for Gradio"""
-
     img = img.convert("RGB").resize(IMG_SIZE)
     x = np.array(img)
     x = preprocess_input(x)
@@ -40,25 +35,14 @@ def predict(img):
     # top = np.argmax(preds)
     return {class_names[i]: float(preds[i]) for i in range(len(preds))}
 
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 # Gradio interface
 iface = gr.Interface(
+    fn=predict,
     inputs=gr.Image(type="pil"),
     outputs=gr.Label(num_top_classes=len(class_names)),
     title="Stone Artifact Classifier",
     description="Upload an image of an artifact and the model will classify it."
 )
 
-iface.launch()
+iface.launch()
+