Update app.py

app.py

@@ -16,21 +16,21 @@ model_path = Path(r'efficientnet_b3_model.pkl')
 learn = load_learner(model_path, cpu=True)
 
 # Define the prediction function
-def classify_image(image,filename):
+def classify_image(image):
     pred, idx, probs = learn.predict(image)
-    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))},filename
+    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
 
 # Set up the Gradio interface
 interface = gr.Interface(
     fn=classify_image,
-    inputs=gr.File(label="Upload Image", type="image"),
-    outputs=[gr.Label(num_top_classes=3),gr.Textbox(label=filename)],    
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
     title="EfficientNet B3 Image Classifier",
     examples= examples,
     description="Upload an image to classify using the trained EfficientNet B3 model.",
-    
 )
 
+
 # Launch the app
 if __name__ == "__main__":
     interface.launch(share=True)