Update app.py

app.py

@@ -1,32 +1,3 @@
-# import gradio as gr
-# from fastai.vision.all import *
-# import timm
-
-# # Load the exported model
-# learn = load_learner('./efficientnet_b3_model.pkl', cpu=True)  # Using cpu=True for compatibility
-
-# learn.export('./efficientnet_b3_model.pkl')  # export_model(learn, 'efficientnet_b3_model.pkl')
-
-
-# # Define the prediction function
-# def classify_image(image):
-#     pred, idx, probs = learn.predict(image)
-#     # Return the top 3 predictions with their probabilities
-#     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,  # Function to make predictions
-#     inputs=gr.Image(type="pil"),  # Input as an image in PIL format
-#     outputs=gr.Label(num_top_classes=3),  # Output shows top 3 predicted classes
-#     title="EfficientNet B3 Image Classifier",
-#     description="Upload an image to classify using the trained EfficientNet B3 model."
-# )
-
-# # Launch the Gradio app
-# if __name__ == "__main__":
-#     interface.launch(share=True)  # `share=True` makes the app publicly accessible
-
 from pathlib import Path
 from fastai.vision.all import *
 import gradio as gr
@@ -46,10 +17,7 @@ def classify_image(image):
 interface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Label(num_top_classes=3),
-        gr.Textbox(value="The prediction results above represent the top 3 cancer stages."),
-    ],
+    outputs=gr.Label(num_top_classes=3),
     title="EfficientNet B3 Image Classifier",
     description="Upload an image to classify using the trained EfficientNet B3 model."
 )