Upload app.py

app.py

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+# 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
+
+# Correctly format the path for Windows
+model_path = Path(r'efficientnet_b3_model.pkl')
+
+# Load the model
+learn = load_learner(model_path, cpu=True)
+
+# Define the prediction function
+def classify_image(image):
+    pred, idx, probs = learn.predict(image)
+    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.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="EfficientNet B3 Image Classifier",
+    description="Upload an image to classify using the trained EfficientNet B3 model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch(share=True)