Update app.py

app.py

@@ -1,29 +1,25 @@
 import gradio as gr
 from fastai.vision.all import *
-import PIL.Image
 
-# Load the learner
-learn = load_learner('export.pkl')
+# Load your trained model
+def load_model():
+    path = 'export.pkl'
+    learn = load_learner(path)
+    return learn
 
-# Get the labels from the learner's vocabulary
-labels = learn.dls.vocab
+learn = load_model()
 
-# Define the prediction function
-def predict(img):
-    img = PILImage.create(img)
+# Define prediction function
+def predict_image(img):
     pred, pred_idx, probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(learn.dls.vocab))}
 
-# Define the title, description, and article for the interface
-title = "Cashew Classifier"
-description = "Classify images of cashews."
-article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+# Create a Gradio interface
+interface = gr.Interface(fn=predict_image,
+                         inputs=gr.inputs.Image(type="pil"),
+                         outputs=gr.outputs.Label(num_top_classes=3),
+                         title="Image Classifier",
+                         description="Upload an image to classify.")
 
-# Create and launch the Gradio interface
-gr.Interface(fn=predict,
-             inputs=gr.Image(),
-             outputs=gr.Label(num_top_classes=3),
-             title=title,
-             description=description,
-             article=article,
-             enable_queue=True).launch()
+if __name__ == "__main__":
+    interface.launch()