Update app.py

app.py

@@ -1,19 +1,33 @@
 import gradio as gr
 from fastai.vision.all import *
-import skimage
+import PIL.Image
 
+# Load the learner
 learn = load_learner('export.pkl')
 
+# Get the labels from the learner's vocabulary
 labels = learn.dls.vocab
+
+# Define the prediction function
 def predict(img):
     img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
+    pred, pred_idx, probs = learn.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
+# Define the title, description, and article for the interface
 title = "Cashew Classifier"
-description = "Cashewwwwwwwwwwwww."
-article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
-interpretation='default'
-enable_queue=True
+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>"
+
+# Define the interpretation mode (if applicable) and enable the queue
+interpretation = None  # Set to None if 'default' is not applicable or not desired
+enable_queue = True
 
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,interpretation=interpretation,enable_queue=enable_queue).launch()
+# Create and launch the Gradio interface
+gr.Interface(fn=predict,
+             inputs=gr.inputs.Image(shape=(512, 512)),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             article=article,
+             enable_queue=enable_queue).launch()