this should work

app.py

@@ -1,24 +1,27 @@
-import gradio as gr
+# AUTOGENERATED! DO NOT EDIT! File to edit: . (unless otherwise specified).
+
+__all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
+
+# Cell
 from fastai.vision.all import *
-import skimage
+import gradio as gr
+import timm
+
+# Cell
+learn = load_learner('model_021724.pkl')
 
-learn = load_learner('export.pkl')
+# Cell
+categories = learn.dls.vocab
 
-labels = learn.dls.vocab
-def predict(img):
-    img = PILImage.create(img)
-    pred, pred_idx, probs = learn.predict(img)
-    return {labels[i]: folat(probs[i] for in in range(len(labels)))}
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
 
-title = 'Pet Breed Classifier'
-description = 'A pet breed classifier trained on the Oxford Pets dataset'
-article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
-examples = ['siamese.jpg']
-interpretation='default'
-enable_queue=True
+# Cell
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['basset.jpg']
 
-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, 
-             examples=examples, interpretation=interpretation, 
-             enable_queue=enable_queue).launch()
+# Cell
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch()