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app.py

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+from fastai.vision.all import *
+import gradio as gr
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('fast-ai/export.pkl')
+
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# Create and launch the Gradio interface
+title = "Pet Breed Classifier"
+examples = ['fast-ai/OxfordPetAI/beagle.jpeg']
+description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+gr.Interface(fn=predict, inputs="image", outputs="label", title=title, description=description, examples=examples).launch(share=True)

requirements.txt

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+fastai
+scikit-image