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app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('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))}
+
+title = "Pet Breed Classifier"
+description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><h6>Dont have any links to share yet</h6></p>"
+examples = ['siamese.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).launch(share=True,debug=True)

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a8ab5e3cb7761082b9a9411b8ca07cf5058ca4ba07d3f1a9d0dad911dad4b876
+size 103056172

requirements.txt

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+fastai==2.7.12
+torch==1.13.1
+torchvision==0.14.1
+gradio==3.38.0