let's deploy to hugging space

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: app1.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'description', 'intf', 'classify_image']
+
+# %% app1.ipynb 2
+from fastai.vision.all import *
+import gradio as gr
+import timm
+
+# %% app1.ipynb 3
+learn = load_learner('pets_classifier_model.pkl')
+
+# %% app1.ipynb 5
+categories = learn.dls.vocab
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories,map(float,probs)))
+
+# %% app1.ipynb 6
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['samoyed.jpg']
+description="A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Deep Learning app using HuggingFace Spaces and Gradio"
+
+# %% app1.ipynb 7
+intf = gr.Interface(fn=classify_image, 
+                    inputs=image, 
+                    outputs=label,
+                    title="Pets classifier",
+                    description=description,
+                    interpretation='default',
+                    examples=examples, enable_queue=True)
+intf.launch(inline=False)

pets_classifier_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7afbd6422233f7f01b0a5fcd3558c9e20179cf19d37d5fa4e02a76577f4c6fac
+size 114808625

requirements.txt

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+fastai
+gradio
+timm
+torch
+torchvision