let's deploy it

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('model.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'><a 
+href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' 
+target='_blank'>Blog post</a></p>"
+examples = ['pet.jpg']
+interpretation='default'
+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,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:196cae8a37e714420d91ec79b7a1fd9ca211e9c510c303e6493b5502d0c4b54d
+size 46956779

requirements.txt

@@ -0,0 +1,2 @@
+fastai
+scikit-image