Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('export.pkl')
+
+label = learn.dls.vocab
+def predict(img):
+  img = PILImage.create(img)
+  pred,pred_idx,probs = learn.predicts(img)
+  return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+title = "Dog Breed Classifier"
+description = "A dog breed classifier trained on 10 most popular breed."
+examples = []
+interpretation='default'
+enable_queue=True
+
+gr.Interface(fn=predict, 
+             input=gr.inputs.Imsge(shape=(512, 512)),
+             output=gr.outputs.Label(num_top_classes=3),
+             title=title,
+             description=description,
+             examples=examples,
+             interpretation=interpretation,
+             enable_queue=enable_queue).launch()