Create app.py

app.py

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+from fastai.vision.all import *
+import gradio as gr
+# any external function used for labeling needs to be included in here
+def is_cat(x): return x[0].isupper()
+# this learner 'pkl' file is exactly the same as what you get when you trained it
+# example : learn = vision_learner(dls,resnet18,metrics=error_rate)
+# example : learn.fine_tune(3)
+learn=load_learner('catsdogsmodel.pkl')
+# Preping data for gradio, We are creating a dictionary for gradio. 
+# One of the annoying things about 'gradio' is that it does not recognize tensor number and probabilities. 
+# In fact, numpy either
+categories = ('Dog', 'Cat')
+
+# prediction, index & probabilities, gradio expects a dictinoary
+def classify_image(img):
+  pred,idx,probs = learn.predict(img)
+  return dict(zip(categories,map(float, probs)))
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['dog.jpg','dog2.jpg','cat.jpg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+# to create a public link, set 'share=True' in 'launch()'
+intf.launch(inline=False)