Dog vs Cat classifier

app2.py

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+# This script is used to create a Gradio interface in which we have a 
+# dog vs cat classifier using the fastai library. For more explanation,
+# visit the Google Colab notebook associated. 
+
+from fastai.vision.all import *
+import gradio as gr
+
+# Define label function
+def is_cat(x): return x[0].isupper()
+
+# Load our model
+learner = load_learner('model.pkl')
+
+
+# Transform our model to obtain results that Gradio can handle with
+categories = ('Dog', 'Cat')
+
+def classify_image(img):
+  # We are saying that this predictions returns: the prediction, its index and the prediction probability
+  pred,idx,probs = learn.predict(img)
+
+  # Here we return a dictionary with categories as keys and its probabilities as values
+  return dict(zip(categories, map(float, probs)))
+
+
+# Create the Gradio interface
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples =  ['dogg.jpg', 'cat.jpg', 'dunno.jpg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False, share=True)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9e615cfcff573a78d8b2bf885d6965ae0c268e7945dfa72f2e010c5449dfc88
+size 47062993