Upload app.py

app.py

@@ -4,20 +4,31 @@
 Automatically generated by Colaboratory.
 
 Original file is located at
-    https://colab.research.google.com/drive/1tB5UaeH3SiFcvNJelcs0apxr4mFEHmtA
+    https://colab.research.google.com/drive/1sm_pZkdxzPTsmU4kQ9Sdiq5VlFEqRWDQ
 """
 
+__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
 
-
+# Cell
 from fastai.vision.all import *
 import gradio as gr
 
+def is_cat(x): return x[0].isupper()
+
+# Cell
 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))}
+# Cell
+categories = ('Dog', 'Cat')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+# Cell
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
 
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)