copy Kaggle notebook to app.py

app.py

@@ -1,7 +1,21 @@
 import gradio as gr
+from fastai.vision.all import *
 
-def greet(name):
-    return "Hello " + name + "!!"
+__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def is_cat(x): return x[0].isupper()
+
+learn = load_learner('model.pkl')
+
+categories = ('forest', 'ocean', 'mercedes', 'bmw')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+image = gr.Image(shape=(192, 192))
+label = gr.Label()
+examples = ['mercedes.jpg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

requirements.txt

@@ -0,0 +1,2 @@
+gradio
+fastai