gradio app

app.py

@@ -1,7 +1,26 @@
 import gradio as gr
+from fastai.vision.all import *
+"""
+Hello world gradio
 
 def greet(name):
     return "Hello " + name + "!!"
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+iface.launch()
+"""
+
+learn = load_learner('pet_model.pkl')
+
+categories = ('Abyssinian', 'Bengal', 'Birman', 'Bombay', 'British_Shorthair', 'Egyptian_Mau', 'Maine_Coon', 'Persian', 'Ragdoll', 'Russian_Blue', 'Siamese', 'Sphynx', 'american_bulldog', 'american_pit_bull_terrier', 'basset_hound', 'beagle', 'boxer', 'chihuahua', 'english_cocker_spaniel', 'english_setter', 'german_shorthaired', 'great_pyrenees', 'havanese', 'japanese_chin', 'keeshond', 'leonberger', 'miniature_pinscher', 'newfoundland', 'pomeranian', 'pug', 'saint_bernard', 'samoyed', 'scottish_terrier', 'shiba_inu', 'staffordshire_bull_terrier', 'wheaten_terrier', 'yorkshire_terrier')
+
+def classify_image(img):
+    _ ,_ ,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['bengal.jpg', 'pug.jpg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)