prediction model

app.py

@@ -1,11 +1,22 @@
-import gradio as gr
+from fastai.vision.all import *
+
+def is_cat(x): return x[0].isupper()
+
+im = PILImage.create('dog.jpeg')
+im.thumbnail((192,192))
+im
 
 learn = load_learner('model.pkl')
+categories = ('Dog', 'Cat')
 
-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))}
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+import gradio as gr
 
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['dog.jpg', 'cat.jpg']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False, share=True)