forgot the app

app.py

@@ -4,29 +4,36 @@ from fastai.vision.widgets import *
 from IPython.display import display
 
 
-learn_inf = load_learner('export.pkl')
-lbl_pred = widgets.Label()
-# lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
-out_pl = widgets.Output()
-btn_upload = widgets.FileUpload()
+# learn_inf = load_learner('export.pkl')
+# lbl_pred = widgets.Label()
+# # lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
+# out_pl = widgets.Output()
+# btn_upload = widgets.FileUpload()
 
-# VBox([widgets.Label('Select your bear!'),
-#       btn_upload, btn_run, out_pl, lbl_pred])
-# def greet(name):
-#     return "Hello " + name + "!!"
+# # VBox([widgets.Label('Select your bear!'),
+# #       btn_upload, btn_run, out_pl, lbl_pred])
+# # def greet(name):
+# #     return "Hello " + name + "!!"
 
-def on_click_classify(picture):
-    # btn_upload = widgets.FileUpload()
-    # lbl_pred = widgets.Label()
-    # lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
-    out_pl = widgets.Output()
-    img = PILImage.create(picture)
-    out_pl.clear_output()
-    with out_pl: display(img.to_thumb(128,128))
-    pred,pred_idx,probs = learn_inf.predict(img)
-    print('pred,pred_idx,probs', pred,pred_idx,probs)
-    lbl_pred.value = f'Prediction: {pred};\n Probability: {probs[pred_idx]:.04f}'
-    return lbl_pred.value
+# def on_click_classify(picture):
+#     out_pl = widgets.Output()
+#     img = PILImage.create(picture)
+#     out_pl.clear_output()
+#     with out_pl: display(img.to_thumb(128,128))
+#     pred,pred_idx,probs = learn_inf.predict(img)
+#     print('pred,pred_idx,probs', pred,pred_idx,probs)
+#     lbl_pred.value = f'Prediction: {pred};\n Probability: {probs[pred_idx]:.04f}'
+#     return lbl_pred.value
+learn = load_learner('export.pkl')
+categories = ('Hot Dog','No Hot Dog')
 
-iface = gr.Interface(fn=on_click_classify, inputs="image", outputs='text')
+def on_click_classify(img):
+    pred,pred_idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+image = gr.Image()
+label = gr.Label()
+examples = ['hot_dog.jpg', 'no_hot_dog.jpg', 'jian_yang.jpg']
+
+iface = gr.Interface(fn=on_click_classify, inputs=image, outputs=label, examples=examples)
 iface.launch()