Update app.py

app.py

@@ -1,52 +1,26 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['btn_upload', 'out_pl', 'lbl_pred', 'learn', 'categories', 'image', 'label', 'examples', 'intf', 'DataLoaders',
-           'is_cat', 'classify_img']
+__all__ = ['learn', 'image', 'label', 'examples', 'intf', 'classify_image']
 
 # %% app.ipynb 2
 from fastai.vision.all import *
 import gradio as gr
-from fastbook import *
-from fastai.vision.widgets import *
-import gradio as gr
-btn_upload = widgets.FileUpload()
-out_pl = widgets.Output()
-lbl_pred = widgets.Label()
-
-# %% app.ipynb 3
-# def on_data_change(change):
-#     lbl_pred.value = ''
-#     img = PILImage.create(btn_upload.data[-1])
-#     out_pl.clear_output()
-#     with out_pl: display(img.to_thumb(128,128))
-#     pred,pred_idx,probs = learn_inf.predict(img)
-#     lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
-
-class DataLoaders(GetAttr):
-    def __init__(self, *loaders): self.loaders = loaders
-    def __getitem__(self, i): return self.loaders[i]
-    train,valid = add_props(lambda i,self: self[i])
-    
-def is_cat(x): return x[0].isupper()
-
-
 
 # %% app.ipynb 4
 learn = load_learner('export.pkl')
-print(type(learn))
+# print(type(learn))
 
-# %% app.ipynb 7
-categories = ('black', 'grizzly', 'teddy')
 
 # %% app.ipynb 8
-def classify_img(img):
-    cat,idx, prob = learn.predict(img)
-    return dict(zip(categories, map(float,prob)))
+def classify_image(img):
+    pred,pred_idx,probs = learn.predict(img)
+    return dict(zip(learn.dls.vocab, map(float, probs)))
 
 # %% app.ipynb 10
-image = gr.inputs.Image(shape = (192,192))
-label = gr.outputs.Label()
-examples = ['teddy.png', 'grizzly.jpg','black.jpeg']
-intf = gr.Interface(fn = classify_img, inputs = image, outputs = label, examples = examples)
-intf.launch(inline = False)
+image = gr.components.Image(shape=(192,192))
+label = gr.components.Label()
+examples = ['teddy.png', 'grizzly.jpg', 'black.jpeg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)