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app.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'iface', 'classify_image']
+__all__ = ['learn', 'categories', 'train_csv', 'n_inp', 'image', 'label', 'examples', 'iface', 'label_func', 'classify_image']
 
 # %% app.ipynb 1
 import gradio as gr
@@ -15,15 +15,24 @@ learn = load_learner('model.pkl')
 categories = ('Badminton', 'Cricket', 'Karate', 'Soccer', 'Swimming', 'Tennis', 'Wrestling')
 
 # %% app.ipynb 4
+import pandas as pd
+train_csv = pd.read_csv('dataset/train.csv')
+n_inp = len(set(train_csv['label']))
+train_csv.head()
+def label_func(item):
+    rel_path = str(item.relative_to('dataset/train'))
+    return train_csv[train_csv['image_ID']==rel_path]["label"].values[0]
+
+# %% app.ipynb 5
 def classify_image(img):
     pred, idx, probs = learn.predict(img)
     return dict(zip(categories, map(float, probs)))
 
-# %% app.ipynb 5
+# %% app.ipynb 6
 image = gr.inputs.Image(shape(224, 224))
 label = gr.outputs.Label()
 examples = ['Badminton.jpg', 'Cricket.jpg', 'Karate.jpg', 'Soccer.jpg', 'Swimming.jpg', 'Tennis.jpg', 'Wrestling.jpg']
 
-# %% app.ipynb 6
+# %% app.ipynb 7
 iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
 iface.launch(inline=False)