updated app.py

app.py

@@ -1,34 +1,34 @@
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
-
-#import timm
-# %% app.ipynb 2
-from fastai.vision.all import *
-import gradio as gr
-#import timm
-
-def is_cat(x): return x[0].isupper()
-
-# %% app.ipynb 4
-learn = load_learner('f1_car.pkl')
-
-# %% app.ipynb 6
-categoris = ('McLaren F1 cars', 'Ferrari F1 racing cars', 'Redbull F1 racing cars', 'Mercedes AMG F1 racing cars', 'Aston Martin F1 racing cars', 'Alpine F1 racing cars', 'Haas F1 racing cars' , 'RB F1 racing cars', 'Williams F1 racing cars', 'Kick Sauber F1 racing cars')
-#categories = ( 'Mercedes cars', 'Ferrari cars', 'BMW cars', 'Bentley cars', 'Porsche cars', 'Aston Martin cars', 'Audi cars' , 'Maserati cars', 'McLaren cars', 'Lamborghini cars', 'Bugatti cars', 'Koenigsegg cars', 'Pagani cars', 'Tesla cars')
-#categories = ('Dog', 'Cat')
-
-def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-# %% app.ipynb 8
-from gradio.components import Image, Label
-
-# %% app.ipynb 9
-image =  Image(width=300, height=240)
-label = Label()
-examples = ['mclaren.jpg', 'ferrari_f1.jpg', 'redbull_f1.jpg',
-            'merc.jpg', 'aston_martin.jpg', 'alpine.jpg', 'haas.jpg',
-            'sauber.jpg', 'rb.jpg', 'williams.jpg'
-            ]
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
+
+#import timm
+# %% app.ipynb 2
+from fastai.vision.all import *
+import gradio as gr
+#import timm
+
+def is_cat(x): return x[0].isupper()
+
+# %% app.ipynb 4
+learn = load_learner('app.pkl')
+
+# %% app.ipynb 6
+categoris = ('Alpine', 'AstonMartin', 'Ferrari', 'Haas', 'McLaren', 'Mercedes', 'RB' , 'Redbull', 'Sauber', 'Williams')
+#categories = ( 'Mercedes cars', 'Ferrari cars', 'BMW cars', 'Bentley cars', 'Porsche cars', 'Aston Martin cars', 'Audi cars' , 'Maserati cars', 'McLaren cars', 'Lamborghini cars', 'Bugatti cars', 'Koenigsegg cars', 'Pagani cars', 'Tesla cars')
+#categories = ('Dog', 'Cat')
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% app.ipynb 8
+from gradio.components import Image, Label
+
+# %% app.ipynb 9
+image =  Image(width=300, height=240)
+label = Label()
+examples = ['mclaren.jpg', 'ferrari_f1.jpg', 'redbull_f1.jpg',
+            'merc.jpg', 'aston_martin.jpg', 'alpine.jpg', 'haas.jpg',
+            'sauber.jpg', 'rb.jpg', 'williams.jpg'
+            ]
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
 intf.launch(inline=False)