Create app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+from fastai.vision.all import *
+
+
+learn = load_learner('model.pkl') 
+
+categories = ('apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio',
+       'beef_tartare', 'beet_salad', 'beignets', 'bibimbap',
+       'bread_pudding', 'breakfast_burrito', 'bruschetta', 'caesar_salad',
+       'cannoli', 'caprese_salad', 'carrot_cake', 'ceviche', 'cheesecake',
+       'cheese_plate', 'chicken_curry', 'chicken_quesadilla',
+       'chicken_wings', 'chocolate_cake', 'chocolate_mousse', 'churros',
+       'clam_chowder', 'club_sandwich', 'crab_cakes', 'creme_brulee',
+       'croque_madame', 'cup_cakes', 'deviled_eggs', 'donuts',
+       'dumplings', 'edamame', 'eggs_benedict', 'escargots', 'falafel',
+       'filet_mignon', 'fish_and_chips', 'foie_gras', 'french_fries',
+       'french_onion_soup', 'french_toast', 'fried_calamari',
+       'fried_rice', 'frozen_yogurt', 'garlic_bread', 'gnocchi',
+       'greek_salad', 'grilled_cheese_sandwich', 'grilled_salmon',
+       'guacamole', 'gyoza', 'hamburger', 'hot_and_sour_soup', 'hot_dog',
+       'huevos_rancheros', 'hummus', 'ice_cream', 'lasagna',
+       'lobster_bisque', 'lobster_roll_sandwich', 'macaroni_and_cheese',
+       'macarons', 'miso_soup', 'mussels', 'nachos', 'omelette',
+       'onion_rings', 'oysters', 'pad_thai', 'paella', 'pancakes',
+       'panna_cotta', 'peking_duck', 'pho', 'pizza', 'pork_chop',
+       'poutine', 'prime_rib', 'pulled_pork_sandwich', 'ramen', 'ravioli',
+       'red_velvet_cake', 'risotto', 'samosa', 'sashimi', 'scallops',
+       'seaweed_salad', 'shrimp_and_grits', 'spaghetti_bolognese',
+       'spaghetti_carbonara', 'spring_rolls', 'steak',
+       'strawberry_shortcake', 'sushi', 'tacos', 'takoyaki', 'tiramisu',
+       'tuna_tartare', 'waffles')
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+
+image = gr.inputs.Image(shape = (192,192))
+label = gr.outputs.Label()
+
+examples = ['tiramisu.jpeg', 'pizza.jpeg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)