app.py

import gradio as gr
from fastai.vision.all import *

def get_images_to_train_on():
    return ['']

def get_label_by_filename(filename):
    return 'Other'


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(num_top_classes=3)

examples = ['tiramisu.jpeg', 'pizza.jpeg']

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)