app.py

__all__ = ['animal', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']

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

# cell
learn = load_learner('model.pkl')

# cell
categories = ('antelope', 'badger', 'bat', 'bear', 'bee', 'beetle', 'bison', 'boar',
              'butterfly', 'cat', 'caterpillar', 'chimpanzee', 'cockroach', 'cow',
              'coyote', 'crab', 'crow', 'deer', 'dog', 'dolphin', 'donkey', 'dragonfly',
              'duck', 'eagle', 'elephant', 'flamingo', 'fly', 'fox', 'goat', 'goldfish',
              'goose', 'gorilla', 'grasshopper', 'hamster', 'hare', 'hedgehog',
              'hippopotamus', 'hornbill', 'horse', 'hummingbird', 'hyena', 'jellyfish',
              'kangaroo', 'koala', 'ladybugs', 'leopard', 'lion', 'lizard', 'lobster',
              'mosquito', 'moth', 'mouse', 'octopus', 'okapi', 'orangutan', 'otter',
              'owl', 'ox', 'oyster', 'panda', 'parrot', 'pelecaniformes', 'penguin',
              'pig', 'pigeon', 'porcupine', 'possum', 'raccoon', 'rat', 'reindeer',
              'rhinoceros', 'sandpiper', 'seahorse', 'seal', 'shark', 'sheep', 'snake',
              'sparrow', 'squid', 'squirrel', 'starfish', 'swan', 'tiger', 'turkey',
              'turtle', 'whale', 'wolf', 'wombat', 'woodpecker', 'zebra')

def classify_image(img):
  preds, idx, probs = learn.predict(img)
  return dict(zip(categories, map(float, probs)))

# cell
image = gr.components.Image()
label = gr.components.Label(num_top_classes=3)
examples = ['5a094c5a36.jpg', '6c28066ea3.jpg', '6dd3ba7825.jpg', '6f634d9a5f.jpg',
            '7b04dad848.jpg', '7b9f3d9464.jpg', '7c43d5ca9e.jpg', '7cc3758d67.jpg', 
            '7f995e322c.jpg', '8aefee4c2c.jpg', '0041c9ff2c.jpg']

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