# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.

# %% auto 0
__all__ = ['learn', 'categories', 'image', 'label',
           'examples', 'interface', 'classify_image']

# %% app.ipynb 2
from fastai.vision.all import *
import gradio as gr

# %% app.ipynb 3
learn = load_learner('model.pkl')

# %% app.ipynb 7
categories = ('ash', 'chestnut', 'ginkgo biloba', 'silver maple', 'willow oak')


def classify_image(img):
  pred, idx, probs = learn.predict(img)
  # Change each probability to a float, since Gradio doesn't support Tensors or NumPy
  return dict(zip(categories, map(float, probs)))


# %% app.ipynb 10
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['images/ash.jpg', 'images/chestnut.jpg', 'images/ginkgo_biloba.jpg',
            'images/silver_maple.jpg', 'images/willow_oak.jpg']

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