app.py

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

# %% auto 0
__all__ = ['path', 'dls', 'im', 'learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']

# %% ../app.ipynb 2
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from fastai.vision.all import *

# %% ../app.ipynb 3
path = untar_data(URLs.PETS)/'images'

# %% ../app.ipynb 4
def is_cat(x): return x[0].isupper() 

# %% ../app.ipynb 5
dls = ImageDataLoaders.from_name_func('.',
    get_image_files(path), valid_pct=0.2, seed=42,
    label_func=is_cat,
    item_tfms=Resize(192))

# %% ../app.ipynb 6
#learn = vision_learner(dls, resnet18, metrics=error_rate)
#learn.fine_tune(1)

# %% ../app.ipynb 7
#learn.export('model.pkl')

# %% ../app.ipynb 8
im = PILImage.create('dog.jpg')
im.thumbnail((192,192))
im

# %% ../app.ipynb 9
learn = load_learner('model.pkl')

# %% ../app.ipynb 10
learn.predict(im)

# %% ../app.ipynb 11
categories = ('Dog', 'Cat')

# %% ../app.ipynb 12
def classify_image(img):
    pred, idx, probs=learn.predict(img)
    return dict(zip(categories, map(float, probs)))

# %% ../app.ipynb 13
classify_image(im)

# %% ../app.ipynb 15
import gradio as gr

# %% ../app.ipynb 16
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = ['dog.jpg', 'cat.jpeg', 'raccoon.jpg']

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