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# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
# %% auto 0
__all__ = ['EXPORT_PATH', 'categories', 'inp_img', 'labels', 'example_img', 'intf', 'set_posix_windows', 'classify_images']
# %% ../app.ipynb 3
import fastai
from fastai.vision.all import *
import gradio as gr
# %% ../app.ipynb 5
from contextlib import contextmanager
import pathlib
'''@contextmanager
def set_posix_windows():
posix_backup = pathlib.PosixPath
try:
pathlib.PosixPath = pathlib.WindowsPath
yield
finally:
pathlib.PosixPath = posix_backup
# %% ../app.ipynb 6
#Exporting our model file:
EXPORT_PATH = pathlib.Path('model.pkl')
with set_posix_windows():
learn = load_learner(EXPORT_PATH)'''
learn = load_learner('model.pkl')
# %% ../app.ipynb 9
'''categories = ('EASTERN TOWEE',
'RING-BILLED GULL',
'LILAC ROLLER',
'CACTUS WREN',
'MALACHITE KINGFISHER',
'EURASIAN MAGPIE',
'TRUMPTER SWAN',
'HOODED MERGANSER',
'RAZORBILL',
'TREE SWALLOW',
'MOURNING DOVE',
'TURKEY VULTURE',
'PEREGRINE FALCON',
'BAR-TAILED GODWIT',
'BLACK SWAN',
'BALTIMORE ORIOLE',
'BLUE HERON',
'MIKADO PHEASANT',
'WHITE CHEEKED TURACO',
'GOLDEN CHLOROPHONIA')'''
categories = learn.dls.vocab
print(categories)
def classify_images(image):
pred, idx, probs = learn.predict(image)
return dict(zip(categories, map(float, probs)))
# %% ../app.ipynb 11
# Building the gradio application interface:
inp_img = gr.inputs.Image(shape=(192, 192))
labels = gr.outputs.Label()
example_img = [ 'baltimore_oriole.jpg', 'bar_tailed_godwit.jpg',
'black_swan.jpg', 'blue_heron.jpg', 'cactus_wren.jpg', 'eastern_towee.jpg', 'golden_chlorophonia.jpg',
'lilac_roller.jpg', 'malachite_kingfisher.jpg', 'mikado_pheasant.jpg', 'mourning_dove.jpg',
'peregine_falcon.jpg', 'razorbill.jpg', 'ring_billed_gull.jpg',
'tree_swallow.jpg', 'trumpter_swan.jpg', 'white_cheeked_turaco.jpg']
intf = gr.Interface(fn=classify_images, inputs=inp_img, outputs=labels, examples=example_img)
intf.launch(inline=False)
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