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# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb. | |
# %% auto 0 | |
__all__ = ['plt', 'learn', 'interface_options', 'demo', 'label_func', 'predict'] | |
# %% ../app.ipynb 2 | |
from fastai.vision.all import * | |
import gradio | |
#import pathlib | |
#temp = pathlib.PosixPath | |
#pathlib.PosixPath = pathlib.WindowsPath | |
import pathlib | |
plt = platform.system() | |
if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath | |
def label_func(filepath): | |
return filepath.parent.name | |
# %% ../app.ipynb 3 | |
learn = load_learner('model.pkl') | |
# %% ../app.ipynb 5 | |
def predict(image): | |
img = PILImage.create(img) | |
_pred, _pred_w_idx, probs = learn.predict(img) | |
labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)} | |
return labels_probs | |
# %% ../app.ipynb 6 | |
interface_options = { | |
"title": "KhetAi", | |
"description": "An web app that predicts the disease based on Image", | |
"interpretation": "default", | |
"layout": "horizontal", | |
"allow_flagging": "never", | |
"enable_queue": True | |
} | |
"""demo = gradio.Interface(fn=predict, | |
inputs=gradio.inputs.Image(shape=(512, 512)), | |
outputs=gradio.outputs.Label(num_top_classes=5), | |
**interface_options)""" | |
demo = gradio.Interface(fn=predict, | |
inputs="image", | |
outputs="label") | |
# demo_options = { | |
# "inline": True, | |
# "inbrowser": True, | |
# "share": True, | |
# "show_error": True, | |
# "server_name": "0.0.0.0", | |
# "server_port": 5000, | |
# "enable_queue": True, | |
# } | |
#demo.launch(**demo_options) | |
demo.launch(share=True) | |