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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
# %% auto 0
__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_img']
# %% app.ipynb 1
from fastai.vision.all import *
import gradio as gr
# def is_cat(x): return x[0].isupper()
# %% app.ipynb 3
learn_swin = load_learner("model_swin.pkl")
learn_conv = load_learner("model_conv.pkl")
# %% app.ipynb 5
categories = [
'Bathroom', 'Bedroom', 'Floor plan', 'Front', 'Home Office', 'Kitchen',
'Laundry', 'Living room', 'Parking', 'Porch', 'Swimming pool', 'Views',
'Walk In Closet', 'Yard'
]
def classify_img(img, use_conv):
pred,idx,probs = learn_conv.predict(img) if use_conv else learn_swin.predict(img)
return dict(zip(categories, map(float, probs)))
# %% app.ipynb 7
examples = [
["kitchen.jpg", False],
["living_room.jpg", False],
["living_room2.jpg", False],
["kitchen.jpg", True],
["living_room.jpg", True],
["living_room2.jpg", True]
]
intf = gr.Interface(
fn=classify_img,
inputs=[
gr.components.Image(shape=(640, 480)),
gr.components.Checkbox(label="Use conv model", value=False),
],
outputs=[
gr.components.Label()
],
examples=examples
)
intf.launch(inline=False)
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