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import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
#|export
#fastai has to be available, i.e. fastai folder
from fastai.vision.all import *
import gradio as gr
import pickle
import timm
# Cell
learn = load_learner('model.pkl')
# Cell
#categories = learn.dls.vocab
def is_real(x): return x[0].isupper()
#|export
learn = load_learner('model.pkl')
#|export
categories =('Virtual Staging','Real')
def classify_image(img):
pred,idx,probs = learn.predict(im)
return dict(zip(categories,map(float,probs)))
#*** We have to cast to float above because KAGGLE does not return number on the answer it returns tensors, and Gradio does not deal with numpy so we have to cast to float
#|export
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = ['virtual.jpg','real.jpg','dunno.jpg']
intf = gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples)
intf.launch(inline=False)
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