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__all__ = ['learn', 'classify_image', 'categories', 'classifier', 'virtual','image', 'label', 'examples', 'intf'] | |
# Cell | |
from fastai.vision.all import * | |
import gradio as gr | |
import timm | |
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(img) | |
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'] | |
# Cell | |
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples,share=True) | |
intf.launch() |