app.py

__all__ = ['is_real', 'learn', 'virtual staging', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
import pathlib

#|export
#fastai has to be available, i.e. fastai folder
from fastai.vision.all import *
import gradio as gr 

def is_real(x): return x[0].isupper()

# Cell
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']

intf = gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples)
intf.launch(inline=False)