app.py

__all__ = ['learn', 'classify_image', 'categories', 'classifier', 'virtual','image', 'label', 'examples', 'intf']

# Cell
from fastai.vision.all import *
import gradio as gr
import timm
import pickle
import torch

#   with open('./model.pkl', 'rb') as f:
#    model = pickle.load(f)

with open('model.pkl', 'rb') as f:
    model = pickle.load(f)

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()