app.py

import gradio as gr
import torch
import numpy as np
import os
from model import gen_model
import torchvision.transforms as T

# Model
gen,transform_gen=gen_model()
# print(gen)
to_img=T.ToPILImage()
# examples=["examples/input_0.png","examples/input_9.png"]
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Predict Function
def predict(img):
  # Apply Transformations
  # img=np.array(img)
  img=transform_gen(img).unsqueeze(0)
  # Predict
  gen.eval()
  with torch.inference_mode():
    y_gen=gen(img)
  y_gen=y_gen[0]
  y_gen=to_img(y_gen)
  return y_gen

# Gradio App
title="Satellite-to-Map GAN"
description="This is a Sattelite Image to Map converter"

demo=gr.Interface(fn=predict,
                  inputs=gr.Image(type='pil'),
                  outputs=gr.Image(type='pil'),
                  title=title ,
                  examples=example_list,
                  description=description)
demo.launch(debug=False)