app.py

import torch 
import torch.nn as nn
import albumentations as A
from albumentations.pytorch import ToTensorV2
from model import Generator

import numpy as np
from PIL import Image
import gradio as gr

model = Generator(3)
model_path = 'state_dict.pth'
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))

transform = A.Compose([
                A.Resize(width=256, height=256),
                A.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_pixel_value=255.0),
                ToTensorV2()
            ])

def main(image):
    augmented = transform(image=image)    
    tensor_img = augmented['image']

    with torch.inference_mode():
        pred = model(tensor_img.unsqueeze(0))
        pred = pred.squeeze(0).permute(1, 2, 0).numpy()
    return pred

app = gr.Interface(
    fn=main,
    inputs=gr.Image(),
    outputs=gr.Image(),
    examples=['1.jpg', '2.jpg', '3.jpg', '4.jpg']
)

app.launch()