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import gradio as gr | |
import torch | |
from utils import load_model, generate_random_img, schedule_function | |
import time | |
import random | |
import threading | |
from gradio_client import Client | |
def generate_image(): | |
with torch.no_grad(): | |
model = load_model('generator', 'generator_model_epoch_94.pth') | |
generated_image = generate_random_img(model) | |
return generated_image | |
iface = gr.Interface( | |
fn=generate_image, | |
inputs=[], | |
outputs=gr.outputs.Image(type='numpy'), | |
allow_screenshot=True, | |
title='Random Landscape Image Generator By Huseyn Gorbani', | |
description='This app generates random images, using DCFAN inspired WGAN-GP model. Special Thanks to Aladdin Persson and Emilien Dupont for their insightful repos on GitHub. Aladdin Persson (repo: https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/GANs/4.%20WGAN-GP) Emilien Dupont (repo: https://github.com/EmilienDupont/wgan-gp/blob/master/training.py)', | |
css='img_styles.css', | |
) | |
if __name__ == '__main__': | |
scheduler_thread = threading.Thread(target=schedule_function) # avoiding sleep, again this project is for academic purposes only | |
# scheduler_thread.start() | |
iface.launch() |