app.py

import torch
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr
from model import Generator

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Configurações
latent_dim = 100
generator = Generator(latent_dim).to(device)
generator.load_state_dict(torch.load("generator128.pth", map_location=device))
generator.eval()

# Função para gerar imagem
def generate_image(seed: int):
    torch.manual_seed(seed)
    noise = torch.randn(1, latent_dim, 1, 1, device=device)
    with torch.no_grad():
        fake_image = generator(noise).detach().cpu()
    fake_image = (fake_image + 1) / 2  # Traz para [0, 1]
    to_pil = transforms.ToPILImage()
    return to_pil(fake_image.squeeze(0))

# Interface Gradio
demo = gr.Interface(
    fn=generate_image,
    inputs=gr.Slider(0, 99999, value=42, label="Seed"),
    outputs=gr.Image(type="pil", label="Generated Image"),
    title="ZYI 0.1 - CringeCoin Generator",
    description="Enter a seed to generate a unique 128x128 image with ZYI 0.1."
)

demo.launch()