import random import gradio as gr import os from os import path import sys sys.path.append(path.dirname(path.abspath(__file__))) from src.olgen.ol_generator import VecOnlineGenerator from src.olgen.olg_policy import RLGenPolicy from src.smb.level import save_batch from src.utils.filesys import getpath from src.utils.img import make_img_sheet import torch device = 'cuda:0' if torch.cuda.is_available() else 'cpu' def generate_and_play(): path = 'models/example_policy' # 使用example policy做生成 N, L = 8, 10 plc = RLGenPolicy.from_path(path, device) generator = VecOnlineGenerator(plc, g_device=device) fd, _ = os.path.split(getpath(path)) os.makedirs(fd, exist_ok=True) lvls = generator.generate(N, L) imgs = [lvl.to_img() for lvl in lvls] return imgs with gr.Blocks(title="NCERL Demo") as demo: gr.Markdown( """

Negatively Correlated Ensemble RL

![banner](/file=media/banner.png) """ ) gallery = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery" , columns=[3], rows=[1], object_fit="contain", height="auto") btn = gr.Button("Generate levels", scale=0) btn.click(generate_and_play, None, gallery) if __name__ == "__main__": demo.launch(allowed_paths=["./"])