Create app.py

app.py

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+import gradio as gr
+import os
+import time
+import argparse
+import yaml, math
+from tqdm import trange
+import torch
+import numpy as np
+from omegaconf import OmegaConf
+import torch.distributed as dist
+from pytorch_lightning import seed_everything
+
+from lvdm.samplers.ddim import DDIMSampler
+from lvdm.utils.common_utils import str2bool
+from lvdm.utils.dist_utils import setup_dist, gather_data
+from lvdm.utils.saving_utils import npz_to_video_grid, npz_to_imgsheet_5d
+from scripts.sample_utils import load_model, get_conditions, make_model_input_shape, torch_to_np
+
+config_path = "model_config.yaml"
+config = OmegaConf.load(config_path)
+
+# # get model & sampler
+# model, _, _ = load_model(config, opt.ckpt_path, 
+#                          inject_lora=opt.inject_lora, 
+#                          lora_scale=opt.lora_scale, 
+#                          lora_path=opt.lora_path
+#                          )
+# ddim_sampler = DDIMSampler(model) if opt.sample_type == "ddim" else None
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()