Changed model loading asynchronously

app.py

@@ -1,21 +1,29 @@
 import gradio as gr
 from PIL import Image
 
+import _thread
+import time
 import torch
 import numpy as np
 from models.network_swinir import SwinIR as net
 
 # model load
-param_key_g = 'params_ema'
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+def load_model():
+    global super_res_model
+    global device
+    
+    param_key_g = 'params_ema'
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    super_res_model = net(upscale=4, in_chans=3, img_size=64, window_size=8,
+                        img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240,
+                        num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+                        mlp_ratio=2, upsampler='nearest+conv', resi_connection='3conv')
+    super_res_pretrained_model = torch.load("model_zoo/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_PSNR.pth")
+    super_res_model.load_state_dict(super_res_pretrained_model[param_key_g] if param_key_g in super_res_pretrained_model.keys() else super_res_pretrained_model, strict=True)
+    super_res_model.eval()
 
-super_res_model = net(upscale=4, in_chans=3, img_size=64, window_size=8,
-                    img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240,
-                    num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
-                    mlp_ratio=2, upsampler='nearest+conv', resi_connection='3conv')
-super_res_pretrained_model = torch.load("model_zoo/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_PSNR.pth")
-super_res_model.load_state_dict(super_res_pretrained_model[param_key_g] if param_key_g in super_res_pretrained_model.keys() else super_res_pretrained_model, strict=True)
-super_res_model.eval()
+_thread.start_new_thread(load_model, tuple())
 
 def predict(input_img):
     out = None