Update app.py

app.py

@@ -1,9 +1,6 @@
 import os
 import subprocess
-
 os.system("pip install gradio==3.50")
-
-
 from argparse import Namespace
 import pprint
 import numpy as np
@@ -11,7 +8,7 @@ from PIL import Image
 import torch
 import torchvision.transforms as transforms
 import cv2
-import dlibs.dlib
+import dlib
 import matplotlib.pyplot as plt
 import gradio as gr  # Importing Gradio as gr
 from tensorflow.keras.preprocessing.image import img_to_array
@@ -56,7 +53,7 @@ opts['checkpoint_path'] = model_path
 opts = Namespace(**opts)
 net = pSp(opts)
 net.eval()
-net.cuda()
+net.cpu()  # Set the model to run on CPU
 
 print('Model successfully loaded!')
 
@@ -131,9 +128,9 @@ def apply_aging(image, target_age):
     results = []
     for age_transformer in age_transformers:
         with torch.no_grad():
-            input_image_age = [age_transformer(input_image.cpu()).to('cuda')]
+            input_image_age = [age_transformer(input_image.cpu()).to('cpu')]
             input_image_age = torch.stack(input_image_age)
-            result_tensor = net(input_image_age.to("cuda").float(), randomize_noise=False, resize=False)[0]
+            result_tensor = net(input_image_age.to("cpu").float(), randomize_noise=False, resize=False)[0]
             result_image = tensor2im(result_tensor)
             results.append(np.array(result_image))
     final_result = results[0]
@@ -172,4 +169,4 @@ iface = gr.Interface(
     description="Upload an image to apply an aging effect. The application will generate two results: one with good teeth and one with bad teeth."
 )
 
-iface.launch(debug=True)
+iface.launch(debug=True)