Update app.py

app.py

@@ -1,66 +1,66 @@
 import gradio as gr
-import torch
-import torch.optim as optim
-import torchvision.models as models
-import torchvision.transforms as transforms
-from PIL import Image
-import numpy as np
-import requests
-from io import BytesIO
+# import torch
+# import torch.optim as optim
+# import torchvision.models as models
+# import torchvision.transforms as transforms
+# from PIL import Image
+# import numpy as np
+# import requests
+# from io import BytesIO
 
 # Load VGG19 model
-vgg = models.vgg19(pretrained=True).features
-for param in vgg.parameters():
-    param.requires_grad_(False)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-vgg.to(device)
+# vgg = models.vgg19(pretrained=True).features
+# for param in vgg.parameters():
+#     param.requires_grad_(False)
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# vgg.to(device)
 
-# Helper functions (load_image, im_convert, get_features, gram_matrix)
-# ... (Include the helper functions you provided earlier here)
+# # Helper functions (load_image, im_convert, get_features, gram_matrix)
+# # ... (Include the helper functions you provided earlier here)
 
-def style_transfer(content_image, style_image, alpha, beta, conv1_1, conv2_1, conv3_1, conv4_1, conv5_1, steps):
-    content = load_image(content_image).to(device)
-    style = load_image(style_image, shape=content.shape[-2:]).to(device)
+# def style_transfer(content_image, style_image, alpha, beta, conv1_1, conv2_1, conv3_1, conv4_1, conv5_1, steps):
+#     content = load_image(content_image).to(device)
+#     style = load_image(style_image, shape=content.shape[-2:]).to(device)
     
-    content_features = get_features(content, vgg)
-    style_features = get_features(style, vgg)
-    style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}
+#     content_features = get_features(content, vgg)
+#     style_features = get_features(style, vgg)
+#     style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}
     
-    target = content.clone().requires_grad_(True).to(device)
+#     target = content.clone().requires_grad_(True).to(device)
     
-    style_weights = {
-        'conv1_1': conv1_1,
-        'conv2_1': conv2_1,
-        'conv3_1': conv3_1,
-        'conv4_1': conv4_1,
-        'conv5_1': conv5_1
-    }
+#     style_weights = {
+#         'conv1_1': conv1_1,
+#         'conv2_1': conv2_1,
+#         'conv3_1': conv3_1,
+#         'conv4_1': conv4_1,
+#         'conv5_1': conv5_1
+#     }
     
-    content_weight = alpha
-    style_weight = beta * 1e6
+#     content_weight = alpha
+#     style_weight = beta * 1e6
     
-    optimizer = optim.Adam([target], lr=0.003)
+#     optimizer = optim.Adam([target], lr=0.003)
     
-    for ii in range(1, steps+1):
-        target_features = get_features(target, vgg)
-        content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])**2)
+#     for ii in range(1, steps+1):
+#         target_features = get_features(target, vgg)
+#         content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])**2)
         
-        style_loss = 0
-        for layer in style_weights:
-            target_feature = target_features[layer]
-            target_gram = gram_matrix(target_feature)
-            _, d, h, w = target_feature.shape
-            style_gram = style_grams[layer]
-            layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram)**2)
-            style_loss += layer_style_loss / (d * h * w)
+#         style_loss = 0
+#         for layer in style_weights:
+#             target_feature = target_features[layer]
+#             target_gram = gram_matrix(target_feature)
+#             _, d, h, w = target_feature.shape
+#             style_gram = style_grams[layer]
+#             layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram)**2)
+#             style_loss += layer_style_loss / (d * h * w)
         
-        total_loss = content_weight * content_loss + style_weight * style_loss
+#         total_loss = content_weight * content_loss + style_weight * style_loss
         
-        optimizer.zero_grad()
-        total_loss.backward()
-        optimizer.step()
+#         optimizer.zero_grad()
+#         total_loss.backward()
+#         optimizer.step()
     
-    return im_convert(target)
+#     return im_convert(target)
 
 # Example images
 examples = [