Create app.py

app.py

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+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
+
+# 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)
+
+# 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)
+    
+    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)
+    
+    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
+    
+    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)
+        
+        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
+        
+        optimizer.zero_grad()
+        total_loss.backward()
+        optimizer.step()
+    
+    return im_convert(target)
+
+# Example images
+examples = [
+    ["path/to/content1.jpg", "path/to/style1.jpg"],
+    ["path/to/content2.jpg", "path/to/style2.jpg"],
+    ["path/to/content3.jpg", "path/to/style3.jpg"],
+]
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Neural Style Transfer")
+    with gr.Row():
+        with gr.Column():
+            content_input = gr.Image(label="Content Image")
+            style_input = gr.Image(label="Style Image")
+        with gr.Column():
+            output_image = gr.Image(label="Output Image")
+    
+    with gr.Row():
+        alpha_slider = gr.Slider(minimum=0, maximum=1, value=1, step=0.1, label="Content Weight (α)")
+        beta_slider = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.1, label="Style Weight (β)")
+    
+    with gr.Row():
+        conv1_1_slider = gr.Slider(minimum=0, maximum=1, value=1, step=0.1, label="Conv1_1 Weight")
+        conv2_1_slider = gr.Slider(minimum=0, maximum=1, value=0.8, step=0.1, label="Conv2_1 Weight")
+        conv3_1_slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.1, label="Conv3_1 Weight")
+        conv4_1_slider = gr.Slider(minimum=0, maximum=1, value=0.3, step=0.1, label="Conv4_1 Weight")
+        conv5_1_slider = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.1, label="Conv5_1 Weight")
+    
+    steps_slider = gr.Slider(minimum=100, maximum=2000, value=1000, step=100, label="Number of Steps")
+    
+    run_button = gr.Button("Run Style Transfer")
+    
+    run_button.click(
+        style_transfer,
+        inputs=[
+            content_input,
+            style_input,
+            alpha_slider,
+            beta_slider,
+            conv1_1_slider,
+            conv2_1_slider,
+            conv3_1_slider,
+            conv4_1_slider,
+            conv5_1_slider,
+            steps_slider
+        ],
+        outputs=output_image
+    )
+    
+    gr.Examples(
+        examples,
+        inputs=[content_input, style_input]
+    )
+
+demo.launch()