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app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+import torchvision.models as models
+import torch.nn as nn
+import torch.optim as optim
+
+from PIL import Image
+from io import BytesIO
+import random
+from datasets import load_dataset
+from datetime import datetime
+
+# ✅ Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 📦 Image preprocessing
+transform = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor()
+])
+
+def load_image(img):
+    image = img.convert('RGB')
+    return transform(image).unsqueeze(0).to(device)
+
+# 🔧 NST Core Classes
+class Normalization(nn.Module):
+    def __init__(self, mean, std):
+        super().__init__()
+        self.mean = mean.view(-1, 1, 1)
+        self.std = std.view(-1, 1, 1)
+    def forward(self, img):
+        return (img - self.mean) / self.std
+
+class ContentLoss(nn.Module):
+    def __init__(self, target):
+        super().__init__()
+        self.target = target.detach()
+        self.loss = 0
+    def forward(self, input):
+        self.loss = nn.functional.mse_loss(input, self.target)
+        return input
+
+def gram_matrix(input):
+    b, c, h, w = input.size()
+    features = input.view(c, h * w)
+    G = torch.mm(features, features.t())
+    return G.div(c * h * w)
+
+class StyleLoss(nn.Module):
+    def __init__(self, target_feature):
+        super().__init__()
+        self.target = gram_matrix(target_feature).detach()
+        self.loss = 0
+    def forward(self, input):
+        G = gram_matrix(input)
+        self.loss = nn.functional.mse_loss(G, self.target)
+        return input
+
+# 🧠 Model builder
+def get_model_losses(cnn, norm_mean, norm_std, style_img, content_img):
+    norm = Normalization(norm_mean, norm_std).to(device)
+    model = nn.Sequential(norm)
+
+    content_losses, style_losses = [], []
+    i = 0
+    for layer in cnn.children():
+        name = None
+        if isinstance(layer, nn.Conv2d):
+            i += 1
+            name = f"conv_{i}"
+        elif isinstance(layer, nn.ReLU):
+            name = f"relu_{i}"
+            layer = nn.ReLU(inplace=False)
+        elif isinstance(layer, nn.MaxPool2d):
+            name = f"pool_{i}"
+        elif isinstance(layer, nn.BatchNorm2d):
+            name = f"bn_{i}"
+        if name:
+            model.add_module(name, layer)
+            if name == "conv_4":
+                target = model(content_img).detach()
+                content_loss = ContentLoss(target)
+                model.add_module(f"content_loss_{i}", content_loss)
+                content_losses.append(content_loss)
+            if name in ["conv_1", "conv_2", "conv_3", "conv_4", "conv_5"]:
+                target_feature = model(style_img).detach()
+                style_loss = StyleLoss(target_feature)
+                model.add_module(f"style_loss_{i}", style_loss)
+                style_losses.append(style_loss)
+
+    for j in range(len(model) - 1, -1, -1):
+        if isinstance(model[j], ContentLoss) or isinstance(model[j], StyleLoss):
+            break
+    return model[:j + 1], style_losses, content_losses
+
+# 🎲 Random selector from Hugging Face dataset
+def get_random_image_pair():
+    ds = load_dataset("heramb04/Famous-paintings", split="train")
+    samples = random.sample(list(ds), 2)
+    imgs = [Image.open(BytesIO(sample["image"]["bytes"])).convert("RGB") for sample in samples]
+    return imgs[0], imgs[1]
+
+# 🖌️ NST logic
+def run_nst(content_pil, style_pil, steps=300):
+    content = load_image(content_pil)
+    style = load_image(style_pil)
+    input_img = content.clone().requires_grad_(True)
+
+    cnn = models.vgg19(pretrained=True).features.to(device).eval()
+    norm_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
+    norm_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
+
+    model, style_losses, content_losses = get_model_losses(cnn, norm_mean, norm_std, style, content)
+    optimizer = optim.LBFGS([input_img])
+    run = [0]
+
+    while run[0] <= steps:
+        def closure():
+            input_img.data.clamp_(0, 1)
+            optimizer.zero_grad()
+            model(input_img)
+            style_score = sum(sl.loss for sl in style_losses)
+            content_score = sum(cl.loss for cl in content_losses)
+            loss = content_score + 1e6 * style_score
+            loss.backward()
+            run[0] += 1
+            return loss
+        optimizer.step(closure)
+
+    output = input_img.clone().detach().cpu().squeeze(0)
+    return transforms.ToPILImage()(output)
+
+# 🎛️ Gradio UI
+with gr.Blocks(title="Neural Style Transfer — A + B = C") as demo:
+    gr.Markdown("## 🎨 Neural Style Transfer<br>Upload two images OR pick random paintings to remix")
+
+    with gr.Row():
+        with gr.Column():
+            content_input = gr.Image(label="🖼️ Content Image", type="pil")
+            style_input = gr.Image(label="🎨 Style Image", type="pil")
+            steps_slider = gr.Slider(100, 500, value=300, step=50, label="Optimization Steps")
+            upload_button = gr.Button("✨ Stylize Uploaded Images")
+            random_button = gr.Button("🎲 Pick Random & Generate")
+
+        with gr.Column():
+            gr.Markdown("### 🧠 A + B = C")
+            content_preview = gr.Image(label="A: Content", interactive=False)
+            style_preview = gr.Image(label="B: Style", interactive=False)
+            output_preview = gr.Image(label="C: Stylized Output", interactive=False)
+
+    upload_button.click(
+        fn=run_nst,
+        inputs=[content_input, style_input, steps_slider],
+        outputs=output_preview
+    )
+
+    def random_nst_wrapper(steps):
+        content_img, style_img = get_random_image_pair()
+        result = run_nst(content_img, style_img, steps)
+        return content_img, style_img, result
+
+    random_button.click(
+        fn=random_nst_wrapper,
+        inputs=[steps_slider],
+        outputs=[content_preview, style_preview, output_preview]
+    )
+
+demo.launch(share=True)

requirements.txt

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+torch
+torchvision
+gradio
+Pillow
+datasets
+matplotlib

style_transfer.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+import torchvision.models as models
+from PIL import Image
+import torchvision.transforms.functional as TF
+
+# 🚀 Device configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 🔧 Preprocessing
+transform = transforms.Compose([
+    transforms.Resize((512, 512)),
+    transforms.ToTensor()
+])
+
+def load_image(img):
+    image = img.convert("RGB")
+    return transform(image).unsqueeze(0).to(device)
+
+# 🎯 Loss modules
+class Normalization(nn.Module):
+    def __init__(self, mean, std):
+        super().__init__()
+        self.mean = mean.view(-1, 1, 1)
+        self.std = std.view(-1, 1, 1)
+    def forward(self, img):
+        return (img - self.mean) / self.std
+
+class ContentLoss(nn.Module):
+    def __init__(self, target):
+        super().__init__()
+        self.target = target.detach()
+        self.loss = 0
+    def forward(self, input):
+        self.loss = nn.functional.mse_loss(input, self.target)
+        return input
+
+def gram_matrix(input):
+    b, c, h, w = input.size()
+    features = input.view(c, h * w)
+    G = torch.mm(features, features.t())
+    return G.div(c * h * w)
+
+class StyleLoss(nn.Module):
+    def __init__(self, target_feature):
+        super().__init__()
+        self.target = gram_matrix(target_feature).detach()
+        self.loss = 0
+    def forward(self, input):
+        G = gram_matrix(input)
+        self.loss = nn.functional.mse_loss(G, self.target)
+        return input
+
+# 🧬 Model builder
+def get_model_losses(cnn, norm_mean, norm_std, style_img, content_img):
+    normalization = Normalization(norm_mean, norm_std).to(device)
+    model = nn.Sequential(normalization)
+
+    content_losses = []
+    style_losses = []
+
+    i = 0
+    for layer in cnn.children():
+        name = None
+        if isinstance(layer, nn.Conv2d):
+            i += 1
+            name = f"conv_{i}"
+        elif isinstance(layer, nn.ReLU):
+            name = f"relu_{i}"
+            layer = nn.ReLU(inplace=False)
+        elif isinstance(layer, nn.MaxPool2d):
+            name = f"pool_{i}"
+        elif isinstance(layer, nn.BatchNorm2d):
+            name = f"bn_{i}"
+        if name:
+            model.add_module(name, layer)
+            if name == "conv_4":
+                target = model(content_img).detach()
+                content_loss = ContentLoss(target)
+                model.add_module(f"content_loss_{i}", content_loss)
+                content_losses.append(content_loss)
+            if name in ["conv_1", "conv_2", "conv_3", "conv_4", "conv_5"]:
+                target_feature = model(style_img).detach()
+                style_loss = StyleLoss(target_feature)
+                model.add_module(f"style_loss_{i}", style_loss)
+                style_losses.append(style_loss)
+
+    for j in range(len(model) - 1, -1, -1):
+        if isinstance(model[j], ContentLoss) or isinstance(model[j], StyleLoss):
+            break
+
+    return model[:j + 1], style_losses, content_losses
+
+# ✨ Stylization pipeline
+def run_nst(content_pil, style_pil, steps=300):
+    content = load_image(content_pil)
+    style = load_image(style_pil)
+    input_img = content.clone().requires_grad_(True)
+
+    cnn = models.vgg19(pretrained=True).features.to(device).eval()
+    norm_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
+    norm_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
+
+    model, style_losses, content_losses = get_model_losses(
+        cnn, norm_mean, norm_std, style, content
+    )
+
+    optimizer = optim.LBFGS([input_img])
+    run = [0]
+
+    while run[0] <= steps:
+        def closure():
+            input_img.data.clamp_(0, 1)
+            optimizer.zero_grad()
+            model(input_img)
+
+            style_score = sum(sl.loss for sl in style_losses)
+            content_score = sum(cl.loss for cl in content_losses)
+
+            loss = content_score + 1e6 * style_score
+            loss.backward()
+
+            run[0] += 1
+            return loss
+        optimizer.step(closure)
+
+    output = input_img.clone().detach().cpu().squeeze(0)
+    return TF.to_pil_image(output)

utils.py

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+import os
+import random
+from datetime import datetime
+from PIL import Image
+import matplotlib.pyplot as plt
+
+# 📦 Resize + convert image
+def resize_image(image, size=(512, 512)):
+    return image.convert("RGB").resize(size)
+
+# 📂 Save output with timestamp
+def save_output(image, save_dir="output", prefix="stylized"):
+    os.makedirs(save_dir, exist_ok=True)
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"{prefix}_{timestamp}.jpg"
+    path = os.path.join(save_dir, filename)
+    image.save(path)
+    return path
+
+# 📜 Log content + style pairing
+def log_run(content_name, style_name, log_path="log.txt"):
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    line = f"{timestamp} | Content: {content_name} + Style: {style_name} = Stylized\n"
+    with open(log_path, "a") as f:
+        f.write(line)
+
+# 🖼️ Triptych visualizer: A + B = C
+def show_triptych(content_img, style_img, output_img):
+    fig, axs = plt.subplots(1, 3, figsize=(15, 5))
+    axs[0].imshow(content_img); axs[0].set_title("A: Content"); axs[0].axis('off')
+    axs[1].imshow(style_img);  axs[1].set_title("B: Style");   axs[1].axis('off')
+    axs[2].imshow(output_img); axs[2].set_title("C: Output");  axs[2].axis('off')
+    plt.suptitle("A + B = C", fontsize=20)
+    plt.show()