Update app.py

app.py

@@ -1,170 +1,168 @@
-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]
-    )
-
+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
+import random
+from datasets import load_dataset
+
+# ✅ 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 = [sample["image"].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)