diff --git a/neural_style_app/.gitignore b/neural_style_app/.gitignore
new file mode 100644
index 0000000..ba0430d
--- /dev/null
+++ b/neural_style_app/.gitignore
@@ -0,0 +1 @@
+__pycache__/
\ No newline at end of file
diff --git a/neural_style_app/app.py b/neural_style_app/app.py
new file mode 100644
index 0000000..5bb27c6
--- /dev/null
+++ b/neural_style_app/app.py
@@ -0,0 +1,99 @@
+from flask import Flask, render_template, request, redirect, url_for, send_file, jsonify
+from mode_style_transfer import StyleTransferModel, save_image, StyleTransferVisualizer
+from PIL import Image
+import io
+import torch
+from torchvision.models import vgg19, VGG19_Weights
+import torchvision.transforms as transforms
+import os
+import matplotlib.pyplot as plt
+import base64
+
+app = Flask(__name__)
+
+# Image transformation
+imsize = 512 if torch.cuda.is_available() else 128
+loader = transforms.Compose([
+    transforms.Resize(imsize),
+    transforms.ToTensor()
+])
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+visualizations = []
+
+def image_loader(image_bytes):
+    image = Image.open(io.BytesIO(image_bytes))
+    image = loader(image).unsqueeze(0)
+    return image.to(device, torch.float)
+
+def tensor_to_image(tensor):
+    image = tensor.clone().detach().squeeze(0)
+    image = transforms.ToPILImage()(image)
+    return image
+
+def image_to_base64(image):
+    img_io = io.BytesIO()
+    image.save(img_io, 'JPEG')
+    img_io.seek(0)
+    return base64.b64encode(img_io.getvalue()).decode('utf-8')
+
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        content_image_file = request.files['content_image']
+        style_image_file = request.files['style_image']
+
+        # Load images directly from the uploaded files
+        content_image = Image.open(content_image_file)
+        style_image = Image.open(style_image_file)
+
+        # Pass the images to the StyleTransferModel
+        style_transfer = StyleTransferModel(content_image, style_image)
+        output = style_transfer.run_style_transfer()
+
+        # Convert the output tensor to an image
+        output_image = tensor_to_image(output)
+
+        # Convert the image to Base64 for JSON response
+        image_base64 = image_to_base64(output_image)
+
+        return jsonify({'image': image_base64})
+
+    return render_template('index.html')
+
+@app.route('/visualize', methods=['POST'])
+def visualize():
+    cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.to(device).eval()
+    
+    cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
+    cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
+    
+    content_image_bytes = visualizations[0]  # The last saved content image
+    content_image = image_loader(content_image_bytes)
+
+    style_transfer = StyleTransferModel(content_image, content_image)
+    
+    # Running the model for visualization purpose
+    input_img = content_image.clone().requires_grad_(True)
+    
+    model, _, _ = style_transfer.get_style_model_and_losses(
+        cnn, cnn_normalization_mean, cnn_normalization_std, content_image, content_image)
+
+    layer_visualizations = []
+
+    # Run the image through each layer and store the output
+    for i, layer in enumerate(model):
+        input_img = layer(input_img)
+        with torch.no_grad():
+            output_image = tensor_to_image(input_img.clamp(0, 1))
+            img_io = io.BytesIO()
+            output_image.save(img_io, 'JPEG')
+            img_io.seek(0)
+            layer_visualizations.append(img_io.getvalue())  # Save the image bytes
+    
+    return render_template('visualize.html', visualizations=layer_visualizations)
+
+if __name__ == '__main__':
+    app.run(debug=True)
diff --git a/neural_style_app/mode_style_transfer.py b/neural_style_app/mode_style_transfer.py
new file mode 100644
index 0000000..1b89c75
--- /dev/null
+++ b/neural_style_app/mode_style_transfer.py
@@ -0,0 +1,258 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+from PIL import Image
+import matplotlib.pyplot as plt
+
+import torchvision.transforms as transforms
+from torchvision.models import vgg19, VGG19_Weights
+
+from torchvision import models
+import matplotlib.pyplot as plt
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+torch.set_default_device(device)
+
+def image_loader(image):
+    #image = Image.open(image_name)
+    # fake batch dimension required to fit network's input dimensions
+
+    imsize = 512 if torch.cuda.is_available() else 128  # use small size if no GPU
+
+    loader = transforms.Compose([
+        transforms.Resize(imsize),  # scale imported image
+        transforms.ToTensor()])  # transform it into a torch tensor
+    image = loader(image).unsqueeze(0)
+    return image.to(device, torch.float)
+
+def save_image(tensor, path):
+    image = tensor.clone().detach()
+    image = image.squeeze(0)
+    image = transforms.ToPILImage()(image)
+    image.save(path)
+
+class ContentLoss(nn.Module):
+    def __init__(self, target,):
+        super(ContentLoss, self).__init__()
+        # we 'detach' the target content from the tree used
+        # to dynamically compute the gradient: this is a stated value,
+        # not a variable. Otherwise the forward method of the criterion
+        # will throw an error.
+        self.target = target.detach()
+    def forward(self, input):
+        self.loss = F.mse_loss(input, self.target)
+        return input
+    
+def gram_matrix(input):
+    a, b, c, d = input.size()  # a=batch size(=1)
+    # b=number of feature maps
+    # (c,d)=dimensions of a f. map (N=c*d)
+    features = input.view(a * b, c * d)  # resize F_XL into \hat F_XL
+    G = torch.mm(features, features.t())  # compute the gram product
+    # we 'normalize' the values of the gram matrix
+    # by dividing by the number of element in each feature maps.
+    return G.div(a * b * c * d)
+
+class StyleLoss(nn.Module):
+    def __init__(self, target_feature):
+        super(StyleLoss, self).__init__()
+        self.target = gram_matrix(target_feature).detach()
+    def forward(self, input):
+        G = gram_matrix(input)
+        self.loss = F.mse_loss(G, self.target)
+        return input
+    
+#cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.eval()
+
+#cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406])
+#cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225])
+
+# create a module to normalize input image so we can easily put it in a
+# ``nn.Sequential``
+class Normalization(nn.Module):
+    def __init__(self, mean, std):
+        super(Normalization, self).__init__()
+        # .view the mean and std to make them [C x 1 x 1] so that they can
+        # directly work with image Tensor of shape [B x C x H x W].
+        # B is batch size. C is number of channels. H is height and W is width.
+        self.mean = torch.tensor(mean).view(-1, 1, 1)
+        self.std = torch.tensor(std).view(-1, 1, 1)
+    def forward(self, img):
+        # normalize ``img``
+        return (img - self.mean) / self.std
+    
+# desired depth layers to compute style/content losses :
+content_layers_default = ['conv_4']
+style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
+
+def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
+                               style_img, content_img,
+                               content_layers=content_layers_default,
+                               style_layers=style_layers_default):
+    # normalization module
+    normalization = Normalization(normalization_mean, normalization_std)
+
+    # just in order to have an iterable access to or list of content/style
+    # losses
+    content_losses = []
+    style_losses = []
+
+    # assuming that ``cnn`` is a ``nn.Sequential``, so we make a new ``nn.Sequential``
+    # to put in modules that are supposed to be activated sequentially
+    model = nn.Sequential(normalization)
+
+    i = 0  # increment every time we see a conv
+    for layer in cnn.children():
+        if isinstance(layer, nn.Conv2d):
+            i += 1
+            name = 'conv_{}'.format(i)
+        elif isinstance(layer, nn.ReLU):
+            name = 'relu_{}'.format(i)
+            # The in-place version doesn't play very nicely with the ``ContentLoss``
+            # and ``StyleLoss`` we insert below. So we replace with out-of-place
+            # ones here.
+            layer = nn.ReLU(inplace=False)
+        elif isinstance(layer, nn.MaxPool2d):
+            name = 'pool_{}'.format(i)
+        elif isinstance(layer, nn.BatchNorm2d):
+            name = 'bn_{}'.format(i)
+        else:
+            raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
+
+        model.add_module(name, layer)
+
+        if name in content_layers:
+            # add content loss:
+            target = model(content_img).detach()
+            content_loss = ContentLoss(target)
+            model.add_module("content_loss_{}".format(i), content_loss)
+            content_losses.append(content_loss)
+
+        if name in style_layers:
+            # add style loss:
+            target_feature = model(style_img).detach()
+            style_loss = StyleLoss(target_feature)
+            model.add_module("style_loss_{}".format(i), style_loss)
+            style_losses.append(style_loss)
+
+    # now we trim off the layers after the last content and style losses
+    for i in range(len(model) - 1, -1, -1):
+        if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
+            break
+
+    model = model[:(i + 1)]
+
+    return model, style_losses, content_losses
+
+def get_input_optimizer(input_img):
+    # this line to show that input is a parameter that requires a gradient
+    optimizer = optim.LBFGS([input_img])
+    return optimizer
+
+class StyleTransferModel:
+    def __init__(self, content_img, style_img, num_steps=300, style_weight=1000000, content_weight=1):
+        self.content_img = content_img
+        self.style_img = style_img.resize(content_img.size)
+        #self.style_img = self.style_img.resize(self.content_img.size)
+        self.style_img = image_loader(self.style_img)
+        self.content_img = image_loader(self.content_img)
+        self.input_img = self.content_img.clone()
+        self.num_steps = num_steps
+        self.style_weight = style_weight
+        self.content_weight = content_weight
+
+        self.cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.to(device).eval()
+        self.cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
+        self.cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
+        
+    def run_style_transfer(self):
+        print('Building the style transfer model..')
+        model, style_losses, content_losses = get_style_model_and_losses(
+            self.cnn, self.cnn_normalization_mean, self.cnn_normalization_std, 
+            self.style_img, self.content_img)
+
+        self.input_img.requires_grad_(True)
+        model.eval()
+        model.requires_grad_(False)
+        optimizer = get_input_optimizer(self.input_img)
+
+        print('Optimizing..')
+        run = [0]
+        while run[0] <= self.num_steps:
+            def closure():
+                with torch.no_grad():
+                    self.input_img.clamp_(0, 1)
+
+                optimizer.zero_grad()
+                model(self.input_img)
+
+                style_score = 0
+                content_score = 0
+
+                for sl in style_losses:
+                    style_score += sl.loss
+                for cl in content_losses:
+                    content_score += cl.loss
+
+                style_score *= self.style_weight
+                content_score *= self.content_weight
+
+                loss = style_score + content_score
+                loss.backward()
+
+                run[0] += 1
+                if run[0] % 50 == 0:
+                    print(f"run {run[0]}:")
+                    print(f'Style Loss : {style_score.item():4f} Content Loss: {content_score.item():4f}')
+                    print()
+
+                return style_score + content_score
+
+            optimizer.step(closure)
+
+        with torch.no_grad():
+            self.input_img.clamp_(0, 1)
+
+        return self.input_img
+
+
+class StyleTransferVisualizer(StyleTransferModel):
+    def __init__(self, content_img, style_img):
+        super().__init__(content_img, style_img)
+        self.model_layers = self.get_model_layers()
+
+    def get_model_layers(self):
+        cnn = models.vgg19(pretrained=True).features.to(self.device).eval()
+        model_layers = []
+        i = 0
+        for layer in cnn.children():
+            if isinstance(layer, torch.nn.Conv2d):
+                i += 1
+                model_layers.append((f'conv_{i}', layer))
+        return model_layers
+
+    def visualize_layers(self):
+        fig, axs = plt.subplots(len(self.model_layers), 3, figsize=(15, 20))
+        
+        input_img = self.content_img.clone().detach()
+
+        for idx, (name, layer) in enumerate(self.model_layers):
+            input_img = layer(input_img)
+            axs[idx, 0].imshow(self.content_img.squeeze(0).permute(1, 2, 0).cpu().numpy())
+            axs[idx, 0].set_title("Original Image")
+            axs[idx, 0].axis('off')
+            
+            axs[idx, 1].imshow(input_img.squeeze(0).permute(1, 2, 0).cpu().detach().numpy())
+            axs[idx, 1].set_title(f"After {name}")
+            axs[idx, 1].axis('off')
+
+            combined = input_img.clone()
+            combined += self.style_img.squeeze(0)
+            axs[idx, 2].imshow(combined.permute(1, 2, 0).cpu().detach().numpy())
+            axs[idx, 2].set_title(f"Combined (Content + Style) after {name}")
+            axs[idx, 2].axis('off')
+
+        plt.tight_layout()
+        plt.show()
\ No newline at end of file
diff --git a/neural_style_app/templates/index.html b/neural_style_app/templates/index.html
new file mode 100644
index 0000000..eb5723b
--- /dev/null
+++ b/neural_style_app/templates/index.html
@@ -0,0 +1,71 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Style Transfer</title>
+    <style>
+        /* Style the container to ensure it doesn't take up the whole page */
+        #result-container {
+            margin-top: 20px;
+            border: 1px solid #ccc;
+            padding: 10px;
+            display: none; /* Hide the container initially */
+        }
+
+        img {
+            max-width: 100%;
+            height: auto;
+        }
+    </style>
+</head>
+<body>
+    <h1>Style Transfer</h1>
+    <form id="upload-form" action="/" method="POST" enctype="multipart/form-data">
+        <label for="content_image">Upload Content Image:</label>
+        <input type="file" id="content_image" name="content_image" required><br><br>
+
+        <label for="style_image">Upload Style Image:</label>
+        <input type="file" id="style_image" name="style_image" required><br><br>
+
+        <input type="submit" value="Submit">
+    </form>
+
+    <!-- Container for the resulting image -->
+    <div id="result-container">
+        <h2>Resulting Image:</h2>
+        <div id="image-container"></div>
+        <a href="{{ url_for('visualize') }}">
+            <button>Visualize Layers</button>
+        </a>
+    </div>
+
+    <script>
+        // JavaScript to handle form submission and display the image
+        document.getElementById('upload-form').addEventListener('submit', async function(event) {
+            event.preventDefault(); // Prevent the form from submitting the traditional way
+
+            const formData = new FormData(this);
+
+            const response = await fetch('/', {
+                method: 'POST',
+                body: formData
+            });
+
+            if (response.ok) {
+                const data = await response.json();
+
+                const imageElement = document.createElement('img');
+                imageElement.src = 'data:image/jpeg;base64,' + data.image;
+
+                const imageContainer = document.getElementById('image-container');
+                imageContainer.innerHTML = ''; // Clear any previous image
+                imageContainer.appendChild(imageElement);
+
+                const resultContainer = document.getElementById('result-container');
+                resultContainer.style.display = 'block'; // Show the container
+            }
+        });
+    </script>
+</body>
+</html>
diff --git a/neural_style_app/templates/visualize.html b/neural_style_app/templates/visualize.html
new file mode 100644
index 0000000..4e57618
--- /dev/null
+++ b/neural_style_app/templates/visualize.html
@@ -0,0 +1,25 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Visualize Layers</title>
+</head>
+<body>
+    <h1>Layer Visualizations</h1>
+    <p>Select a layer to view the image before and after processing through that layer:</p>
+
+    {% for i in range(visualizations|length) %}
+        <div>
+            <h2>Layer {{ i + 1 }}</h2>
+            <button onclick="document.getElementById('img_before').src='{{ url_for(show_image, index=i) }}';">
+                View Layer {{ i + 1 }}
+            </button>
+        </div>
+    {% endfor %}
+
+    <h2>Layer Output:</h2>
+    <img id="img_before" src="" alt="Layer Image Output" style="max-width: 100%; height: auto;">
+</body>
+</html>
+
diff --git a/neural_style_pytorch/neural_style_tutorial.ipynb b/neural_style_pytorch/neural_style_tutorial.ipynb
index d16315a..f2a3c2a 100644
--- a/neural_style_pytorch/neural_style_tutorial.ipynb
+++ b/neural_style_pytorch/neural_style_tutorial.ipynb
@@ -2,7 +2,7 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 135,
+      "execution_count": 1,
       "metadata": {
         "collapsed": false
       },
@@ -69,7 +69,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 136,
+      "execution_count": 2,
       "metadata": {
         "collapsed": false
       },
@@ -91,11 +91,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 137,
+      "execution_count": 3,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "#torch.cuda.is_available()"
+        "torch.cuda.is_available()"
       ]
     },
     {
@@ -113,7 +124,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 138,
+      "execution_count": 4,
       "metadata": {
         "collapsed": false
       },
@@ -123,6 +134,26 @@
         "torch.set_default_device(device)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "device(type='cuda')"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "device"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -150,7 +181,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 154,
+      "execution_count": 6,
       "metadata": {
         "collapsed": false
       },
@@ -181,6 +212,26 @@
         "    \"we need to import style and content images of the same size\"\n"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "512"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "imsize"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},