diff --git a/__helpers__.py b/__helpers__.py
index e60cd1f..867102d 100644
--- a/__helpers__.py
+++ b/__helpers__.py
@@ -19,15 +19,16 @@
 import urllib
 from tqdm import tqdm
 
+
 if tf.__version__[0] == '1':
 	tf.enable_eager_execution()
 # print("Eager Execution Initialized:",tf.executing_eagerly())
 
 
 # Starting session to download Images from URL if fed.
-s = requests.Session()
-s.proxies = {"http": "http://61.233.25.166:80"}
-r = s.get("http://www.google.com")
+# s = requests.Session()
+# s.proxies = {"http": "http://61.233.25.166:80"}
+# r = s.get("http://www.google.com")
 
 
 # Defining the Feature Layers we need respectively
@@ -60,8 +61,7 @@ def getModel():
   return models.Model(model.input, modelOutput)
 
 
- # Defining GRAM MATRIX
-
+# Defining GRAM MATRIX
 def gram(x):
 
   # number of channels
@@ -73,9 +73,9 @@ def gram(x):
   
   # gram matrix
   gram = tf.matmul(a, a, transpose_a=True)
-  
   return gram / tf.cast(n, tf.float32)
 
+
 # Defining CONTENT COST
 def contentCost(contentFeatures, generateFeatures):
   return tf.reduce_mean(tf.square(contentFeatures-generateFeatures))
@@ -94,13 +94,13 @@ def getFeatures(content, style, model):
   
   # Extracting out the different features from the model output
   contentFeatures = [contentFeature[0] for contentFeature in contentOutputs[numStyleLayers:]]
-  styleFeatures = [styleFeature[0] for styleFeature in styleOutputs[:numStyleLayers]]
-
+  styleFeatures   = [styleFeature[0] for styleFeature in styleOutputs[:numStyleLayers]]
   return contentFeatures, styleFeatures
 
 
 def loadImage(path_to_img):
   max_dim = 512
+  # import pdb; pdb.set_trace()
   if type(path_to_img) == str:
     img2show = Image.open(path_to_img)
   else:
@@ -109,17 +109,19 @@ def loadImage(path_to_img):
   # long = max(img2show.size)
   long = (img2show.size)
 
+  long = long[0]
   scale = max_dim/long
   img = img2show.resize((round(img2show.size[0]*scale), round(img2show.size[1]*scale)), Image.ANTIALIAS)
-  
+
   img = image.img_to_array(img)
   
   # We need to broadcast the image array such that it has a batch dimension 
-  img = np.expand_dims(img2show, axis=0)
+  img = np.expand_dims(img, axis=0)
   return img2show, img
  
 
 def urlToImage(url):
+  import pdb; pdb.set_trace()
   resp = urllib.request.urlopen(url)
   img = np.asarray(bytearray(resp.read()), dtype='uint8')
   img = cv2.imdecode(img, cv2.IMREAD_COLOR)
@@ -132,15 +134,14 @@ def urlToImage(url):
 def inputImageAndPreprocess(path):
   # Loading the image and reshaping it according to VGG19 requirements.
   if path[:4]=='http':
-    # print("Loading Image from Internet...")
+    print("Loading Image from Internet...")
     img2show, img = urlToImage(path)
   else:
-    # print("Loading Image from Local...")
+    print("Loading Image from Local...")
     img2show, img = loadImage(path)
   
   # Preprocessing the img according to VGG19 requirements
   img = tf.keras.applications.vgg19.preprocess_input(img)
-
   return img2show, img
 
 
@@ -190,7 +191,6 @@ def totalLoss(model, lossWeights, generateImage, contentFeatures, styleFeatures)
   # Computing Style Cost for every layer
   for generateStyle, combinationStyle in zip(styleFeatures, styleGenerateFeatures):
     styleCostValue += styleWeightPerLayer * styleCost(combinationStyle[0], generateStyle)
-  
 
   # Assigning the weights
   contentCostValue *= contentWeight
@@ -198,7 +198,6 @@ def totalLoss(model, lossWeights, generateImage, contentFeatures, styleFeatures)
 
   # Computing the Total Loss
   totalLossValue = contentCostValue + styleCostValue
-
   return totalLossValue, contentCostValue, styleCostValue
 
 
@@ -207,7 +206,6 @@ def computeGrads(config):
 	  allLoss = totalLoss(**config)
 
 	loss = allLoss[0]
-
 	return tape.gradient(loss, config['generateImage']), allLoss
 
 
@@ -252,7 +250,8 @@ def runStyleTransfer(contentPath,
                      SAVE_EVERY     = 0,
                      contentWeight  = 1e3,
                      styleWeight    = 1e-2,
-                     output_dirName = None):
+                     output_dirName = None,
+                     save_stats     = False):
     
   # Importing the Model
   model = getModel()
@@ -294,23 +293,22 @@ def runStyleTransfer(contentPath,
   
   
   normMeans = np.array([103.939, 116.779, 123.68])
-  minVals = -normMeans
-  maxVals = 255 - normMeans  
+  minVals   = -normMeans
+  maxVals   = 255 - normMeans  
 
   # Creating Logs to use for Plotting Later
   # global contentCostLog, styleCostLog, totalCostLog 
   contentCostLog, styleCostLog, totalCostLog = [], [], []
 
-
   if output_dirName:
-    PATH = os.path.join(os.curdir, 'outputs', output_dirName)
+    PATH = os.path.join(os.curdir, output_dirName)
     if not os.path.isdir(PATH):
       os.mkdir(PATH)
     os.chdir(PATH)
 
-  for iter in tqdm(range(iterations), leave=False):
 
-    # Computing the Grads and Loss
+  for iter in tqdm(range(iterations), leave=False):
+  # for iter in range(iterations):
     grads, allLoss = computeGrads(config)
 
     # Extracting different kinds of Losses
@@ -339,13 +337,13 @@ def runStyleTransfer(contentPath,
         new = deprocessImage(generateImage.numpy())
         cv2.imwrite(f'generateImage_{iter+1}.jpg', cv2.cvtColor(new, cv2.COLOR_BGR2RGB))
 
-    
-  
   bestImage = deprocessImage(generateImage.numpy())
-  cv2.imwrite(f'FINAL_OUTPUT.jpg', cv2.cvtColor(bestImage, cv2.COLOR_BGR2RGB))
-  # Saving the numpy Arrays to plot later
-  np.save('contentLoss.npy', contentCostLog)
-  np.save('styleLoss.npy', styleCostLog)
-  np.save('totalCostLoss.npy', totalCostLog)
+  cv2.imwrite(f'output.jpg', cv2.cvtColor(bestImage, cv2.COLOR_BGR2RGB))
+  
+  if save_stats:
+    # Saving the numpy Arrays to plot later
+    np.save('contentLoss.npy', contentCostLog)
+    np.save('styleLoss.npy', styleCostLog)
+    np.save('totalCostLoss.npy', totalCostLog)
 
   return bestImage, (contentCostLog, styleCostLog, totalCostLog)
diff --git a/application.py b/application.py
new file mode 100644
index 0000000..70cd6e8
--- /dev/null
+++ b/application.py
@@ -0,0 +1,88 @@
+from flask import Flask, render_template, request, redirect
+import numpy as np
+import joblib
+import os, time, random
+from PIL import Image
+import numpy as np
+
+from __helpers__ import *
+
+
+app = Flask(__name__)
+
+# Creating a user_file folder if not present in the working dir
+if 'temp_user_files' not in os.listdir():
+	os.mkdir('temp_user_files')
+# temp_user_file : It'll contain the images uploaded by the user temporarily
+
+app.config["IMAGE_UPLOADS"] = os.path.join(os.getcwd(), 'temp_user_files')
+home_dir = os.getcwd()
+
+# To DISABLE Caching
+@app.after_request
+def add_header(r):
+    """
+    Add headers to both force latest IE rendering engine or Chrome Frame,
+    and also to cache the rendered page for 10 minutes.
+    """
+    r.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
+    r.headers["Pragma"] = "no-cache"
+    r.headers["Expires"] = "0"
+    r.headers['Cache-Control'] = 'public, max-age=0'
+    return r
+
+# Defining the Home page
+@app.route('/', methods=['GET', 'POST'])
+def home():
+	return render_template('index.html')
+
+
+# Defining the Prediction page
+@app.route('/output', methods=['GET', 'POST'])
+def output():
+
+	if request.method == 'POST':
+		if request.form['submit'] == 'Do the magic!':
+
+	# 		# Reading the POST request
+			content_img = request.files['img1name']
+			style_img   = request.files['img2name']
+			iterations  = int(request.form['iterations'])
+			print('Iterations :',iterations)
+
+	# 		# Saving the Image file in local env
+			content_path = os.path.join(app.config["IMAGE_UPLOADS"], 'content_img.jpg')
+			style_path   = os.path.join(app.config["IMAGE_UPLOADS"], 'style_img.jpg')
+			content_img.save(content_path)
+			style_img.save(style_path)
+
+	# 		# Running Style Transfer
+			print('[INFO] Style Transfer Intializing..')
+			generatedImage, losses = runStyleTransfer(content_path,
+													style_path,
+													iterations     = iterations,
+													SAVE_EVERY     = 0,
+													contentWeight  = 1,
+													styleWeight    = 0.8,
+													output_dirName = 'static',
+													save_stats     = False)
+
+
+			# Deleting the file from the database
+			os.remove(content_path)
+			os.remove(style_path)
+			
+			# Switching back to the main dir because Current dir is changed internally
+			os.chdir(home_dir)
+			print('[INFO] Output generated.')
+			return render_template('output.html')
+	
+		print('[INFO] Rendering Output')
+		return render_template('index.html')
+		
+
+
+if __name__ == '__main__':
+	app.run(port=5555, debug=True)
+
+# int(random.random()*10000)
\ No newline at end of file
diff --git a/main_colab_notebook.ipynb b/main_colab_notebook.ipynb
deleted file mode 100644
index fb42864..0000000
--- a/main_colab_notebook.ipynb
+++ /dev/null
@@ -1,756 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "main - NST",
-      "provenance": [],
-      "collapsed_sections": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/ParthikB/Neural-Style-Transfer/blob/master/main_colab_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "NYb4w64K_a7r",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Uncomment the following code to mount the google drive\n",
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "roKFsgZS_VmJ",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "%tensorflow_version 1.15.0 # Defining which Tf version to use\n",
-        "import tensorflow as tf\n",
-        "import keras.backend as K\n",
-        "from keras.applications.vgg19 import VGG19, preprocess_input\n",
-        "from tensorflow.python.keras import models\n",
-        "from keras.models import Model\n",
-        "from keras.preprocessing import image\n",
-        "\n",
-        "from PIL import Image\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "import time\n",
-        "import cv2\n",
-        "import os\n",
-        "import urllib\n",
-        "import statistics\n",
-        "from tqdm import tqdm_notebook as tqdm"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "f76I4l_B_b5M",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Enabling Eager Execution\n",
-        "tf.enable_eager_execution()\n",
-        "print(\"Eager Execution Initialized:\",tf.executing_eagerly())"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0lOW4Vh9ktyY",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Helper Functions"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RYmdVNcLCaCD",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Selecting the Feature Layers needed"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FUg3eIQDBgnG",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Defining the Feature Layers we need respectively\n",
-        "styleLayers = ['block1_conv2', \n",
-        "               'block2_conv2', \n",
-        "               'block3_conv3', \n",
-        "               'block4_conv3', \n",
-        "               'block5_conv3']\n",
-        "\n",
-        "contentLayer = ['block3_conv2']\n",
-        "\n",
-        "\n",
-        "numContentLayers = len(contentLayer) # Number of Content Layers\n",
-        "numStyleLayers   = len(styleLayers)  # Number of Style Layers"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "VyrVfwX-CeKw",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Defining Function to import model (VGG19)\n",
-        "def getModel():\n",
-        "\n",
-        "  # Loading Model from tf\n",
-        "  model = tf.keras.applications.vgg19.VGG19(include_top=False, weights='imagenet')\n",
-        "  model.trainable = False # Freezing to parameters\n",
-        "\n",
-        "  # Features of the Respective Layers\n",
-        "  contentFeatures = [model.get_layer(name).output for name in contentLayer]\n",
-        "  styleFeatures   = [model.get_layer(name).output for name in styleLayers]\n",
-        "  \n",
-        "  modelOutput = contentFeatures + styleFeatures\n",
-        "\n",
-        "  return models.Model(model.input, modelOutput)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "GLOL1qbSDkDY",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Defining GRAM MATRIX\n",
-        "def gram(x):\n",
-        "\n",
-        "  # number of channels\n",
-        "  channels = int(x.shape[-1])\n",
-        "  \n",
-        "  # reshaping to channel first\n",
-        "  a = tf.reshape(x, [-1, channels])\n",
-        "  n = tf.shape(a)[0]\n",
-        "  \n",
-        "  # gram matrix\n",
-        "  gram = tf.matmul(a, a, transpose_a=True)\n",
-        "  \n",
-        "  return gram / tf.cast(n, tf.float32)\n",
-        "\n",
-        "\n",
-        "# Defining CONTENT COST\n",
-        "def contentCost(contentFeatures, generateFeatures):\n",
-        "  return tf.reduce_mean(tf.square(contentFeatures-generateFeatures))\n",
-        "\n",
-        "\n",
-        "# Defining STYLE COST\n",
-        "def styleCost(styleFeatures, generateFeatures):\n",
-        "  styleGram = gram(styleFeatures)\n",
-        "  return tf.reduce_mean(tf.square(styleGram - generateFeatures))"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_OHnNdN10-PE",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Image Manipulations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Vry1Gt1mEcnk",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def getFeatures(content, style, model):\n",
-        "  \n",
-        "  # Defining the respective outputs from our model\n",
-        "  contentOutputs = model(content)\n",
-        "  styleOutputs = model(style)\n",
-        "  \n",
-        "  # Extracting out the different features from the model output\n",
-        "  contentFeatures = [contentFeature[0] for contentFeature in contentOutputs[numStyleLayers:]]\n",
-        "  styleFeatures = [styleFeature[0] for styleFeature in styleOutputs[:numStyleLayers]]\n",
-        "\n",
-        "  return contentFeatures, styleFeatures\n",
-        "\n",
-        "\n",
-        "def loadImage(path_to_img):\n",
-        "  max_dim = 512\n",
-        "  if type(path_to_img) == str:\n",
-        "    img2show = Image.open(path_to_img)\n",
-        "  else:\n",
-        "    img2show = path_to_img\n",
-        "\n",
-        "  # long = max(img2show.size)\n",
-        "  long = (img2show.size)\n",
-        "\n",
-        "  scale = max_dim/long\n",
-        "  img = img2show.resize((round(img2show.size[0]*scale), round(img2show.size[1]*scale)), Image.ANTIALIAS)\n",
-        "  \n",
-        "  img = image.img_to_array(img)\n",
-        "  \n",
-        "  # We need to broadcast the image array such that it has a batch dimension \n",
-        "  img = np.expand_dims(img2show, axis=0)\n",
-        "  return img2show, img\n",
-        " \n",
-        "\n",
-        "def urlToImage(url):\n",
-        "  resp = urllib.request.urlopen(url)\n",
-        "  img = np.asarray(bytearray(resp.read()), dtype='uint8')\n",
-        "  img = cv2.imdecode(img, cv2.IMREAD_COLOR)\n",
-        "  img2show = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
-        "\n",
-        "  img = np.expand_dims(img2show, axis=0)\n",
-        "  return img2show, img\n",
-        " \n",
-        "\n",
-        "def inputImageAndPreprocess(path):\n",
-        "  # Loading the image and reshaping it according to VGG19 requirements.\n",
-        "  if path[:4]=='http':\n",
-        "    # print(\"Loading Image from Internet...\")\n",
-        "    img2show, img = urlToImage(path)\n",
-        "  else:\n",
-        "    # print(\"Loading Image from Local...\")\n",
-        "    img2show, img = loadImage(path)\n",
-        "  \n",
-        "  # Preprocessing the img according to VGG19 requirements\n",
-        "  img = tf.keras.applications.vgg19.preprocess_input(img)\n",
-        "\n",
-        "  return img2show, img\n",
-        "\n",
-        "\n",
-        "# Deprocessing Image to save locally\n",
-        "def deprocessImage(processed_img):\n",
-        "  x = processed_img.copy()\n",
-        "  if len(x.shape) == 4:\n",
-        "    x = np.squeeze(x, 0)\n",
-        "  assert len(x.shape) == 3, (\"Input to deprocess image must be an image of \"\n",
-        "                             \"dimension [1, height, width, channel] or [height, width, channel]\")\n",
-        "  if len(x.shape) != 3:\n",
-        "    raise ValueError(\"Invalid input to deprocessing image\")\n",
-        "  \n",
-        "  # perform the inverse of the preprocessiing step\n",
-        "  x[:, :, 0] += 103.939\n",
-        "  x[:, :, 1] += 116.779\n",
-        "  x[:, :, 2] += 123.68\n",
-        "  x = x[:, :, ::-1]\n",
-        "\n",
-        "  x = np.clip(x, 0, 255).astype('uint8')\n",
-        "  return x"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "G--g9kADOCuI",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Computing the total Loss"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "U3bx7mzlonff",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def totalLoss(model, lossWeights, generateImage, contentFeatures, styleFeatures):\n",
-        "\n",
-        "  # Extracting the respective weights\n",
-        "  contentWeight, styleWeight = lossWeights\n",
-        "\n",
-        "  # Extracting the generate image features from the model\n",
-        "  modelOutputs = model(generateImage)\n",
-        "\n",
-        "  # Splitting the generate Features into different categories\n",
-        "  contentGenerateFeatures = modelOutputs[numStyleLayers:]\n",
-        "  styleGenerateFeatures   = modelOutputs[:numStyleLayers]\n",
-        "\n",
-        "  # Initializing all costs with 0\n",
-        "  contentCostValue, styleCostValue = 0, 0\n",
-        "\n",
-        "  # Defining partial weights\n",
-        "  contentWeightPerLayer = 1.0 / float(numContentLayers)\n",
-        "  styleWeightPerLayer = 1.0 / float(numStyleLayers)\n",
-        "\n",
-        "  # Computing Content Cost\n",
-        "  for generateContent, combinationContent in zip(contentFeatures, contentGenerateFeatures):\n",
-        "    contentCostValue += contentWeightPerLayer * contentCost(combinationContent[0], generateContent)\n",
-        "\n",
-        "  # Computing Style Cost for every layer\n",
-        "  for generateStyle, combinationStyle in zip(styleFeatures, styleGenerateFeatures):\n",
-        "    styleCostValue += styleWeightPerLayer * styleCost(combinationStyle[0], generateStyle)\n",
-        "  \n",
-        "\n",
-        "  # Assigning the weights\n",
-        "  contentCostValue *= contentWeight\n",
-        "  styleCostValue *= styleWeight\n",
-        "\n",
-        "  # Computing the Total Loss\n",
-        "  totalLossValue = contentCostValue + styleCostValue\n",
-        "\n",
-        "  return totalLossValue, contentCostValue, styleCostValue"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h5m_rbo5OGnU",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Computing the Gradient Descent"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "QkuJHoGOtQkf",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def computeGrads(config):\n",
-        "  with tf.GradientTape() as tape:\n",
-        "    allLoss = totalLoss(**config)\n",
-        "\n",
-        "  loss = allLoss[0]\n",
-        "\n",
-        "  return tape.gradient(loss, config['generateImage']), allLoss"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "SBBVPtneLdxK",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Time to get it all together"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DWMyORWOuPhN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Defining the MAIN TRAINING FUNCTION\n",
-        "def runStyleTransfer(contentPath,\n",
-        "                     stylePath,\n",
-        "                     iterations     = 1000,\n",
-        "                     SAVE_EVERY     = 0,\n",
-        "                     contentWeight  = 1e3,\n",
-        "                     styleWeight    = 1e-2,\n",
-        "                     output_dirName = None):\n",
-        "    \n",
-        "  # Importing the Model\n",
-        "  model = getModel()\n",
-        "  for layer in model.layers:\n",
-        "    layer.trainable = False\n",
-        "\n",
-        "  if type(contentPath) == str:\n",
-        "    _ , contentImage = inputImageAndPreprocess(contentPath)\n",
-        "  else:\n",
-        "    contentImage = contentPath\n",
-        "    \n",
-        "  _ ,     styleImage = inputImageAndPreprocess(stylePath)\n",
-        "  \n",
-        "  # Extracting out the respective features from the model\n",
-        "  contentFeatures, styleFeatures = getFeatures(contentImage, styleImage, model)\n",
-        "  styleFeatures = [gram(styleFeature) for styleFeature in styleFeatures]\n",
-        "\n",
-        "  # Creating the Generate Image\n",
-        "  generateImage = contentImage\n",
-        "  generateImage = tf.Variable(generateImage, dtype=tf.float32)\n",
-        "\n",
-        "  # Defining the Adam Optimizer\n",
-        "  optimizer = tf.train.AdamOptimizer(learning_rate=5, epsilon=1e-3)\n",
-        "\n",
-        "  # Storing the best Image and Loss\n",
-        "  bestLoss, bestImage = float('inf'), None\n",
-        "\n",
-        "  # Zipping the Weights\n",
-        "  lossWeights = (contentWeight, styleWeight)\n",
-        "  \n",
-        "  # Defining the Config File\n",
-        "  config = {\n",
-        "    'model': model,\n",
-        "    'lossWeights': lossWeights,\n",
-        "    'generateImage': generateImage,\n",
-        "    'contentFeatures': contentFeatures,\n",
-        "    'styleFeatures': styleFeatures\n",
-        "    }\n",
-        "  \n",
-        "  \n",
-        "  normMeans = np.array([103.939, 116.779, 123.68])\n",
-        "  minVals = -normMeans\n",
-        "  maxVals = 255 - normMeans  \n",
-        "\n",
-        "  # Creating Logs to use for Plotting Later\n",
-        "  global contentCostLog, styleCostLog, totalCostLog \n",
-        "  contentCostLog, styleCostLog, totalCostLog = [], [], []\n",
-        "\n",
-        "\n",
-        "  if output_dirName:\n",
-        "    PATH = f'/content/drive/My Drive/Colab Notebooks/Neural Style Transfer/output/vids/{output_dirName}'\n",
-        "    if not os.path.isdir(PATH):\n",
-        "      os.mkdir(PATH)\n",
-        "    os.chdir(PATH)\n",
-        "\n",
-        "  for iter in tqdm(range(iterations), leave=False):\n",
-        "\n",
-        "    # Computing the Grads and Loss\n",
-        "    grads, allLoss = computeGrads(config)\n",
-        "\n",
-        "    # Extracting different kinds of Losses\n",
-        "    loss, contentLoss, styleLoss = allLoss\n",
-        "    \n",
-        "    # Saving the respective losses in respective lists for plotting\n",
-        "    contentCostLog.append(contentLoss)\n",
-        "    styleCostLog.append(styleLoss)\n",
-        "    totalCostLog.append(loss)\n",
-        "\n",
-        "    # Applying gradients to Generate Image\n",
-        "    optimizer.apply_gradients([(grads, generateImage)])\n",
-        "\n",
-        "    # Clipping the values of Generate Image from (-255, 255)\n",
-        "    clipped = tf.clip_by_value(generateImage, minVals, maxVals)\n",
-        "    generateImage.assign(clipped)\n",
-        "\n",
-        "    # Updating the Best Image and Loss\n",
-        "    if loss < bestLoss:\n",
-        "      bestLoss = loss\n",
-        "      bestImage = generateImage.numpy()\n",
-        "\n",
-        "    # Saving the Generate Image\n",
-        "    if SAVE_EVERY:\n",
-        "      if iter % SAVE_EVERY == 0:\n",
-        "        new = cv2.cvtColor(deprocessImage(generateImage.numpy()), cv2.COLOR_RGB2BGR)\n",
-        "        cv2.imwrite(f'generateImage_{iter+1}.jpg', new)\n",
-        "\n",
-        "    \n",
-        "  if not SAVE_EVERY:\n",
-        "    bestImage = deprocessImage(generateImage.numpy())\n",
-        "  else:\n",
-        "    # Saving the numpy Arrays to plot later\n",
-        "    np.save('contentLoss.npy', contentCostLog)\n",
-        "    np.save('styleLoss.npy', styleCostLog)\n",
-        "    np.save('totalCostLoss.npy', totalCostLog)\n",
-        "\n",
-        "  return bestImage, bestLoss, output_dirName\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hFMQ6znrMY3y",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Visualizing our Content And Style Images"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "BpXligxdVBFZ",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Style Templates:\n",
-        "wave = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ8LOCDfTvcJ_V4fBdtL3R_oQn7D9P96PPzJFCksdWeKHHhyfUZ'\n",
-        "seated_nude = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ0ip7KMW5XB_qhU3cwBDDd1fjlogHfgOxw9gnVq2CqZdLwHgY3'\n",
-        "shinchan = 'https://pbs.twimg.com/profile_images/452516792426975232/rOQPTVq4_400x400.png'\n",
-        "\n",
-        "# PATH = '/content/drive/My Drive/Colab Notebooks/Neural Style Transfer/vids/surf/'\n",
-        "\n",
-        "# Enter the path of the respective Images\n",
-        "contentImagePath = wave\n",
-        "styleImagePath   = seated_nude\n",
-        "\n",
-        "content, _ = inputImageAndPreprocess(contentImagePath)\n",
-        "style, _   = inputImageAndPreprocess(styleImagePath)\n",
-        "\n",
-        "plt.figure(figsize=(10, 10))\n",
-        "\n",
-        "plt.subplot(1, 2, 1)\n",
-        "plt.imshow(content)\n",
-        "plt.title('Content Image')\n",
-        "\n",
-        "plt.subplot(1, 2, 2)\n",
-        "plt.imshow(style)\n",
-        "plt.title('Style Image')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "117gi_T-MneQ",
-        "colab_type": "text"
-      },
-      "source": [
-        "# **Training**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "wPGdWaG43zMD",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "ITERATIONS = 1\n",
-        "DIR_NAME   = 'seated_nude'\n",
-        "\n",
-        "\n",
-        "bestImage, bestLoss, output_dirName = runStyleTransfer(contentImagePath,\n",
-        "                                                      styleImagePath,\n",
-        "                                                      iterations=ITERATIONS,\n",
-        "                                                      SAVE_EVERY = 0,\n",
-        "                                                      contentWeight = 1,\n",
-        "                                                      styleWeight= 0.8,\n",
-        "                                                      output_dirName = None)\n",
-        "\n",
-        "# Output Cleared for Fairness"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XzkI-DB6M5Wj",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Visualizing the Best Image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "id": "ywRuBn7fHpkP",
-        "colab": {}
-      },
-      "source": [
-        "plt.figure(figsize=(10,10))\n",
-        "plt.imshow(bestImage)\n",
-        "plt.title('Generated Image')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4BIqrewkNBxJ",
-        "colab_type": "text"
-      },
-      "source": [
-        "#### Plotting the Cost Functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "QpxrPjPH-AJ7",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "plt.figure(figsize=(23, 8))\n",
-        "plt.plot(totalCostLog,   linewidth=3, label='total loss')\n",
-        "plt.plot(styleCostLog,   linewidth=1, label='style loss')\n",
-        "plt.plot(contentCostLog, linewidth=2, label='content loss')\n",
-        "# plt.plot(learning_curve_tv, linewidth=2, label='total variation loss')\n",
-        "plt.title(\"Learning curve\")\n",
-        "plt.ylabel(\"error\")\n",
-        "plt.xlabel(\"epoch\")\n",
-        "plt.yscale(\"log\")\n",
-        "plt.legend()\n",
-        "plt.grid()\n",
-        "plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f12hKz_q9LJa",
-        "colab_type": "text"
-      },
-      "source": [
-        "#Video Conversion"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "TsD-NPZavu9v",
-        "colab_type": "code",
-        "cellView": "form",
-        "colab": {}
-      },
-      "source": [
-        "#@title Parameters:\n",
-        "Video_path = \"/content/drive/My Drive/Colab Notebooks/Neural Style Transfer/vids/surf.mp4\" #@param {type:\"string\"}\n",
-        "\n",
-        "style_path = \"https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ8LOCDfTvcJ_V4fBdtL3R_oQn7D9P96PPzJFCksdWeKHHhyfUZ\" #@param {type:\"string\"}\n",
-        "\n",
-        "\n",
-        "fps_quality = \"Just show me the thing already\" #@param [\"Super Duper\", \"Fancier than Average\", \"Ya ok Whatever\", \"Just show me the thing already\"]\n",
-        "\n",
-        "style_quality = 100 #@param {type:\"slider\", min:100, max:1000, step:100}\n",
-        "\n",
-        "if fps_quality == 'Super Duper':\n",
-        "  skip_frame_every = 1\n",
-        "elif fps_quality == 'Fancier than Average':\n",
-        "  skip_frame_every = 3\n",
-        "elif fps_quality == 'Ya ok Whatever':\n",
-        "  skip_frame_every = 6\n",
-        "else:\n",
-        "  skip_frame_every = 10"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "_PMZLRMvT5V8",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def extract_frames_out_of_the_video(vid_path):\n",
-        "  cam = cv2.VideoCapture(vid_path)\n",
-        "\n",
-        "  currentframe = 1\n",
-        "  frames = []\n",
-        "\n",
-        "  while(True):   \n",
-        "      # reading from frame \n",
-        "      ret, frame = cam.read() \n",
-        "      \n",
-        "      if ret:\n",
-        "          frame = np.expand_dims(frame, axis=0)\n",
-        "          frames.append(frame.astype('float32'))\n",
-        "          print(currentframe, end='\\r') \n",
-        "          currentframe += 1\n",
-        "      else: \n",
-        "          break\n",
-        "  print('Frames generated..!')\n",
-        "  return frames"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5KbvxNOzx3sf",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "VID_PATH   = '/content/drive/My Drive/Colab Notebooks/Neural Style Transfer/vids/surf.mp4'\n",
-        "OUTPUT_DIR = '/content/drive/My Drive/Colab Notebooks/Neural Style Transfer/output/vids/'\n",
-        "OUTPUT_FILE_NAME = 'trying_aivayy'\n",
-        "\n",
-        "generated_frames = []\n",
-        "vid_frames = extract_frames_out_of_the_video(VID_PATH)\n",
-        "\n",
-        "for frame_number in tqdm(range(0, len(vid_frames), skip_frame_every)):\n",
-        "\n",
-        "  content = vid_frames[frame_number]\n",
-        "\n",
-        "  bestImage, bestLoss, output_dirName = runStyleTransfer(content,\n",
-        "                                                        style_path,\n",
-        "                                                        iterations=style_quality,\n",
-        "                                                        contentWeight = 1,\n",
-        "                                                        styleWeight= 0.8)\n",
-        "  generated_frames.append(bestImage)\n",
-        "  height, width, channels = bestImage.shape\n",
-        "\n",
-        "\n",
-        "print('Developing the Video')\n",
-        "out = cv2.VideoWriter(OUTPUT_DIR + OUTPUT_FILE_NAME + '.avi', cv2.VideoWriter_fourcc(*'DIVX'), 15, (width,height))\n",
-        "for i in (range(len(generated_frames))):\n",
-        "    out.write(generated_frames[i])\n",
-        "out.release()\n",
-        "\n",
-        "print('Video Converted and Saved Succesfully!')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file
diff --git a/main_image.py b/main_image.py
deleted file mode 100644
index 6a41e41..0000000
--- a/main_image.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import matplotlib.pyplot as plt
-
-from __helpers__ import *
-
-###################################################################
-# Few templates
-wave        = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ8LOCDfTvcJ_V4fBdtL3R_oQn7D9P96PPzJFCksdWeKHHhyfUZ'
-seated_nude = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ0ip7KMW5XB_qhU3cwBDDd1fjlogHfgOxw9gnVq2CqZdLwHgY3'
-shinchan    = 'https://pbs.twimg.com/profile_images/452516792426975232/rOQPTVq4_400x400.png'
-taj_mahal   = 'https://thumbs-prod.si-cdn.com/Abm-e-V_cqdIqYDo_cXApagw8zI=/800x600/filters:no_upscale():focal(1471x1061:1472x1062)/https://public-media.si-cdn.com/filer/b6/30/b630b48b-7344-4661-9264-186b70531bdc/istock-478831658.jpg'
-###################################################################
-
-STYLE_QUALITY = 500           # Iterations
-DIR_NAME      = 'seated_nude' # Set this to None if outputs are not required to be saved locally.
-
-contentImagePath = taj_mahal
-styleImagePath   = wave
-
-generatedImage, losses = runStyleTransfer(contentImagePath,
-                                          styleImagePath,
-                                          iterations     = STYLE_QUALITY,
-                                          SAVE_EVERY     = 0,
-                                          contentWeight  = 1,
-                                          styleWeight    = 0.8,
-                                          output_dirName = DIR_NAME)
-
-
-contentCostLog, styleCostLog, totalCostLog = losses
-
-print('Plotting...')
-
-fig = plt.figure(constrained_layout=False, figsize=(5, 5))
-gs1 = fig.add_gridspec(nrows=2, ncols=3, left=0.005, right=0.48, wspace=0.05, hspace=0.01)
-
-fig.add_subplot(gs1[:-1, :])
-plt.imshow(generatedImage)
-plt.axis('off')
-plt.title('Generated Image')
-
-fig.add_subplot(gs1[-1, :-1])
-plt.imshow(inputImageAndPreprocess(contentImagePath)[0])
-plt.axis('off')
-plt.title('Content Image')
-
-fig.add_subplot(gs1[-1, -1])
-plt.imshow(inputImageAndPreprocess(styleImagePath)[0])
-plt.axis('off')
-plt.title('Style Image')
-
-
-
-plt.figure(figsize=(10, 4))
-plt.plot(totalCostLog,   linewidth=3, label='total loss')
-plt.plot(styleCostLog,   linewidth=1, label='style loss')
-plt.plot(contentCostLog, linewidth=2, label='content loss')
-plt.title("Learning curve")
-plt.ylabel("error")
-plt.xlabel("epoch")
-plt.yscale("log")
-plt.legend()
-plt.grid()
-plt.show()
-
-print('All done yay!')
diff --git a/main_video.py b/main_video.py
deleted file mode 100644
index 7322b62..0000000
--- a/main_video.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from __helpers__ import *
-
-
-VIDEO_PATH       = ''
-STYLE_IMAGE_PATH = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ8LOCDfTvcJ_V4fBdtL3R_oQn7D9P96PPzJFCksdWeKHHhyfUZ'
-
-FPS_QUALITY      = 'low' 
-'''
-high   > original fps > slowest
-medium > 
-low    > bad fps      > fastest
-''' 
-
-STYLE_QUALITY    = 100 # Any integer in range (100, 1000)
-'''
-The more the STYLE_QUALITY, the more better every frame is stylized.
-BUT, will take more time.
-'''
-
-# Final Output video name
-OUTPUT_FILE_NAME = 'trying_aivayy'
-
-
-
-
-vid_frames = extract_frames_out_of_the_video(VIDEO_PATH)
-generated_frames = []
-
-for frame_number in tqdm(range(0, len(vid_frames), skip_frame_every(FPS_QUALITY))):
-  content = vid_frames[frame_number]
-  generated_image, _ = runStyleTransfer(content,
-                                        STYLE_IMAGE_PATH,
-                                        iterations=STYLE_QUALITY)
-  generated_frames.append(generated_image)
-
-
-height, width, channels = generated_frames[0].shape
-
-PATH = os.path.join(os.curdir, 'outputs')
-if not os.path.isdir(PATH):
-  os.mkdir(PATH)
-os.chdir(PATH)
-
-print('Developing the Video...')
-out = cv2.VideoWriter(OUTPUT_FILE_NAME + '.avi', cv2.VideoWriter_fourcc(*'DIVX'), 15, (width,height))
-for i in (range(len(generated_frames))):
-    out.write(generated_frames[i])
-out.release()
-
-print('Video Converted and Saved Succesfully!')
diff --git a/neural_style_transfer_torch.ipynb b/neural_style_transfer_torch.ipynb
deleted file mode 100644
index 7adfdf9..0000000
--- a/neural_style_transfer_torch.ipynb
+++ /dev/null
@@ -1,469 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "neural style transfer - torch",
-      "provenance": [],
-      "collapsed_sections": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/ParthikB/Neural-Style-Transfer/blob/master/neural_style_transfer_torch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "jpE-RBdyWhlu",
-        "colab_type": "code",
-        "outputId": "79b8337f-802a-4a90-dac2-c003a580caab",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "import torch\n",
-        "import torchvision.models as models\n",
-        "import torch.nn as nn\n",
-        "import torch.nn.functional as F\n",
-        "import torch.optim as optim\n",
-        "\n",
-        "import urllib\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "import cv2\n",
-        "# from google.colab.patches import cv2_imshow\n",
-        "\n",
-        "if torch.cuda.is_available():\n",
-        "  device = torch.device('cuda:0')\n",
-        "  running = 'GPU'\n",
-        "else:\n",
-        "  device = torch.device('cpu')\n",
-        "  running = 'CPU'\n",
-        "print(f'Running on : {running}')"
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Running on : GPU\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "kaITuLhhF9e1",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "'''\n",
-        "THIS IS WHAT I HAVE TO DO:\n",
-        "\n",
-        "# 1) Get the VGG19 model.\n",
-        "# 2) Define the Content, Style and Total Loss Function, and also Gram Matrix.\n",
-        "# 3) Define a method to extract features from 'x' layer of the Network.\n",
-        "4) Create the Training Loop which will minimize the Total Loss function and update the values of the image.\n",
-        "\n",
-        "'''"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EGoNFVofWrdK",
-        "colab_type": "code",
-        "outputId": "688aaa99-cb0b-4eb6-bab6-80d4090c6a78",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 286
-        }
-      },
-      "source": [
-        "def urlToImage(url):\n",
-        "  resp = urllib.request.urlopen(url)\n",
-        "  img = np.asarray(bytearray(resp.read()), dtype='uint8')\n",
-        "  img = cv2.imdecode(img, cv2.IMREAD_COLOR)\n",
-        "  img2show = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
-        "\n",
-        "  img = np.expand_dims(img2show, axis=0)\n",
-        "  return img2show, img\n",
-        "\n",
-        "\n",
-        "\n",
-        "url = 'https://pbs.twimg.com/profile_images/452516792426975232/rOQPTVq4_400x400.png'\n",
-        "\n",
-        "img2show, img = urlToImage(url)\n",
-        "\n",
-        "# Reshaping to remove the batch size dimension so that can be plotted\n",
-        "img2show = img2show.reshape(400, 400, -1)\n",
-        "\n",
-        "# Reshaping to put the channels first as the torch network wants\n",
-        "img = np.rollaxis(img, 3, 1)\n",
-        "img = torch.tensor(img).float()\n",
-        "img.shape\n",
-        "\n",
-        "# # A small program to find the correct values for np.rollaxis \n",
-        "# for i in range(4):\n",
-        "#   for j in range(4):\n",
-        "#     new = np.rollaxis(img, i, j)\n",
-        "#     if new.shape == (1, 3, 400, 400):\n",
-        "#       print(i, j)\n",
-        "#       break\n",
-        "\n",
-        "plt.imshow(img2show)\n",
-        "print(img2show.shape)"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "(400, 400, 3)\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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Mh0MMy0Sh2N67htdo4noNPM/BME0c+8OJKViTQIWrN5ZSiul8wXAwZrGYk8QRGsXDb+7y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\nvCTNC8ZRxnlUsH8wIo0W2HmMROO5NhvdwPScQ5m/NTD0XckZkAKhJZb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J6rnWRoc0k2hE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\nsMJ7jhUJrLDCe44VCaywwnuO/x/hQq8vpbQSBAAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FgAo9QtRG5xy",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Step 1 | Get Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "SN4n-p5B-8LG",
-        "colab_type": "code",
-        "outputId": "4f427f8f-2f4d-4cfc-80ac-a2d44fbd83ec",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 88
-        }
-      },
-      "source": [
-        "# Define the model\n",
-        "model = models.vgg19(pretrained=True)\n",
-        "print('Model Loaded!')"
-      ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/checkpoints/vgg19-dcbb9e9d.pth\n",
-            "100%|██████████| 548M/548M [00:03<00:00, 184MB/s]\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Model Loaded!\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a86u0RyOAe8P",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Step 2 | Define the Loss Functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "d_nHQODUHKpn",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Defining the Gram Matrix\n",
-        "def gram(x):\n",
-        "  pass # ---------------------------------------------------------------------------------------------- need to complete this\n",
-        "\n",
-        "\n",
-        "# Defining the Content Loss Function\n",
-        "def content_loss(contentFeatures, generatedFeatures):\n",
-        "  'contentFeatures, generatedFeatures --> tensors'\n",
-        "  loss = torch.mean((contentFeatures[0] - generatedFeatures[0])**2)\n",
-        "  return loss\n",
-        "\n",
-        "\n",
-        "# Defining the Style Loss Function\n",
-        "def style_loss(styleFeatures, generatedFeatures):\n",
-        "  'styleFeatures, generatedFeatures --> tensors'\n",
-        "  \n",
-        "  # Extracting the Gram matrix\n",
-        "  styleGram = gram(styleFeatures)\n",
-        "\n",
-        "  loss = torch.mean((styleFeatures - generatedFeatures)**2)\n",
-        "  return loss\n",
-        "\n",
-        "\n",
-        "# Defining the Total Loss Function\n",
-        "def total_loss(loss_weights, generateImage, content_features, style_features):\n",
-        "\n",
-        "  # Extracting the respective weights\n",
-        "  content_weight, style_weight = loss_weights\n",
-        "\n",
-        "  # Extracting the generate image features from the model\n",
-        "  generate_outputs = get_generate_features(generateImage)\n",
-        "\n",
-        "  # Splitting the generate Features into different categories\n",
-        "  generate_content_features = generate_outputs[0]\n",
-        "  generate_style_features   = generate_outputs[1:]\n",
-        "\n",
-        "  # Initializing all costs with 0\n",
-        "  content_cost_value, style_cost_value = 0, 0\n",
-        "\n",
-        "  # Defining partial weights\n",
-        "  # contentWeightPerLayer = 1.0 / float(numContentLayers)\n",
-        "  # styleWeightPerLayer = 1.0 / float(numStyleLayers)\n",
-        "\n",
-        "  # Computing Content Cost\n",
-        "  content_cost_value += content_loss(content_features, generate_content_features)\n",
-        "\n",
-        "  # Computing Style Cost for every layer\n",
-        "  # for i in range(len(style_features)):\n",
-        "  #   style_cost_value += style_loss(style_features[i], generate_style_features[i])\n",
-        "  \n",
-        "\n",
-        "  # Assigning the weights\n",
-        "  content_cost_value *= content_weight\n",
-        "  # style_cost_value   *= style_weight\n",
-        "\n",
-        "  # Computing the Total Loss\n",
-        "  # total_loss_value = content_cost_value + style_cost_value\n",
-        "  total_loss_value = content_cost_value\n",
-        "\n",
-        "  # return total_loss_value, content_cost_value, style_cost_value\n",
-        "  return total_loss_value, content_cost_value\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_Zx-6dAHBnsC",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Step 3 | Feature Extraction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "HTuFQO3x5AOA",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def get_features(input_img, type_of_feature):\n",
-        "  '''\n",
-        "  >> A function that return the features of the 'input_img'.\n",
-        "  type_of_feature --> content/style\n",
-        "  '''\n",
-        "\n",
-        "  if type_of_feature == 'content':\n",
-        "    layers = [12]\n",
-        "  else:\n",
-        "    layers = [2, 7, 14, 23, 32]\n",
-        "  \n",
-        "  outputs= []\n",
-        "  def hook(module, input, output):\n",
-        "      outputs.append(output)\n",
-        "\n",
-        "  # Creating a dictionary containing the children of the layers\n",
-        "  features = {}\n",
-        "  for idx, layer_num in enumerate(layers):\n",
-        "    features['style_' + str(idx)] = list(model.children())[0][layer_num]\n",
-        "\n",
-        "  # Hooking the network to get the output of every layer in the features dict.\n",
-        "  for i in range(len(layers)):\n",
-        "    features['style_' + str(i)].register_forward_hook(hook)\n",
-        "  \n",
-        "  # Passing the image through the network\n",
-        "  final_output_which_is_not_needed = model(input_img)\n",
-        "  \n",
-        "  # Returning the features\n",
-        "  return outputs\n",
-        "\n",
-        "\n",
-        "\n",
-        "def get_generate_features(input_img):\n",
-        "  '''\n",
-        "  >> A function that returnes the content and style features of the 'input_img'.\n",
-        "  '''\n",
-        "  # Extracting the respective features\n",
-        "  content_features = get_features(input_img, 'content')\n",
-        "  style_features   = get_features(input_img, 'style')\n",
-        "\n",
-        "  return content_features, style_features"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "NSYrNlVq_FzT",
-        "colab_type": "code",
-        "outputId": "e2cca850-e38a-4322-fa59-68210bca0eb3",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "# This is the layer of which the output is needed.\n",
-        "LAYER_NUM = 12\n",
-        "\n",
-        "x = get_layer(model, LAYER_NUM)\n",
-        "x.shape"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "torch.Size([1, 256, 100, 100])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 28
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "faS-bHuIFfIB",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Step 4 | Training Loop"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "6b4ZgHlQFfK1",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "def run_style_transfer(content_image, style_image, content_weight, style_weight, EPOCHS=1000):\n",
-        "\n",
-        "  # Loading the VGG19 model\n",
-        "  model = models.vgg19(pretrained=True)\n",
-        "  print('Model Loaded!')\n",
-        "\n",
-        "  # Extracting the content and style features\n",
-        "  content_features = get_features(content_image, 'content')\n",
-        "  style_features   = get_features(style_image  , 'style')\n",
-        "\n",
-        "  # Creating the generate Image\n",
-        "  generate_image = content_image\n",
-        "\n",
-        "  # Defining the optimizer\n",
-        "  optimizer = optim.Adam(model.parameters(), lr=5)\n",
-        "\n",
-        "  # Storing the best Image and Loss\n",
-        "  best_loss, best_image = float('inf'), None\n",
-        "\n",
-        "  # Zipping the Weights\n",
-        "  loss_weights = (content_weight, style_weight)\n",
-        "\n",
-        "  for iter in range(EPOCHS):\n",
-        "\n",
-        "    model.zero_grad()\n",
-        "\n",
-        "    # Computing the outputs and thus, Total Loss\n",
-        "    totalLoss = total_loss(loss_weights, generate_image, content_features, style_features)\n",
-        "\n",
-        "    print(totalLoss)\n",
-        "    break\n",
-        "\n",
-        "\n",
-        "  "
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "0VYpSTKiFp-H",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        },
-        "outputId": "db038fd6-f67a-497c-b9ae-231889406c69"
-      },
-      "source": [
-        "run_style_transfer(img, img, 0.7, 0.4)"
-      ],
-      "execution_count": 30,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Model Loaded!\n",
-            "(tensor(0., grad_fn=<MulBackward0>), tensor(0., grad_fn=<MulBackward0>))\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..27ba8da
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,12 @@
+certifi==2020.6.20
+click==7.1.2
+Flask==1.1.2
+itsdangerous==1.1.0
+Jinja2==2.11.2
+joblib==0.17.0
+MarkupSafe==1.1.1
+numpy==1.18.5
+Pillow==8.0.1
+tensorflow-gpu==2.2.0
+keras==2.4.3
+opencv-python==4.4.0.42
diff --git a/static/elements/back-block.gif b/static/elements/back-block.gif
new file mode 100644
index 0000000..c68e463
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diff --git a/static/elements/banner.jpg b/static/elements/banner.jpg
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diff --git a/static/elements/banner.psd b/static/elements/banner.psd
new file mode 100644
index 0000000..aa21da7
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diff --git a/static/elements/loading.gif b/static/elements/loading.gif
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index 0000000..1e2508d
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diff --git a/static/elements/output_banner.jpg b/static/elements/output_banner.jpg
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index 0000000..f8855e5
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diff --git a/static/elements/spiral.gif b/static/elements/spiral.gif
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index 0000000..fc2c927
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diff --git a/static/index.js b/static/index.js
new file mode 100644
index 0000000..070d616
--- /dev/null
+++ b/static/index.js
@@ -0,0 +1,29 @@
+// To show the image //
+
+function readURL(input, tag) {
+  if (input.files && input.files[0]) {
+    var reader = new FileReader();
+
+    reader.onload = function (e) {
+      $(tag).attr('src', e.target.result);
+    }
+
+    reader.readAsDataURL(input.files[0]); // convert to base64 string
+  }
+}
+
+
+function onButtonClick(div, div2){
+  document.getElementById(div).className="show";
+  document.getElementById(div2).className="hide";
+
+}
+
+
+$("#img1name").change(function () {
+  readURL(this, '#img1');
+});
+
+$("#img2name").change(function () {
+  readURL(this, '#img2');
+});
\ No newline at end of file
diff --git a/static/output.jpg b/static/output.jpg
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index 0000000..03b3870
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diff --git a/static/styles.css b/static/styles.css
new file mode 100644
index 0000000..6560b79
--- /dev/null
+++ b/static/styles.css
@@ -0,0 +1,226 @@
+h1 {
+  color: white;
+  text-align: center;
+  font-size: 150px;
+  font-family: "Montserrat", sans-serif;
+  margin-bottom: 40px;
+  margin-top: 10px;
+}
+
+h3 {
+  color: white;
+  text-align: center;
+  font-size: 25px;
+  font-family: "Montserrat", sans-serif;
+}
+
+label {
+  color: white;
+  font-family: "Montserrat", sans-serif;
+}
+
+body {
+  background-image: url('elements/back-block.gif');
+  background-size: 150px;
+  background-color: #222222;
+  padding: 0;
+  margin: 0;
+}
+
+
+/* Image properties */
+.imtip {
+  position: absolute;
+  bottom: 0;
+  right: 0;
+}
+
+.input_img {
+  max-width: 500px;
+  max-height: 500px;
+  border: 10px white solid;
+}
+
+/* 
+.banner{
+  max-width: 500px;
+  max-height: 500px;
+  border: none;
+} */
+
+
+input[type=submit] {
+  background-color: transparent;
+  color: white;
+  padding: 16px 0px;
+  text-align: center;
+  text-decoration: none;
+  display: inline-block;
+  font-size: 16px;
+  font-family: "Montserrat", sans-serif;
+  margin: 4px 2px;
+  transition-duration: 0.4s;
+  cursor: pointer;
+  border: 5px solid #FFC300;
+  width: 200px;
+}
+
+input[type=submit]:hover {
+  background-color: #FFC300;
+  color: black;
+}
+
+.center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+  width: 50%;
+}
+
+/* all css code for file uploader */
+
+
+.hide {
+  display: none;
+}
+
+.show {
+  display: block;
+  width: 80px;
+}
+
+#output_img {
+  width: 1000px;
+  height: auto;
+  border-image: url(http://placekitten.com/100/100) 25% round;
+  /* border: 2px solid #FFC300; */
+
+}
+
+/* .out_img {
+
+} */
+
+.file-upload {
+  display: block;
+  text-align: center;
+  font-family: "Montserrat", sans-serif;
+  font-size: 12px;
+  margin: 0 0 20px 0;
+  padding: 0;
+  width: 100%;
+  background-color: transparent;
+  transition-duration: 0.4s;
+
+}
+
+.file-upload .file-select {
+  display: block;
+  border: 2px solid #FFC300;
+  color: white;
+  cursor: pointer;
+  height: 40px;
+  width: 100%;
+  line-height: 40px;
+  text-align: center;
+  background: transparent;
+  overflow: hidden;
+  position: relative;
+}
+
+
+.file-upload .file-select .file-select-button {
+  background: transparent;
+  padding: 0 10px;
+  display: inline-block;
+  height: 40px;
+  line-height: 40px;
+}
+
+.file-upload .file-select .file-select-name {
+  line-height: 40px;
+  display: inline-block;
+  padding: 0 10px;
+}
+
+.file-upload .file-select:hover {
+  border-color: #FFC300;
+  transition: all .2s ease-in-out;
+  -moz-transition: all .2s ease-in-out;
+  -webkit-transition: all .2s ease-in-out;
+  -o-transition: all .2s ease-in-out;
+}
+
+.file-upload .file-select:hover .file-select-button {
+  background: #FFC300;
+  color: black;
+  width: 100%;
+  text-align: center;
+  transition: all .2s ease-in-out;
+  -moz-transition: all .2s ease-in-out;
+  -webkit-transition: all .2s ease-in-out;
+  -o-transition: all .2s ease-in-out;
+}
+
+.file-upload.active .file-select {
+  border-color: #3fa46a;
+  transition: all .2s ease-in-out;
+  -moz-transition: all .2s ease-in-out;
+  -webkit-transition: all .2s ease-in-out;
+  -o-transition: all .2s ease-in-out;
+}
+
+.file-upload.active .file-select .file-select-button {
+  background: #3fa46a;
+  color: #FFFFFF;
+  transition: all .2s ease-in-out;
+  -moz-transition: all .2s ease-in-out;
+  -webkit-transition: all .2s ease-in-out;
+  -o-transition: all .2s ease-in-out;
+}
+
+.file-upload .file-select input[type=file] {
+  z-index: 100;
+  cursor: pointer;
+  position: absolute;
+  height: 100%;
+  width: 100%;
+  top: 0;
+  left: 0;
+  opacity: 0;
+  filter: alpha(opacity=0);
+}
+
+.file-upload .file-select.file-select-disabled {
+  opacity: 0.65;
+}
+
+.file-upload .file-select.file-select-disabled:hover {
+  cursor: default;
+  display: block;
+  border: 2px solid #dce4ec;
+  color: #34495e;
+  cursor: pointer;
+  height: 40px;
+  line-height: 40px;
+  margin-top: 5px;
+  text-align: left;
+  background: #FFFFFF;
+  overflow: hidden;
+  position: relative;
+}
+
+.file-upload .file-select.file-select-disabled:hover .file-select-button {
+  background: #dce4ec;
+  color: #666666;
+  padding: 0 10px;
+  display: inline-block;
+  height: 40px;
+  line-height: 40px;
+}
+
+.file-upload .file-select.file-select-disabled:hover .file-select-name {
+  line-height: 40px;
+  display: inline-block;
+  padding: 0 10px;
+}
\ No newline at end of file
diff --git a/temp_user_files/don't delete.txt b/temp_user_files/don't delete.txt
new file mode 100644
index 0000000..08778d7
--- /dev/null
+++ b/temp_user_files/don't delete.txt	
@@ -0,0 +1 @@
+{\rtf1}
\ No newline at end of file
diff --git a/temp_user_files/readme.txt b/temp_user_files/readme.txt
new file mode 100644
index 0000000..7699aab
--- /dev/null
+++ b/temp_user_files/readme.txt
@@ -0,0 +1,2 @@
+User inputs will be saved here temporarily.
+DO NOT DELETE THIS FOLDER.
\ No newline at end of file
diff --git a/templates/index.html b/templates/index.html
new file mode 100644
index 0000000..3bffc25
--- /dev/null
+++ b/templates/index.html
@@ -0,0 +1,79 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<p style="text-align: center;"> </p>
+<p> </p>
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Style Transfer</title>
+    <link href="https://fonts.googleapis.com/css2?family=Montserrat" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
+    <link rel="stylesheet" type="text/css" href='{{ url_for("static", filename="styles.css") }}'>
+    <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
+
+</head>
+
+<img class='banner' style="width: auto; max-width: 100%;" src="{{url_for('static', filename='elements/banner.jpg')}}">
+<form class="forms" action="/output" enctype="multipart/form-data" method="POST">
+
+    <table style="width: 1645px; height: 243px; margin-left: auto; margin-right: auto;" cellspacing="20">
+        <tbody>
+            <tr>
+                <td style="width: 781px;">
+                    <h3 style="text-align: center;">Select CONTENT Image</h3>
+                </td>
+                <td style="width: 794px; text-align: right;">
+                    <h3 style="text-align: center;">Select STYLE Image</h3>
+                </td>
+            </tr>
+            <tr>
+                <td style="width: 50%;">
+                    <div class="file-upload">
+                        <div class="file-select">
+                            <div id="fileName" class="file-select-button">Choose File</div>
+                            <!-- <div id="noFile" class="file-select-name">No file chosen...</div> -->
+                            <input id="img1name" name="img1name" type="file" />
+                        </div>
+                    </div>
+
+                </td>
+                <td style="width: 50%;">
+                    <div class="file-upload">
+
+                        <div class="file-select">
+                            <div id="fileName" class="file-select-button">Choose File</div>
+                            <!-- <div id="noFile" class="file-select-name">No file chosen...</div> -->
+                            <input id="img2name" name="img2name" type="file" />
+                        </div>
+                    </div>
+                </td>
+            </tr>
+            <tr>
+                <td style="width: 781px; text-align: center;"><img class="input_img" id="img1" src="" /></td>
+                <td style="width: 794px; text-align: center;"><img class="input_img" id="img2" src="" /></td>
+            </tr>
+        </tbody>
+    </table>
+    <center>
+        
+        <h3> Style Quality </h3>
+        <label for="iterations">Poor (Fastest)</label>
+        <input type="range" id="iterations" name="iterations" min="2" max="102" step="10" value="20">
+        <label for="iterations">Best (Slowest)</label>
+        <!-- <input type="submit"> -->
+        
+        <br><br><br><br><br><br>
+
+        <img class='hide' id='loader' src="{{url_for('static', filename='elements/loading.gif')}}">
+        <div class="file-upload" id='submit-button'>
+            <input id="submit-button" name="submit" type="submit" value="Do the magic!"
+                onclick="onButtonClick('loader', 'submit-button')" />
+        </div>
+</form>
+</center>
+
+<script src="{{url_for('static', filename='index.js')}}"></script>
+
+</html>
\ No newline at end of file
diff --git a/templates/output.html b/templates/output.html
new file mode 100644
index 0000000..ee475ca
--- /dev/null
+++ b/templates/output.html
@@ -0,0 +1,34 @@
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Style Transfer</title>
+    <link href="https://fonts.googleapis.com/css2?family=Montserrat" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
+    <link rel="stylesheet" type="text/css" href='{{ url_for("static", filename="styles.css") }}'>
+
+</head>
+
+<h1 style="text-align: center;">Isn't this just amazing??</h1>
+
+<body>
+    <form class="forms" method="POST" action="/">
+        <p style="text-align: center;"></p>
+        <!-- <div>
+            <img id='banner' style="width: auto; 
+        max-width: 100%; 
+        position: absolute;
+        top: 47.25%;
+        left: 0%;
+        z-index: 1;" src="{{url_for('static', filename='elements/output_banner.jpg')}}"> -->
+        <div style="
+        text-align: center;">
+
+            <img id='output_img'
+                src='{{ url_for("static", filename="output.jpg") }}' />
+        </div>
+<br><br><hr><hr>
+        <div style="text-align:center;">
+            <input class="center" id="submit" name="submit" type="submit" value="I wanna do it again!" />
+        </div>
+    </form>
+</body>
\ No newline at end of file
diff --git a/templates/output_banner.jpg b/templates/output_banner.jpg
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