diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..c5d7e85
Binary files /dev/null and b/.DS_Store differ
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index b879910..c7e6962 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -38,7 +38,8 @@
 			// Add the IDs of extensions you want installed when the container is created.
 			"extensions": [
 				"ms-python.python",
-				"ms-python.vscode-pylance"
+				"ms-python.vscode-pylance",
+				"stevensona.character-count"
 			]
 		}
 	},
diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml
index ad7f05d..cb7b74a 100644
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -9,9 +9,9 @@ jobs:
       matrix:
         python-version: ["3.8", "3.9", "3.10"]
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
@@ -24,4 +24,4 @@ jobs:
         echo "PYTHONPATH=./" >> $GITHUB_ENV
     - name: Analysing the code with pylint
       run: |
-        pylint --disable=missing-docstring --indent-string='   ' $(git ls-files "*.py")
+        pylint --disable=missing-docstring --disable=no-member --indent-string='   ' $(git ls-files "*.py")
diff --git a/.gitignore b/.gitignore
index 2955a67..daead37 100644
--- a/.gitignore
+++ b/.gitignore
@@ -130,5 +130,14 @@ dmypy.json
 
 # fake data generated by the tests
 .fake
+
 # upload directory
 data/
+
+# JPEG
+*.jpg
+*.jpeg
+*.jpe
+*.jif
+*.jfif
+*.jfi
diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 0000000..f7f2287
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,5 @@
+{
+   "recommendations": [
+      "stevensona.character-count"
+   ]
+}
\ No newline at end of file
diff --git a/application/code_generator.py b/application/code_generator.py
index 15c1b81..ff5f61f 100644
--- a/application/code_generator.py
+++ b/application/code_generator.py
@@ -1,95 +1,60 @@
-from model import code_blocks
-class CodeGenerator:
-   def __init__(self, template_mapping, parse_template):
-      self.blocks = code_blocks.AllBlocks()
-      self.function_mapping = template_mapping
-      self.parse_template = parse_template
-      self.data = {}
-
+from application.code_parser import CodeParser
+class CodeGenerator(CodeParser):
    def get_data(self):
       return self.data['dataframe']
 
    def load_data(self, csv_file):
-      dataframe = self._parse_and_execute('read_csv', [csv_file])
-      self._save('dataframe', dataframe)
+      dataframe = self.parse_and_execute('read_csv', [csv_file])
+      self.save('dataframe', dataframe)
       return self.data['dataframe'].shape
 
    def describe_data(self):
-      output = self._parse_and_execute('describe_data', ['dataframe'])
+      output = self.parse_and_execute('describe_data', ['dataframe'])
       return output
 
    def get_labels(self):
-      keys = self._parse_and_execute('get_keys', ['dataframe'])
+      keys = self.parse_and_execute('get_keys', ['dataframe'])
       return keys.values.tolist()
 
    def select_y(self, output_label):
-      x_values, y_values = self._parse_and_execute('select_y', ['dataframe', output_label])
-      self._save('x_values', x_values)
-      self._save('y_values', y_values)
+      x_values, y_values = self.parse_and_execute('select_y', ['dataframe', output_label])
+      self.save('x_values', x_values)
+      self.save('y_values', y_values)
 
    def drop_x(self, input_labels):
-      dataframe = self._parse_and_execute('drop_x', ['dataframe', input_labels])
-      self._save('dataframe', dataframe)
+      dataframe = self.parse_and_execute('drop_x', ['dataframe', input_labels])
+      self.save('dataframe', dataframe)
 
    def clean_data(self):
-      dataframe = self._parse_and_execute('clean_data', ['dataframe'])
-      self._save('dataframe', dataframe)
+      dataframe = self.parse_and_execute('clean_data', ['dataframe'])
+      self.save('dataframe', dataframe)
       return self.data['dataframe'].shape
 
    def split_data(self, train_ratio = 0.8, seed = 200):
       # the ordering of x/y train/test is different here but I don't know why
-      (x_train, y_train, x_test, y_test)=self._parse_and_execute(
+      (x_train, y_train, x_test, y_test)=self.parse_and_execute(
          'split',['x_values','y_values',1-train_ratio,seed]
       )
-      self._save('x_train', x_train)
-      self._save('x_test', x_test)
-      self._save('y_train', y_train)
-      self._save('y_test', y_test)
+      self.save('x_train', x_train)
+      self.save('x_test', x_test)
+      self.save('y_train', y_train)
+      self.save('y_test', y_test)
       return self.data['x_train'].shape
 
    def train_lin_reg(self):
-      model = self._parse_and_execute('train_lin_reg', ['x_train', 'y_train'])
-      self._save('model', model)
+      model = self.parse_and_execute('train_lin_reg', ['x_train', 'y_train'])
+      self.save('model', model)
       return model
 
    def lin_reg_predict(self):
-      (train_preds, test_preds) = self._parse_and_execute(
+      (train_preds, test_preds) = self.parse_and_execute(
          'lin_reg_predict', ['model', 'x_train', 'x_test']
       )
-      self._save('train_preds', train_preds)
-      self._save('test_preds', test_preds)
+      self.save('train_preds', train_preds)
+      self.save('test_preds', test_preds)
 
    def eval_lin_reg(self):
-      error_change = self._parse_and_execute(
+      error_change = self.parse_and_execute(
          'eval_lin_reg', ['y_train', 'y_test', 'train_preds', 'test_preds']
       )
-      self._save('error_change', error_change)
-
-   def download_code(self):
-      return self.blocks.to_text()
-
-   def _create_new_block(self, comment, statements):
-      block = code_blocks.CodeBlock(comment, statements)
-      self.blocks.add_next_block(block)
-
-   def _parse_and_execute(self, template, args):
-      replaced_args = []
-      string_args = []
-      for arg in args:
-         if isinstance(arg, str) and arg in self.data:
-            replaced_args.append(self.data[arg])
-            string_args.append(arg)
-         else:
-            replaced_args.append(arg)
-            if isinstance(arg, str):
-               string_args.append('\"'+arg+'\"')
-            else:
-               string_args.append(str(arg))
-
-      (comments, code) = self.parse_template(template, string_args)
-      self._create_new_block(comments[0], code)
-      output = self.function_mapping[template](replaced_args)  # where the code is executed
-      return output
-
-   def _save(self, key, value):
-      self.data[key] = value
+      self.save('error_change', error_change)
diff --git a/application/code_parser.py b/application/code_parser.py
new file mode 100644
index 0000000..305172a
--- /dev/null
+++ b/application/code_parser.py
@@ -0,0 +1,36 @@
+from model import code_blocks
+class CodeParser:
+   def __init__(self, template_mapping, parse_template):
+      self.blocks = code_blocks.AllBlocks()
+      self.function_mapping = template_mapping
+      self.parse_template = parse_template
+      self.data = {}
+
+   def download_code(self):
+      return self.blocks.to_text()
+
+   def create_new_block(self, comment, statements):
+      block = code_blocks.CodeBlock(comment, statements)
+      self.blocks.add_next_block(block)
+
+   def parse_and_execute(self, template, args):
+      replaced_args = []
+      string_args = []
+      for arg in args:
+         if isinstance(arg, str) and arg in self.data:
+            replaced_args.append(self.data[arg])
+            string_args.append(arg)
+         else:
+            replaced_args.append(arg)
+            if isinstance(arg, str):
+               string_args.append('\"'+arg+'\"')
+            else:
+               string_args.append(str(arg))
+
+      (comments, code) = self.parse_template(template, string_args)
+      self.create_new_block(comments[0], code)
+      output = self.function_mapping[template](replaced_args)
+      return output
+
+   def save(self, key, value):
+      self.data[key] = value
diff --git a/application/image_generator.py b/application/image_generator.py
new file mode 100644
index 0000000..0669604
--- /dev/null
+++ b/application/image_generator.py
@@ -0,0 +1,62 @@
+import random
+import matplotlib.pyplot as plt
+from application.code_parser import CodeParser
+class ImageGenerator(CodeParser):
+   def load_images(self, images_directory):
+      #Dividing the data in different batches and showing labels
+      data = self.\
+         parse_and_execute('load_images', [images_directory])
+      print(data)
+
+   def validate_images(self, images_directory):
+      self.\
+         parse_and_execute('validate_images', [images_directory])
+
+   def preprocess_images(self, images_directory): #directory after validating the images
+      (data, normalized_data) = self.\
+         parse_and_execute('preprocess_images', [images_directory])
+      class_mapping = {}
+      print("Train classes:: ", data.class_names)
+      print("Number of training files:", len(data.file_paths))
+      self.save('data', data)
+      self.save('normalized_data', normalized_data)
+      class_names = self.data['data'].class_names
+      plt.figure(figsize=(10, 10))
+      for images, labels in self.data['data'].take(random.randint(1,len(data))):
+         for i in range(9):
+            plt.subplot(3, 3, i + 1)
+            # plt.imshow(cv2.cvtColor(images[i].numpy().astype('uint8'), cv2.COLOR_BGR2RGB))
+            plt.imshow(images[i].numpy().astype("uint8"))
+            class_mapping[int(labels[i].numpy())]= class_names[labels[i]]
+            plt.title(class_names[labels[i]] + ' => ' + str(labels[i].numpy()))
+            plt.axis("off")
+      self.save('class_mapping', class_mapping)
+      plt.show()
+      return self.data['data']
+
+   def split_images(self, train_ratio, val_ratio, test_ratio):
+      (train, val, test) = self.\
+         parse_and_execute('split_images', ['normalized_data', train_ratio, val_ratio, test_ratio])
+      self.save('train', train)
+      self.save('val', val)
+      self.save('test', test)
+      return self.data['train'], self.data['val'], self.data['test']
+
+   def train_model(self, epochs):
+      (history, model) = self.\
+         parse_and_execute('images_model', ['normalized_data', 'train', 'val', epochs])
+      self.save('history', history)
+      self.save('model', model)
+      return self.data['history']
+
+   def model_metrics(self):
+      (precision, recall, accuracy) = self.parse_and_execute('metrics', ['test', 'model'])
+      return precision, recall, accuracy
+
+   def visualize(self):
+      self.\
+         parse_and_execute('visualize', ['history'])
+
+   def test_image(self, location):
+      self.\
+         parse_and_execute('test_image', [location, 'model', 'class_mapping'])
diff --git a/data/.DS_Store b/data/.DS_Store
new file mode 100644
index 0000000..59cdcee
Binary files /dev/null and b/data/.DS_Store differ
diff --git a/data/possum.csv b/data/possum.csv
deleted file mode 100644
index 0f7cc0f..0000000
--- a/data/possum.csv
+++ /dev/null
@@ -1,105 +0,0 @@
-case,site,Pop,sex,age,hdlngth,skullw,totlngth,taill,footlgth,earconch,eye,chest,belly
-1,1,Vic,m,8,94.1,60.4,89,36,74.5,54.5,15.2,28,36
-2,1,Vic,f,6,92.5,57.6,91.5,36.5,72.5,51.2,16,28.5,33
-3,1,Vic,f,6,94,60,95.5,39,75.4,51.9,15.5,30,34
-4,1,Vic,f,6,93.2,57.1,92,38,76.1,52.2,15.2,28,34
-5,1,Vic,f,2,91.5,56.3,85.5,36,71,53.2,15.1,28.5,33
-6,1,Vic,f,1,93.1,54.8,90.5,35.5,73.2,53.6,14.2,30,32
-7,1,Vic,m,2,95.3,58.2,89.5,36,71.5,52,14.2,30,34.5
-8,1,Vic,f,6,94.8,57.6,91,37,72.7,53.9,14.5,29,34
-9,1,Vic,f,9,93.4,56.3,91.5,37,72.4,52.9,15.5,28,33
-10,1,Vic,f,6,91.8,58,89.5,37.5,70.9,53.4,14.4,27.5,32
-11,1,Vic,f,9,93.3,57.2,89.5,39,77.2,51.3,14.9,31,34
-12,1,Vic,f,5,94.9,55.6,92,35.5,71.7,51,15.3,28,33
-13,1,Vic,m,5,95.1,59.9,89.5,36,71,49.8,15.8,27,32
-14,1,Vic,m,3,95.4,57.6,91.5,36,74.3,53.7,15.1,28,31.5
-15,1,Vic,m,5,92.9,57.6,85.5,34,69.7,51.8,15.7,28,35
-16,1,Vic,m,4,91.6,56,86,34.5,73,51.4,14.4,28,32
-17,1,Vic,f,1,94.7,67.7,89.5,36.5,73.2,53.2,14.7,29,31
-18,1,Vic,m,2,93.5,55.7,90,36,73.7,55.4,15.3,28,32
-19,1,Vic,f,5,94.4,55.4,90.5,35,73.4,53.9,15.2,28,32
-20,1,Vic,f,4,94.8,56.3,89,38,73.8,52.4,15.5,27,36
-21,1,Vic,f,3,95.9,58.1,96.5,39.5,77.9,52.9,14.2,30,40
-22,1,Vic,m,3,96.3,58.5,91,39.5,73.5,52.1,16.2,28,36
-23,1,Vic,f,4,92.5,56.1,89,36,72.8,53.3,15.4,28,35
-24,1,Vic,m,2,94.4,54.9,84,34,75,53.5,16.2,27,32
-25,1,Vic,m,3,95.8,58.5,91.5,35.5,72.3,51.6,14.9,31,35
-26,1,Vic,m,7,96,59,90,36,73.6,56.2,15,29,38
-27,1,Vic,f,2,90.5,54.5,85,35,70.3,50.8,14.2,23,28
-28,1,Vic,m,4,93.8,56.8,87,34.5,73.2,53,15.3,27,30
-29,1,Vic,f,3,92.8,56,88,35,74.9,51.8,14,24,32
-30,1,Vic,f,2,92.1,54.4,84,33.5,70.6,50.8,14.5,24.5,33
-31,1,Vic,m,3,92.8,54.1,93,37,68,52.5,14.5,27,31
-32,1,Vic,f,4,94.3,56.7,94,39,74.8,52,14.9,28,34
-33,1,Vic,m,3,91.4,54.6,89,37,70.8,51.8,14.8,24,30
-34,2,Vic,m,2,90.6,55.7,85.5,36.5,73.1,53.1,14.4,26,28.5
-35,2,Vic,m,4,94.4,57.9,85,35.5,71.2,55.5,16.4,28,35.5
-36,2,Vic,m,7,93.3,59.3,88,35,74.3,52,14.9,25.5,36
-37,2,Vic,f,2,89.3,54.8,82.5,35,71.2,52,13.6,28,31.5
-38,2,Vic,m,7,92.4,56,80.5,35.5,68.4,49.5,15.9,27,30
-39,2,Vic,f,1,84.7,51.5,75,34,68.7,53.4,13,25,25
-40,2,Vic,f,3,91,55,84.5,36,72.8,51.4,13.6,27,30
-41,2,Vic,f,5,88.4,57,83,36.5,NA,40.3,15.9,27,30.5
-42,2,Vic,m,3,85.3,54.1,77,32,62.7,51.2,13.8,25.5,33
-43,2,Vic,f,2,90,55.5,81,32,72,49.4,13.4,29,31
-44,2,Vic,m,NA,85.1,51.5,76,35.5,70.3,52.6,14.4,23,27
-45,2,Vic,m,3,90.7,55.9,81,34,71.5,54,14.6,27,31.5
-46,2,Vic,m,NA,91.4,54.4,84,35,72.8,51.2,14.4,24.5,35
-47,3,other,m,2,90.1,54.8,89,37.5,66,45.5,15,25,33
-48,3,other,m,5,98.6,63.2,85,34,66.9,44.9,17,28,35
-49,3,other,m,4,95.4,59.2,85,37,69,45,15.9,29.5,35.5
-50,3,other,f,5,91.6,56.4,88,38,65,47.2,14.9,28,36
-51,3,other,f,5,95.6,59.6,85,36,64,43.9,17.4,28,38.5
-52,3,other,m,6,97.6,61,93.5,40,67.9,44.3,15.8,28.5,32.5
-53,3,other,f,3,93.1,58.1,91,38,67.4,46,16.5,26,33.5
-54,4,other,m,7,96.9,63,91.5,43,71.3,46,17.5,30,36.5
-55,4,other,m,2,103.1,63.2,92.5,38,72.5,44.9,16.4,30.5,36
-56,4,other,m,3,99.9,61.5,93.7,38,68.7,46.8,16.4,27.5,31.5
-57,4,other,f,4,95.1,59.4,93,41,67.2,45.3,14.5,31,39
-58,4,other,m,3,94.5,64.2,91,39,66.5,46.4,14.4,30.5,33
-59,4,other,m,2,102.5,62.8,96,40,73.2,44.5,14.7,32,36
-60,4,other,f,2,91.3,57.7,88,39,63.1,47,14.4,26,30
-61,5,other,m,7,95.7,59,86,38,63.1,44.9,15,26.5,31
-62,5,other,f,3,91.3,58,90.5,39,65.5,41.3,16,27,32
-63,5,other,f,6,92,56.4,88.5,38,64.1,46.3,15.2,25.5,28.5
-64,5,other,f,3,96.9,56.5,89.5,38.5,63,45.1,17.1,25.5,33
-65,5,other,f,5,93.5,57.4,88.5,38,68.2,41.7,14,29,38.5
-66,5,other,f,3,90.4,55.8,86,36.5,63.2,44.2,15.7,26.5,34
-67,5,other,m,4,93.3,57.6,85,36.5,64.7,44.1,16.5,27.5,29.5
-68,5,other,m,5,94.1,56,88.5,38,65.9,43.1,17.4,27,30
-69,5,other,m,5,98,55.6,88,37.5,65,45.6,15,28.5,34
-70,5,other,f,7,91.9,56.4,87,38,65.4,44.1,13,27,34
-71,5,other,m,6,92.8,57.6,90,40,65.7,42.8,15,27.5,34
-72,5,other,m,1,85.9,52.4,80.5,35,62,42.4,14.1,25.5,30
-73,5,other,m,1,82.5,52.3,82,36.5,65.7,44.7,16,23.5,28
-74,6,other,f,4,88.7,52,83,38,61.5,45.9,14.7,26,34
-75,6,other,m,6,93.8,58.1,89,38,66.2,45.6,16.9,26,33.5
-76,6,other,m,5,92.4,56.8,89,41,64.5,46.4,17.8,26,33
-77,6,other,m,6,93.6,56.2,84,36,62.8,42.9,16.2,25,35
-78,6,other,m,1,86.5,51,81,36.5,63,44.3,13.2,23,28
-79,6,other,m,1,85.8,50,81,36.5,62.8,43,14.8,22,28.5
-80,6,other,m,1,86.7,52.6,84,38,62.3,44.8,15,23.5,30.5
-81,6,other,m,3,90.6,56,85.5,38,65.6,41.7,17,27.5,35
-82,6,other,f,4,86,54,82,36.5,60.7,42.9,15.4,26,32
-83,6,other,f,3,90,53.8,81.5,36,62,43.3,14,25,29
-84,6,other,m,3,88.4,54.6,80.5,36,62.6,43.6,16.3,25,28.5
-85,6,other,m,3,89.5,56.2,92,40.5,65.6,43.5,14.5,27,31.5
-86,6,other,f,3,88.2,53.2,86.5,38.5,60.3,43.7,13.6,26,31
-87,7,other,m,2,98.5,60.7,93,41.5,71.7,46.8,15,26,36
-88,7,other,f,2,89.6,58,87.5,38,66.7,43.5,16,25.5,31.5
-89,7,other,m,6,97.7,58.4,84.5,35,64.4,46.2,14.4,29,30.5
-90,7,other,m,3,92.6,54.6,85,38.5,69.8,44.8,14.5,25.5,32.5
-91,7,other,m,3,97.8,59.6,89,38,65.5,48,15,26,32
-92,7,other,m,2,90.7,56.3,85,37,67.6,46.8,14.5,25.5,31
-93,7,other,m,3,89.2,54,82,38,63.8,44.9,12.8,24,31
-94,7,other,m,7,91.8,57.6,84,35.5,64.2,45.1,14.4,29,35
-95,7,other,m,4,91.6,56.6,88.5,37.5,64.5,45.4,14.9,27,31
-96,7,other,m,4,94.8,55.7,83,38,66.5,47.7,14,25,33
-97,7,other,m,3,91,53.1,86,38,63.8,46,14.5,25,31.5
-98,7,other,m,5,93.2,68.6,84,35,65.6,44.3,14.5,28.5,32
-99,7,other,f,3,93.3,56.2,86.5,38.5,64.8,43.8,14,28,35
-100,7,other,m,1,89.5,56,81.5,36.5,66,46.8,14.8,23,27
-101,7,other,m,1,88.6,54.7,82.5,39,64.4,48,14,25,33
-102,7,other,f,6,92.4,55,89,38,63.5,45.4,13,25,30
-103,7,other,m,4,91.5,55.2,82.5,36.5,62.9,45.9,15.4,25,29
-104,7,other,f,3,93.6,59.9,89,40,67.6,46,14.8,28.5,33.5
diff --git a/pandas_code/code_templates/images_model.py b/pandas_code/code_templates/images_model.py
new file mode 100644
index 0000000..c26c129
--- /dev/null
+++ b/pandas_code/code_templates/images_model.py
@@ -0,0 +1,30 @@
+import tensorflow as tf
+#Deep learning images model
+def get_code(args):
+
+   model = tf.keras.models.Sequential([
+   tf.keras.layers.Conv2D(16, 3, padding='same', input_shape=(256, 256, 3), activation='relu'),
+   tf.keras.layers.MaxPooling2D(),
+   tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
+   tf.keras.layers.MaxPooling2D(),
+   tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),
+   tf.keras.layers.MaxPooling2D(),
+   tf.keras.layers.Flatten(),
+   tf.keras.layers.Dense(256, activation='relu'),
+   tf.keras.layers.Dense(1, activation='sigmoid')
+   ])
+   early_stopping = tf.keras.callbacks.EarlyStopping\
+      (monitor='val_loss', mode='min', verbose=1, patience=10)
+   model.compile('adam',
+               #   SparseCategoricalCrossentropy(from_logits=True)
+    loss=tf.losses.BinaryCrossentropy(),metrics=['accuracy'])
+   model.summary()
+
+   history = model.fit(
+      args[1], #train batch
+      validation_data = args[2], #validation batch
+      epochs = args[3], #change number of epochs as per your requirement
+      callbacks=[early_stopping]
+   )
+   # model.save(os.path.join('models', 'simplemodel.h5'))
+   return history, model
diff --git a/pandas_code/code_templates/load_images.py b/pandas_code/code_templates/load_images.py
new file mode 100644
index 0000000..97d4bcf
--- /dev/null
+++ b/pandas_code/code_templates/load_images.py
@@ -0,0 +1,9 @@
+import tensorflow as tf
+# Identifying number of classes and labels
+def get_code(args):
+   #For the configuration of using GPU
+   gpus = tf.config.experimental.list_physical_devices('GPU')
+   for gpu in gpus:
+      tf.config.experimental.set_memory_growth(gpu, True)
+   data = tf.keras.utils.image_dataset_from_directory(args[0], batch_size=22)
+   return data
diff --git a/pandas_code/code_templates/metrics.py b/pandas_code/code_templates/metrics.py
new file mode 100644
index 0000000..3fbb538
--- /dev/null
+++ b/pandas_code/code_templates/metrics.py
@@ -0,0 +1,17 @@
+import tensorflow as tf
+#Finding the accuracy of the model
+# pylint: disable=not-callable
+def get_code(args):
+   precision = tf.keras.metrics.Precision()
+   recall = tf.keras.metrics.Recall()
+   accuracy = tf.keras.metrics.BinaryAccuracy()
+   # test = args[0].map(lambda x, y: (x/255, y))
+   for batch in args[0].as_numpy_iterator():
+      images_batch, labels_batch = batch
+      yhat = args[1].predict(images_batch)
+      precision.update_state(labels_batch, yhat)
+      recall.update_state(labels_batch, yhat)
+      accuracy.update_state(labels_batch, yhat)
+   print(f'Precision: {precision.result().numpy()}, \
+   Recall: {recall.result().numpy()}, Accuracy: {accuracy.result().numpy()}')
+   return precision, recall, accuracy
diff --git a/pandas_code/code_templates/plot_images.py b/pandas_code/code_templates/plot_images.py
new file mode 100644
index 0000000..3ca80c3
--- /dev/null
+++ b/pandas_code/code_templates/plot_images.py
@@ -0,0 +1,16 @@
+# data_dir = '../data'
+# print(os.listdir(os.path.join(data_dir)))
+#Dividing the data in different batches and showing labels
+# data = tf.keras.utils.image_dataset_from_directory(data_dir, batch_size=12)
+# # data_iterator = data.as_numpy_iterator() # Iterating thru each batch of images
+# # batch = data_iterator.next() # Getting images of each batch
+# # Plotting batch of images
+# class_names = data.class_names
+# plt.figure(figsize=(10, 10))
+# for images, labels in data.take(1):
+#    for i in range(9):
+#       ax = plt.subplot(3, 3, i + 1)
+#       plt.imshow(images[i].numpy().astype("uint8"))
+#       plt.title(class_names[labels[i]] + ' => ' + str(labels[i].numpy()))
+#       plt.axis("off")
+# plt.show()
diff --git a/pandas_code/code_templates/preprocess_images.py b/pandas_code/code_templates/preprocess_images.py
new file mode 100644
index 0000000..c6171bc
--- /dev/null
+++ b/pandas_code/code_templates/preprocess_images.py
@@ -0,0 +1,14 @@
+import tensorflow as tf
+#Preprocessing images
+def get_code(args):
+   # Preprocess all images
+   data = tf.keras.utils.image_dataset_from_directory(
+      args[0],
+      # validation_split=0.2,
+      # subset='training',
+      image_size = (256, 256),
+      batch_size = 22,
+      seed = 123
+   )
+   normalized_data = data.map(lambda x, y: (x/255, y))
+   return data, normalized_data
diff --git a/pandas_code/code_templates/split_images.py b/pandas_code/code_templates/split_images.py
new file mode 100644
index 0000000..29d1a0b
--- /dev/null
+++ b/pandas_code/code_templates/split_images.py
@@ -0,0 +1,19 @@
+#splitting images
+def get_code(args):
+   train_size = int(len(args[0]) * args[1])
+   val_size = int(len(args[0]) * args[2]) + 1
+   test_size = int(len(args[0])* args[3]) + 1
+   train = args[0].take(train_size)
+   val = args[0].skip(train_size).take(val_size)
+   test = args[0].skip(train_size + val_size).take(test_size)
+   return train, val, test
+
+# def split_images(self, train_ration, val_ratio, test_ratio):
+#    # Splitting images
+#    train_size = int(len(data) * 0.7)
+#    val_size = int(len(data) * .2) + 1
+#    test_size = int(len(data)* .1) + 1
+
+#    train = data.take((train_size))
+#    val = data.skip(train_size).take(val_size)
+#    test = data.skip(train_size + val_size).take(test_size)
diff --git a/pandas_code/code_templates/test_image.py b/pandas_code/code_templates/test_image.py
new file mode 100644
index 0000000..87c7ddf
--- /dev/null
+++ b/pandas_code/code_templates/test_image.py
@@ -0,0 +1,19 @@
+import cv2
+import numpy as np
+import tensorflow as tf
+# Testing the model on images which it havn't seen before
+def get_code(args):
+   img = cv2.imread(args[0])
+   # plt.imshow(img)
+   # plt.show()
+   resize = tf.image.resize(img, (256, 256))
+   # plt.imshow(resize.numpy().astype('uint8'))
+   # plt.show()
+   # print(resize.shape)
+   # We're expanding dimensions of the images because
+   # our model expects batch of images
+   yhat = args[1].predict(np.expand_dims(resize/255, 0))
+   if yhat > 0.5:
+      print(f"Predicted class is {args[2][0]}")
+   else:
+      print(f"Predicted class is {args[2][1]}")
diff --git a/pandas_code/code_templates/validate_images.py b/pandas_code/code_templates/validate_images.py
new file mode 100644
index 0000000..8afd08e
--- /dev/null
+++ b/pandas_code/code_templates/validate_images.py
@@ -0,0 +1,23 @@
+import os
+import imghdr
+import cv2
+#Validating Images in the specified directory
+def get_code(args):
+   image_ext = ['jpeg', 'jpg', 'bmp', 'png']
+   for image_class in os.listdir(args[0]):
+      if image_class[0] != '.':
+         for image in os.listdir(os.path.join(args[0], image_class)):
+            if image[0] != '.':
+               image_path = os.path.join(args[0], image_class, image)
+            try:
+               cv2.imread(image_path)
+               tip = imghdr.what(image_path)
+               if tip not in image_ext:
+                  print(f"Bad image with invalid ext {image_path}")
+                  os.remove(image_path)
+               # Uncomment the following two line if you've problem validating the files
+               # img_bytes = tf.io.read_file(image_path)
+               # tf.io.decode_image(img_bytes)
+            except IOError:
+               print(f"Found bad path {image_path}")
+               os.remove(image_path)
diff --git a/pandas_code/code_templates/visualize.py b/pandas_code/code_templates/visualize.py
new file mode 100644
index 0000000..b8b0f9e
--- /dev/null
+++ b/pandas_code/code_templates/visualize.py
@@ -0,0 +1,18 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+#visualize accuracy
+def get_code(args):
+#Accuracy Graph
+   history_df = pd.DataFrame(args[0].history)
+   plt.figure(figsize=(12,4))
+   plt.subplot(1,2,1)
+   plt.plot(history_df['loss'], label='training loss')
+   plt.plot(history_df['val_loss'], label='validation loss')
+   plt.title('Model Loss Function')
+   plt.legend()
+   plt.subplot(1,2,2)
+   plt.plot(history_df['accuracy'], label='training accuracy')
+   plt.plot(history_df['val_accuracy'], label='validation accuracy')
+   plt.title('Model Accuracy')
+   plt.legend()
+   plt.show()
diff --git a/pandas_code/mapping.py b/pandas_code/mapping.py
index 87a5f88..070e632 100644
--- a/pandas_code/mapping.py
+++ b/pandas_code/mapping.py
@@ -9,6 +9,14 @@
 from pandas_code.code_templates import lin_reg_predict
 from pandas_code.code_templates import eval_lin_reg
 
+from pandas_code.code_templates import load_images
+from pandas_code.code_templates import validate_images
+from pandas_code.code_templates import preprocess_images
+from pandas_code.code_templates import split_images
+from pandas_code.code_templates import images_model
+from pandas_code.code_templates import visualize
+from pandas_code.code_templates import metrics
+from pandas_code.code_templates import test_image
 template_mapping = {
    'read_csv': read_csv.get_code,
    'describe_data': describe_data.get_code,
@@ -19,5 +27,13 @@
    'drop_x': drop_x.get_code,
    'train_lin_reg': train_lin_reg.get_code,
    'lin_reg_predict': lin_reg_predict.get_code,
+   'load_images':load_images.get_code,
+   'validate_images':validate_images.get_code,
+   'preprocess_images':preprocess_images.get_code,
+   'split_images':split_images.get_code,
+   'images_model':images_model.get_code,
+   'visualize':visualize.get_code,
+   'metrics':metrics.get_code,
+   'test_image':test_image.get_code,
    'eval_lin_reg': eval_lin_reg.get_code
 }
diff --git a/requirements.txt b/requirements.txt
index 16ed80d..6352719 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,8 @@ Authlib
 pylint
 scikit-learn
 numpy
-google-api-python-client
\ No newline at end of file
+tensorflow
+tensorflow-gpu
+opencv-python
+matplotlib
+google-api-python-client
diff --git a/tests/pandas_code/parse_template_test.py b/tests/pandas_code/parse_template_test.py
index 8e85760..4c0235f 100644
--- a/tests/pandas_code/parse_template_test.py
+++ b/tests/pandas_code/parse_template_test.py
@@ -54,6 +54,7 @@ def test_parse_train():
    template_name = 'train_lin_reg'
    args = ['x_values', 'y_values']
    parse_template(template_name, args)
+
 def test_parse_split():
    template_name = 'split'
    args = ['X', 'Y', '0.8', '200']
diff --git a/visual.py b/visual.py
new file mode 100644
index 0000000..f884a0b
--- /dev/null
+++ b/visual.py
@@ -0,0 +1,34 @@
+import sys
+from application import image_generator
+from pandas_code.mapping import template_mapping
+from pandas_code.parse_template import parse_template
+
+def run_generator(args):
+   input_file = args[1]
+   image_gen = image_generator.ImageGenerator(template_mapping, parse_template)
+   image_gen.load_images(input_file)
+   image_gen.validate_images(input_file)
+   data_generator = image_gen.preprocess_images(input_file)
+   print("Train classes:: ", data_generator.class_names)
+   print("Number of training files:", len(data_generator.file_paths))
+   classes=  list(data_generator.class_names)
+   print(f"There are {len(classes)} number of classes")
+   (train, val, test) = image_gen.split_images(0.7, 0.1, 0.1)
+   print(len(train))
+   print(len(val))
+   print(len(test))
+   history = image_gen.train_model(epochs=20)
+   print(history)
+   image_gen.visualize()
+   image_gen.model_metrics()
+   #Create any image for testing purpose
+   image_gen.test_image('/Users/harisalam/ml_code_generator/test/happyFace.jpg')
+   print("\n\n\n\nPrinting Starts from here.\n")
+   print(image_gen.download_code())
+
+#process the arguments
+if __name__ == '__main__':
+   run_generator(sys.argv)
+# code can be run using the following command
+# python visual.py data/
+# data/ => is the directory where we have images of different classes