edit setting metadate

.gitignore

@@ -33,3 +33,4 @@ __pycache__/
 /tests/augmented_dataset/
 /tests/dataset/
 /s3_dataset/
+/neural_network_model/settings.json

neural_network_model/bit_vision.py

@@ -129,7 +129,12 @@ def assemble_deep_net_model(self) -> tensorflow.keras.models.Sequential:
         activ = "relu"
 
         model.add(
-            Conv2D(32, kernel_size=(3, 3), activation=activ, input_shape=(image_size, image_size, 3))
+            Conv2D(
+                32,
+                kernel_size=(3, 3),
+                activation=activ,
+                input_shape=(image_size, image_size, 3),
+            )
         )
         model.add(BatchNormalization())
 
@@ -167,19 +172,25 @@ def assemble_deep_net_model_2(self) -> tensorflow.keras.models.Sequential:
         dropout_dense = 0.3
 
         model = Sequential()
-        model.add(Conv2D(first_filters, kernel_size, activation='relu',
-                         input_shape=(image_size, image_size, 3)))
-        model.add(Conv2D(first_filters, kernel_size, activation='relu'))
+        model.add(
+            Conv2D(
+                first_filters,
+                kernel_size,
+                activation="relu",
+                input_shape=(image_size, image_size, 3),
+            )
+        )
+        model.add(Conv2D(first_filters, kernel_size, activation="relu"))
         model.add((MaxPooling2D(pool_size=pool_size)))
         model.add(Dropout(dropout_conv))
 
-        model.add(Conv2D(second_filters, kernel_size, activation='relu'))
-        model.add(Conv2D(second_filters, kernel_size, activation='relu'))
+        model.add(Conv2D(second_filters, kernel_size, activation="relu"))
+        model.add(Conv2D(second_filters, kernel_size, activation="relu"))
         model.add((MaxPooling2D(pool_size=pool_size)))
         model.add(Dropout(dropout_conv))
 
-        model.add(Conv2D(third_filters, kernel_size, activation='relu'))
-        model.add(Conv2D(third_filters, kernel_size, activation='relu'))
+        model.add(Conv2D(third_filters, kernel_size, activation="relu"))
+        model.add(Conv2D(third_filters, kernel_size, activation="relu"))
         model.add((MaxPooling2D(pool_size=pool_size)))
         model.add(Dropout(dropout_conv))
 
@@ -191,7 +202,6 @@ def assemble_deep_net_model_2(self) -> tensorflow.keras.models.Sequential:
         self.model = model
         return self.model
 
-
     def compile_model(self, *args, **kwargs) -> None:
         """
         This function is used to compile the model.
@@ -216,7 +226,9 @@ def plot_image_category(self, *args, **kwargs) -> None:
         fig, axs = plt.subplots(nrows, ncols, figsize=fig_size)
 
         if nrows == 1 and ncols > 1:
-            axs = axs.reshape((ncols,))  # Reshape axs to handle 1 row and multiple columns
+            axs = axs.reshape(
+                (ncols,)
+            )  # Reshape axs to handle 1 row and multiple columns
 
         for i, category in enumerate(self.categories):
             category_path = self.train_test_val_dir / subdir / category
@@ -409,9 +421,9 @@ def predict(self, *args, **kwargs):
         default_figsize = SETTING.FIGURE_SETTING.FIGURE_SIZE_IN_PRED_MODEL
 
         # Extract details from kwargs or use default settings
-        num_rows = kwargs.get('num_rows', default_num_rows)
-        num_cols = kwargs.get('num_cols', default_num_cols)
-        figure_size = kwargs.get('figure_size', default_figsize)
+        num_rows = kwargs.get("num_rows", default_num_rows)
+        num_cols = kwargs.get("num_cols", default_num_cols)
+        figure_size = kwargs.get("figure_size", default_figsize)
 
         for category in self.categories:
             plt.figure(figsize=figure_size)
@@ -672,6 +684,10 @@ def return_best_model_name(
 
         return best_model
 
+    @staticmethod
+    def print_deepnet_project_metadata():
+        SETTING.load_settings_from_json()
+
 
 if __name__ == "__main__":
     obj = BitVision(

neural_network_model/model.py

@@ -1,10 +1,13 @@
+import json
 import os
 from pathlib import Path
+from pprint import pprint
 from typing import List
 
 from dotenv import load_dotenv
 from pydantic import BaseModel
 from tensorflow import keras
+from dotenv import load_dotenv
 
 # Load environment variables from .env file
 load_dotenv()
@@ -136,7 +139,7 @@ class GradCamSetting(BaseModel):
 
 
 class S3BucketSetting(BaseModel):
-    BUCKET_NAME: str = "bitimages123"
+    BUCKET_NAME: str = os.getenv("BUCKET_NAME")
     PARENT_FOLDER_NAME: str = "dataset"
     SUBFOLDER_NAME: list = ["dataset/pdc_bit", "dataset/rollercone_bit"]
     DOWNLOAD_LOCATION: str = (Path(__file__).parent / ".." / "s3_dataset").resolve()
@@ -192,9 +195,32 @@ class Setting(BaseModel):
     EC2_SETTING: Ec2Setting = Ec2Setting()
     GITHUB_SETTING: GitHubSetting = GitHubSetting()
 
+    @staticmethod
+    def save_settings_to_json(filename: str = "settings.json"):
+        def convert_paths(obj):
+            if isinstance(obj, (Path, tuple)):
+                return str(obj)
+            return obj
+
+        with open(filename, "w") as f:
+            json.dump(SETTING.dict(), f, indent=4, default=convert_paths)
+
+    @staticmethod
+    def load_settings_from_json(filename: str = "settings.json") -> json:
+        with open(filename, "r") as f:
+            data = json.load(f)
+        pprint(Setting(**data))
+        return Setting(**data)
+
 
 SETTING = Setting()
 
 
 if __name__ == "__main__":
-    print(SETTING.EC2_SETTING.SECURITY_GROUP_ID)
+    # print(SETTING.EC2_SETTING.SECURITY_GROUP_ID)
+    # Save the settings to a JSON file
+    # SETTING.save_settings_to_json("settings.json")
+    SETTING.load_settings_from_json("settings.json")
+
+    # Load the settings from a JSON file
+    # loaded_settings = load_settings_from_json("settings.json")

neural_network_model/process_data.py

@@ -354,6 +354,10 @@ def _copy_images(
             f"copied {len(images)} images for category {categ} to {dest_folder}"
         )
 
+    @staticmethod
+    def print_deepnet_project_metadata():
+        SETTING.load_settings_from_json()
+
 
 if __name__ == "__main__":
     obj = Preprocessing(dataset_address=Path(__file__).parent / ".." / "dataset")
@@ -366,5 +370,6 @@ def _copy_images(
     # obj.download_images(from_s3=True)
 
     print(obj.image_dict)
-    obj.augment_data(number_of_images_tobe_gen=20)
+    obj.augment_data(number_of_images_tobe_gen=10)
     obj.train_test_split()
+    obj.print_deepnet_project_metadata()