Merge pull request #159 from Aydinhamedi/Alpha-b

Alpha b

.gitignore

@@ -82,5 +82,10 @@ Samples/*
 /Interface/GUI/GUI_DEV.cmd
 
 # Exclude logs in GUI interface
-/Interface/GUI/Data/logs
-/Test_ENV_G.py
+/Interface/GUI/Data/logs
+
+# Other
+/Test_ENV_G.py
+/scc.exe
+/SCC_Auto.cmd
+/Microsoft.PowerShell_profile_Nvidia_smi.ps1

Interface/CLI/CLI.cmd

@@ -88,7 +88,6 @@ if /i "%restart%"=="y" (
     goto :EOF
 )
 
-REM Check it 🚧Beta🚧 --- [>>>
 :errorNoPython
 REM Handle the error if Python is not installed
 if exist %PUE_filepath% (
@@ -147,7 +146,6 @@ if /I "!UserInput!"=="y" (
 )
 pause
 goto :EOF
-REM Check it 🚧Beta🚧 --- <<<]
 
 :check_install
 REM Check if a package is installed and offer to install it if not

Interface/GUI/Data/GUI_main.py

@@ -26,7 +26,6 @@
 from time import sleep
 import PySimpleGUI as sg
 from loguru import logger
-import efficientnet.tfkeras
 from tkinter import filedialog
 from datetime import datetime
 from PIL import Image
@@ -43,6 +42,7 @@
 from Utils.Grad_cam import make_gradcam_heatmap
 from Utils.print_color_V2_NEW import print_Color_V2
 from Utils.print_color_V1_OLD import print_Color
+from Utils.FixedDropout import FixedDropout
 from Utils.Other import *
 
 # global vars>>>
@@ -135,7 +135,7 @@ def is_updated(self):
         sg.Button('Reload Model', key='-BUTTON_RELOAD_MODEL-')
     ],
     [
-        sg.Checkbox('Download Light Model', key='-CHECKBOX_DOWNLOAD_LIGHT_MODEL-', default=False)
+        sg.Checkbox('Download Light Model', key='-CHECKBOX_DOWNLOAD_LIGHT_MODEL-', default=True)
     ],
     [sg.Text('Ai Model Info:', font=(None, 10, 'bold'))],
     [
@@ -334,7 +334,7 @@ def CI_pwai(show_gradcam: bool = True) -> str:
         try:
             if model is None:
                 print_Color('loading the Ai model...', ['normal'])
-                model = load_model(Model_dir)
+                model = load_model(Model_dir, custom_objects={'FixedDropout': FixedDropout})
         except (ImportError, IOError):
             return 'ERROR: Failed to load the model.'
         else:
@@ -384,7 +384,7 @@ def CI_rlmw() -> None:
     model = None
     GUI_Queue['-Main_log-'].put('loading the Ai model...')
     try:
-        model = load_model(Model_dir)
+        model = load_model(Model_dir, custom_objects={'FixedDropout': FixedDropout})
     except (ImportError, IOError):
         GUI_Queue['-Main_log-'].put('ERROR: Failed to load the model.')
         return None

Interface/GUI/Data/Utils/FixedDropout.py

@@ -0,0 +1,19 @@
+from tensorflow.keras import layers, backend
+
+class FixedDropout(layers.Dropout):
+    def _get_noise_shape(self, inputs):
+        if self.noise_shape is None:
+            return self.noise_shape
+
+        symbolic_shape = backend.shape(inputs)
+        noise_shape = [symbolic_shape[axis] if shape is None else shape
+                       for axis, shape in enumerate(self.noise_shape)]
+        return tuple(noise_shape)
+
+    def get_config(self):
+        config = super().get_config()
+        return config
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(**config)

Interface/GUI/Data/Utils/Other.py

@@ -1,12 +1,20 @@
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 from Utils.print_color_V2_NEW import print_Color_V2
 from Utils.print_color_V1_OLD import print_Color
+from tabulate import tabulate
+import numpy as np
 import pickle
 import gzip
 
 def save_list(history, filename, compress=True):
-    # Saves the given history list to the specified filename.
-    # If compress is True, the file will be gzip compressed.
-    # Otherwise it will be saved as a normal pickle file.
+    """Saves a list to a file.
+
+    Args:
+        history: The list to save.
+        filename: The file to save the list to.
+        compress: Whether to gzip compress the file. Default is True.
+
+    """
     if compress:
         with gzip.open(filename, 'wb') as f:
             pickle.dump(history, f)
@@ -16,17 +24,69 @@ def save_list(history, filename, compress=True):
 
 
 def load_list(filename, compressed=True):
-    # Loads a pickled object from a file.
-    # If compressed=True, it will load from a gzip compressed file.
-    # Otherwise loads from a regular file.
+    """Loads a list from a file.
+
+    Args:
+        filename: The file to load from.
+        compressed: Whether the file is gzip compressed. Default is True.
+
+    Returns:
+        The loaded list from the file.
+    """
     if compressed:
         with gzip.open(filename, 'rb') as f:
             return pickle.load(f)
     else:
         with open(filename, 'rb') as f:
             return pickle.load(f)
+
+
 def P_warning(msg):
-    # Prints a warning message with color formatting.
-    # msg: The message to print as a warning.
+    """Prints a warning message to the console.
+
+    Args:
+        msg (str): The warning message to print.
+    """
     print_Color_V2(f'<light_red>Warning: <yellow>{msg}')
-
+
+def evaluate_model_full(y_test, model_pred, model=None, x_test=None):
+    """Evaluates a machine learning model on a test set.
+
+    Args:
+        x_test: Test set features.
+        y_test: Test set labels.  
+        model_pred: Model predictions.
+        model: The model object.
+
+    Returns:
+        None. Prints a table with accuracy, precision, recall and 
+        F1 score.
+    """
+    # Get the model predictions
+    if model_pred is None:
+        y_pred = model.predict(x_test)
+    else:
+        y_pred = model_pred
+
+    # Convert one-hot encoded predictions and labels to label encoded form
+    y_pred_bin = np.argmax(y_pred, axis=1)
+    y_test_bin = np.argmax(y_test, axis=1)
+
+    # Calculate normal metrics
+    accuracy = accuracy_score(y_test_bin, y_pred_bin)
+
+    # Calculate weighted metrics
+    weighted_precision = precision_score(
+        y_test_bin, y_pred_bin, average='macro')
+    weighted_f1 = f1_score(y_test_bin, y_pred_bin, average='macro')
+    weighted_recall = recall_score(y_test_bin, y_pred_bin, average='macro')
+
+    # Prepare data for the table
+    metrics = [["Accuracy", round(accuracy * 100, 6)],
+               ["Precision", round(weighted_precision * 100, 6)],
+               ["F1 Score", round(weighted_f1 * 100, 6)],
+               ["Recall", round(weighted_recall * 100, 6)]]
+
+    # Print the table
+    print(tabulate(metrics, headers=["Metric", "Value"], tablefmt="pretty"))
+

Interface/GUI/Data/Utils/README.md

@@ -13,3 +13,13 @@
 ## Grad_cam (by GPT-4 😁)
 
 ## Other.py (by Me)
+
+## FixedDropout.py (by Me)
+For EfficientNet model. Example:
+```python
+from Utils.FixedDropout import FixedDropout
+from keras.models import load_model
+
+# Load the model
+model = load_model('PAI_model_T.h5', custom_objects={'FixedDropout': FixedDropout})
+```