Alpha b

.gitignore

@@ -96,9 +96,13 @@ Samples/*
 /Temp
 /Test.md
 /Interface/GUI/Data/model_info.json
+/ruff_check_results.txt
+/ruff_format_results.txt
 /Load_Backup.py
 
 # Exclude backup load
 /Backup_*_COPY.ipynb
-/ruff_check_results.txt
-/ruff_format_results.txt
+
+# Temp
+/history/Archive
+/Test_ENV_G.ipynb

README.md

@@ -72,11 +72,11 @@ The model is a Convolutional Neural Network (CNN) trained on a dataset of 23681
 This combined dataset provides a comprehensive set of images for training the model.\
 
 ### Model list:
-| Model    | Base Model      | Params | acc    | Status |
-|----------|-----------------|--------|--------|--------|
-| V6       | efficientnet-b7 | 65.4M  | 97.12% |   ✅   |
-| V7 light | efficientnet-b4 | 29.7M  | 97.12% |   ✅  |
-| V8 Super light | efficientnet-b0 | 4.8M  | 96.47% |   ✅  |
+| Model    | Base Model      | Params | acc    | Status | Grad Cam |
+|----------|-----------------|--------|--------|--------|----------|
+| V6       | efficientnet-b7 | 65.4M  | 97.12% |   ✅   |  ✅  | 
+| V7 light | efficientnet-b4 | 29.7M  | 97.12% |   ✅   |  ⚠️  | 
+| V8 Super light | efficientnet-b0 | 4.8M  | 96.47% |  ✅  |   ❌  | 
 
 ## Training Methods
 ### The AI model supports two distinct training approaches:

Test_ENV_G.ipynb

@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T\n",
+      "Timeout!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'user_input': 'N/A', 'input_time': 5, 'default_var_used': True}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from Utils.Timeout_input import TimeoutInput\n",
+    "\n",
+    "TimeoutInput('T', 5, 'N/A').run()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Utils/Other.py

@@ -128,3 +128,38 @@ def evaluate_model_full(y_test, model_pred, model=None, x_test=None):
 
     # Print the table
     print(tabulate(metrics, headers=["Metric", "Value"], tablefmt="pretty"))
+
+
+def set_optimizer_attribute(optimizer, attribute, value, verbose: bool = False):
+    """Sets an attribute on the given optimizer to the specified value.
+
+    Args:
+        optimizer: The optimizer instance to modify.
+        attribute: The attribute name to set.
+        value: The value to set the attribute to.
+        verbose: Whether to print a message if the attribute does not exist.
+    """
+    if hasattr(optimizer, attribute):
+        setattr(optimizer, attribute, value)
+    else:
+        if verbose:
+            print(f"The optimizer does not have an attribute named '{attribute}'")
+
+
+def print_optimizer_info(model):
+    """Prints information about the optimizer used by a Keras model.
+
+    Prints the optimizer class name and its parameter values. Useful
+    for inspecting optimizer configuration.
+
+    Args:
+        model: The Keras model whose optimizer to print info for.
+
+    """
+    if model.optimizer:
+        print_Color(f"~*Optimizer: ~*{model.optimizer.__class__.__name__}", ["cyan", "green"], advanced_mode=True)
+        print_Color(" <Opt> Parameters:", ["cyan"])
+        for param, value in model.optimizer.get_config().items():
+            print_Color(f"~* <Opt> -- ~*{param}: ~*{value}", ["cyan", "light_cyan", "green"], advanced_mode=True)
+    else:
+        print_Color("No optimizer found in the model.", ["red"])

Utils/README.md

@@ -17,6 +17,8 @@
 
 ## Other.py (by Me)
 
+## Timeout_input.py (by Me)
+
 ## FixedDropout.py (by Me)
 For EfficientNet model Example:
 ```python

Utils/Timeout_input.py

@@ -0,0 +1,59 @@
+import threading
+import queue
+import keyboard
+
+
+class TimeoutInput:
+    """
+    A class to get user input with a timeout.
+
+    Attributes:
+        prompt (str): The prompt to display to the user.
+        timeout (int): The time in seconds to wait for user input.
+        default_var (str): The default value to return if the user does not provide input.
+        timeout_message (str): The message to display when the input times out.
+    """
+
+    def __init__(self, prompt, timeout, default_var, timeout_message="\nTimeout!"):
+        self.prompt = prompt
+        self.timeout = timeout
+        self.default_var = default_var
+        self.timeout_message = timeout_message
+        self.user_input = None
+        self.input_queue = queue.Queue()
+        self.stop_thread = False
+
+    def get_input(self):
+        """Get user input in a non-blocking manner."""
+        print(self.prompt, end="", flush=True)
+        while not self.stop_thread:
+            if keyboard.is_pressed("\n"):
+                line = input()
+                if line:
+                    self.input_queue.put(line.strip())
+                    return
+
+    def run(self):
+        """
+        Run the TimeoutInput.
+
+        Starts a thread to get user input and waits for the specified timeout.
+        If the user does not provide input within the timeout, returns the default value.
+        """
+        thread = threading.Thread(target=self.get_input)
+        thread.start()
+        thread.join(self.timeout)
+        if thread.is_alive():
+            self.stop_thread = True
+            print(self.timeout_message)
+            return {"user_input": self.default_var, "input_time": self.timeout, "default_var_used": True}
+        else:
+            self.user_input = self.input_queue.get()
+            return {"user_input": self.user_input, "input_time": self.timeout, "default_var_used": False}
+
+
+# Example usage
+if __name__ == "__main__":
+    timeout_input = TimeoutInput("Enter something: ", 5, "default", "\nTimeout from TimeoutInput!")
+    result = timeout_input.run()
+    print(result)

Utils/lr_find.py

@@ -1,13 +1,10 @@
+import keras
 import tempfile
-
 import matplotlib.pyplot as plt
 import numpy as np
-
 import tensorflow as tf
-from tensorflow import keras
 from tqdm.auto import tqdm
-
-K = keras.backend
+import keras.src.legacy.backend as K
 
 
 class Scheduler:
@@ -59,9 +56,9 @@ class LrFinder:
 
     def __init__(
         self,
-        model: tf.keras.Model,
-        optimizer: tf.keras.optimizers.Optimizer,
-        loss_fn: tf.keras.losses.Loss,
+        model: keras.Model,
+        optimizer: keras.optimizers.Optimizer,
+        loss_fn: keras.losses.Loss,
     ) -> None:
         self.lrs = []
         self.losses = []

Utils/one_cycle.py

@@ -1,9 +1,7 @@
-from tensorflow import keras
+import keras
 import math
 import matplotlib.pyplot as plt
-
-K = keras.backend
-
+import keras.src.legacy.backend as K
 
 class OneCycleLr(keras.callbacks.Callback):
     """