made so code can be trained on GPU

• update readme to install environment

Func/ClassifierModule.py

+import keras
+import numpy as np
+import cv2
+
+
+class Classifier:
+
+    def __init__(self, model_path, label_path):
+        self.model_path = model_path
+
+        np.set_printoptions(suppress=True)
+
+        self.model = keras.models.load_model(self.model_path)
+        self.data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+        self.labels_path = label_path
+
+        if self.labels_path:
+            with open(self.labels_path, "r") as label_file:
+                self.list_labels = [line.strip() for line in label_file]
+        else:
+            print("No Labels Found")
+
+    def getPrediction(self, img, draw=True, pos=(50, 50), scale=2, color=(0, 255, 0)):
+        img_resized = cv2.resize(img, (224, 224))
+        image_array = np.asarray(img_resized)
+        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
+        self.data[0] = normalized_image_array
+
+        prediction = self.model.predict(self.data)
+        index_val = np.argmax(prediction)
+
+        if draw and self.labels_path:
+            label_text = str(self.list_labels[index_val])
+            cv2.putText(img, label_text, pos, cv2.FONT_HERSHEY_COMPLEX, scale, color, 2)
+
+        return list(prediction[0]), index_val
+
+
+def main():
+    cap = cv2.VideoCapture(0)
+
+    mask_classifier = Classifier('Model/keras_model.h5', 'Model/labels.txt')
+
+    while True:
+        ret, frame = cap.read()
+
+        if not ret:
+            print("Failed to capture frame")
+            break
+
+        prediction = mask_classifier.getPrediction(frame)
+
+        print("Prediction:", prediction)
+
+        cv2.imshow("Image", frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()