made so code can be trained on GPU

• update readme to install environment

Func/ClassifierModule.py

 import keras
 import numpy as np
 import cv2
+from cvzone.HandTrackingModule import HandDetector
 
 
 class Classifier:
@@ -20,7 +21,8 @@ class Classifier:
         else:
             print("No Labels Found")
 
-    def getPrediction(self, img, draw=True, pos=(50, 50), scale=2, color=(0, 255, 0)):
+    def getPrediction(self, img):
+        cv2.imshow("imgFixed classifier", img)
         img_resized = cv2.resize(img, (224, 224))
         image_array = np.asarray(img_resized)
         normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
@@ -30,30 +32,29 @@ class Classifier:
         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)
+    detector = HandDetector(maxHands=1)
 
     mask_classifier = Classifier('Model/keras_model.h5', 'Model/labels.txt')
 
     while True:
         ret, frame = cap.read()
+        hands, img = detector.findHands(frame)
+        
 
         if not ret:
             print("Failed to capture frame")
             break
 
-        prediction = mask_classifier.getPrediction(frame)
+        prediction = mask_classifier.getPrediction(img)
 
         print("Prediction:", prediction)
 
-        cv2.imshow("Image", frame)
+
 
         if cv2.waitKey(1) & 0xFF == ord('q'):
             break