rectified indentation

app.py

@@ -1,11 +1,12 @@
 # Import the required libraries
-import cv2
-import numpy as np
 import os
+
+import cv2
 import gradio as gr
+import mediapipe as mp
+import numpy as np
 import tensorflow as tf
 import tensorflow.lite as tflite
-import mediapipe as mp
 
 # Initialize MediaPipe solutions
 mp_holistic = mp.solutions.holistic
@@ -21,32 +22,36 @@ model_path = os.path.join(current_dir, model_filename)
 interpreter = tf.lite.Interpreter(model_path=model_path)
 interpreter.allocate_tensors()
 
+
 # ... (other functions from previous code)
 
 # Function to perform holistic detection using Mediapipe
 def mediapipe_detection(image, model):
     # COLOR CONVERSION BGR 2 RGB
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    image_rgb.flags.writeable = False                  # Image is no longer writeable
-    results = model.process(image_rgb)                 # Make prediction
-    image_rgb.flags.writeable = True                   # Image is now writeable
+    image_rgb.flags.writeable = False  # Image is no longer writeable
+    results = model.process(image_rgb)  # Make prediction
+    image_rgb.flags.writeable = True  # Image is now writeable
     image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)  # COLOR CONVERSION RGB 2 BGR
     return image_bgr, results
 
 
-
 # Function to extract keypoints from Mediapipe results
 def extract_keypoints(results):
     lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten(
-    ) if results.left_hand_landmarks else np.zeros(21*3)
+    ) if results.left_hand_landmarks else np.zeros(21 * 3)
     rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten(
-    ) if results.right_hand_landmarks else np.zeros(21*3)
+    ) if results.right_hand_landmarks else np.zeros(21 * 3)
     pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten(
-    ) if results.pose_landmarks else np.zeros(33*4)
-    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten(
-    ) if results.face_landmarks else np.zeros(468*3)
+    ) if results.pose_landmarks else np.zeros(33 * 4)
+    face = np.array([[res.x, res.y, res.z] for res in
+                     results.face_landmarks.landmark]).flatten(
+    ) if results.face_landmarks else np.zeros(468 * 3)
     return
- np.concatenate([lh, rh, pose, face])
+
+
+np.concatenate([lh, rh, pose, face])
+
 
 # Main prediction function that combines everything
 def predict_with_webcam(frame):