import torch

import cv2

import numpy as np

import mediapipe as mp

def extract_keypoints(results):

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)

return np.concatenate([pose, face])

# Load the trained model

model = torch.load('gloss_to_pose.pth')

model.eval()

while True:

ret, frame = cap.read()

# Processing frame through MediaPipe

predictions = model(features)

cv2.imshow('AI Sign Language', frame)

# Wait for exit

if cv2.waitKey(1) & 0xFF == ord('q'):

break