model.py

import cv2
import numpy as np
import os
from matplotlib import pyplot as plt
import time
import mediapipe as mp

DATA_PATH = os.path.join('dataset')
actions = np.array(['hello', 'thanks', 'iloveyou'])

num_sequences = 40
sequence_length = 30

# Preprocessing the data
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical

label_map = {label: num for num, label in enumerate(actions)}

sequences, labels = [], []
for action in actions:
    for sequence in range(num_sequences):
        window = []
        for frame_num in range(sequence_length):
            res = np.load(os.path.join(DATA_PATH, action, str(sequence), "{}.npy".format(frame_num)))
            window.append(res)
        sequences.append(window)
        labels.append(label_map[action])

# Creating training and testing set
X = np.array(sequences)
y = to_categorical(labels).astype(int)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1)

# Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.callbacks import TensorBoard

log_dir = os.path.join('Logs')
tb_callback = TensorBoard(log_dir=log_dir)

model = Sequential()
model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30, 1662)))
model.add(LSTM(128, return_sequences=True, activation='relu'))
model.add(LSTM(64, return_sequences=False, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(actions.shape[0], activation='softmax'))

model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])

try:
    model.fit(X_train, y_train, epochs=500, callbacks=[tb_callback])
except KeyboardInterrupt:
    print("Training interrupted. Saving model...")
    model.save('saved_model.h5')
    print("Model saved as 'saved_model.h5'")