Upload train_model.py

train_model.py

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+import os
+# --- FIX: Set environment variable to prevent Protobuf/TensorFlow crash ---
+os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'
+# -----------------------------------------------------------------------
+
+import numpy as np
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from tensorflow.keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# --- 1. Data Loading and Setup ---
+DATA_PATH = os.path.join('ML_Data')
+ACTIONS = np.array(sorted(os.listdir(DATA_PATH)))
+SEQUENCE_LENGTH = 20
+label_map = {label:num for num, label in enumerate(ACTIONS)}
+NUM_CLASSES = len(ACTIONS)
+
+sequences, labels = [], []
+for action in ACTIONS:
+    for sequence in range(15):
+        window = []
+        for frame_num in range(SEQUENCE_LENGTH):
+            try:
+                # Load the 63 features (21 landmarks * 3 coords)
+                res = np.load(os.path.join(DATA_PATH, action, str(sequence), f"{frame_num}.npy"))
+                window.append(res)
+            except:
+                continue
+        if len(window) == SEQUENCE_LENGTH:
+            sequences.append(window)
+            labels.append(label_map[action])
+
+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=0.05)
+
+# --- 2. Define the Neural Network Model (LSTM) ---
+model = Sequential()
+model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(SEQUENCE_LENGTH, 63)))
+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(NUM_CLASSES, activation='softmax'))
+
+# --- 3. Compile, Train, and Save ---
+model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])
+print(f"Starting Training for {NUM_CLASSES} signs...")
+
+model.fit(X_train, y_train, epochs=100, callbacks=[])
+
+res = model.predict(X_test)
+y_pred = np.argmax(res, axis=1)
+y_true = np.argmax(y_test, axis=1)
+print(f"\nModel Accuracy on Test Data: {accuracy_score(y_true, y_pred)*100:.2f}%")
+model.save('/tmp/sign_language_model.h5')
+print("Model trained and saved as sign_language_model.h5")