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app/modelutil.py

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+from __future__ import annotations
+import os
+import tensorflow as tf
+from tensorflow.keras.layers import (Activation, Bidirectional, Conv3D, Dense,
+                                     Dropout, LSTM, MaxPool3D, Reshape)
+from tensorflow.keras.models import Sequential
+
+# Disable all GPUS
+tf.config.set_visible_devices([], 'GPU')
+
+def load_model() -> Sequential:
+    model = Sequential()
+    model.add(Conv3D(128, 3, input_shape=(75, 46, 140, 1), padding='same'))
+    model.add(Activation('relu'))
+    model.add(MaxPool3D((1, 2, 2)))
+
+    model.add(Conv3D(256, 3, padding='same'))
+    model.add(Activation('relu'))
+    model.add(MaxPool3D((1, 2, 2)))
+
+    model.add(Conv3D(75, 3, padding='same'))
+    model.add(Activation('relu'))
+    model.add(MaxPool3D((1, 2, 2)))
+
+    # Reshape instead of TimeDistributed(Flatten) — matches your trained weights
+    model.add(Reshape((75, 5 * 17 * 75)))
+
+    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    model.add(Dropout(.5))
+
+    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    model.add(Dropout(.5))
+
+    model.add(Dense(41, kernel_initializer='he_normal', activation='softmax'))
+
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    weights_path = os.path.abspath(
+        os.path.join(base_dir, '..', 'models', 'checkpoint.weights.h5')
+    )
+    if not os.path.exists(weights_path):
+        raise FileNotFoundError(f"Model weights not found at: {weights_path}")
+
+    model.load_weights(weights_path)
+    return model