Update ML setup guide and add model scripts

train_model.py

@@ -0,0 +1,82 @@
+import os
+import tensorflow as tf
+from tensorflow.keras import layers, models, optimizers
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+def create_model(num_classes):
+    # Use MobileNetV2 as the base model
+    base_model = tf.keras.applications.MobileNetV2(
+        input_shape=(64, 64, 3),
+        include_top=False,
+        weights='imagenet'
+    )
+    base_model.trainable = False  # Freeze base model for transfer learning
+
+    model = models.Sequential([
+        base_model,
+        layers.GlobalAveragePooling2D(),
+        layers.Dropout(0.2),
+        layers.Dense(num_classes, activation='softmax')
+    ])
+
+    model.compile(
+        optimizer=optimizers.Adam(),
+        loss='categorical_crossentropy',
+        metrics=['accuracy']
+    )
+    return model
+
+def train(model_type, dataset_root, output_path):
+    # Set parameters based on model type
+    if model_type == 'suit':
+        num_classes = 4
+    elif model_type == 'value':
+        num_classes = 9
+    else:
+        raise ValueError("model_type must be 'suit' or 'value'")
+
+    # Data augmentation as per ML_SETUP_GUIDE.md
+    datagen = ImageDataGenerator(
+        rescale=1./255,
+        rotation_range=15,
+        brightness_range=[0.8, 1.2],
+        validation_split=0.2
+    )
+
+    train_generator = datagen.flow_from_directory(
+        os.path.join(dataset_root, f'{model_type}_model'),
+        target_size=(64, 64),
+        batch_size=32,
+        class_mode='categorical',
+        subset='training'
+    )
+
+    validation_generator = datagen.flow_from_directory(
+        os.path.join(dataset_root, f'{model_type}_model'),
+        target_size=(64, 64),
+        batch_size=32,
+        class_mode='categorical',
+        subset='validation'
+    )
+
+    model = create_model(num_classes)
+    
+    print(f"Training {model_type} model...")
+    model.fit(
+        train_generator,
+        epochs=20,
+        validation_data=validation_generator
+    )
+
+    model.save(output_path)
+    print(f"Model saved to {output_path}")
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--type', choices=['suit', 'value'], required=True)
+    parser.add_argument('--dataset', default='dataset')
+    parser.add_argument('--output', required=True)
+    args = parser.parse_args()
+
+    train(args.type, args.dataset, args.output)