Update ML setup guide and add model scripts

.gitignore

@@ -1,3 +1,7 @@
+/venv
+/dataset
+*.h5
+
 # Dependencies
 node_modules/
 .pnp/
@@ -79,4 +83,4 @@ yarn-cache/
 *.sublime-*
 .sass-cache
 parcel-cache.json
-.pnp.*
+.pnp.*

ML_SETUP_GUIDE.md

@@ -12,7 +12,7 @@ The system uses a **Hybrid Pipeline** to balance performance on mobile devices w
 2.  **Classification**: Crops each detected card and passes it through two specialized TensorFlow.js models to determine the **Suit** and the **Value**.
 
 ## 2. Data Collection & Labeling
-Because Jass cards have unique iconography, we use a custom dataset created from internet samples.
+Because Jass cards have unique iconography, we use a custom dataset.
 
 ### Labeling Strategy: Folder-Based Annotation
 Instead of using bounding-box tools, we use the directory structure as labels.
@@ -29,7 +29,7 @@ dataset/
     ├── 6/
     ├── 7/
     ...
-    └── 13/
+    └── 14/
 ```
 
 ### Process:

convert_model.sh

@@ -0,0 +1,32 @@
+#!/bin/bash
+
+# Check if tensorflowjs is installed
+if ! command -v tensorflowjs_converter &> /dev/null
+then
+    echo "tensorflowjs_converter could not be found. Please install it using: pip install tensorflowjs"
+    exit 1
+fi
+
+MODEL_TYPE=$1
+INPUT_MODEL=$2
+OUTPUT_DIR=$3
+
+if [ -z "$MODEL_TYPE" ] || [ -z "$INPUT_MODEL" ] || [ -z "$OUTPUT_DIR" ]; then
+    echo "Usage: ./convert_model.sh [suit|value] [path_to_h5_model] [output_directory]"
+    exit 1
+fi
+
+echo "Converting $MODEL_TYPE model from $INPUT_MODEL to $OUTPUT_DIR..."
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+# Perform conversion
+tensorflowjs_converter --input_format=keras "$INPUT_MODEL" "$OUTPUT_DIR"
+
+if [ $? -eq 0 ]; then
+    echo "Conversion successful. Model saved to $OUTPUT_DIR"
+else
+    echo "Conversion failed."
+    exit 1
+fi

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)