more updates

docs/data-ai/ai/train.md

@@ -63,7 +63,7 @@ my-training/
 
 Add the following code to `setup.py` and add additional required packages on line 11:
 
-```python {class="line-numbers linkable-line-numbers" data-line="11"}
+```python {class="line-numbers linkable-line-numbers" data-line="9"}
 from setuptools import find_packages, setup
 
 setup(
@@ -92,11 +92,13 @@ If you haven't already, create a folder called <file>model</file> and create an
 
 {{% expand "Click to see the template" %}}
 
-```python {class="line-numbers linkable-line-numbers" data-line="126,170" }
+```python {class="line-numbers linkable-line-numbers" data-line="139" }
 import argparse
 import json
 import os
 import typing as ty
+from tensorflow.keras import Model  # Add proper import
+import tensorflow as tf # Add proper import
 
 single_label = "MODEL_TYPE_SINGLE_LABEL_CLASSIFICATION"
 multi_label = "MODEL_TYPE_MULTI_LABEL_CLASSIFICATION"
@@ -106,32 +108,37 @@ unknown_label = "UNKNOWN"
 API_KEY = os.environ['API_KEY']
 API_KEY_ID = os.environ['API_KEY_ID']
 
+DEFAULT_EPOCHS = 200
 
 # This parses the required args for the training script.
 # The model_dir variable will contain the output directory where
 # the ML model that this script creates should be stored.
 # The data_json variable will contain the metadata for the dataset
 # that you should use to train the model.
 def parse_args():
-    """Returns dataset file, model output directory, and num_epochs if present.
+    """Returns dataset file, model output directory, labels, and num_epochs if present.
     These must be parsed as command line arguments and then used as the model
     input and output, respectively. The number of epochs can be used to
     optionally override the default.
     """
     parser = argparse.ArgumentParser()
-    parser.add_argument("--dataset_file", dest="data_json", type=str)
-    parser.add_argument("--model_output_directory", dest="model_dir", type=str)
+    parser.add_argument("--dataset_file", dest="data_json", type=str, required=True)
+    parser.add_argument("--model_output_directory", dest="model_dir", type=str, required=True)
     parser.add_argument("--num_epochs", dest="num_epochs", type=int)
     parser.add_argument(
         "--labels",
         dest="labels",
         type=str,
-        required=False,
+        required=True,
         help="Space-separated list of labels, enclosed in single quotes (e.g., 'label1 label2').",
     )
     args = parser.parse_args()
-    return args.data_json, args.model_dir, args.num_epochs, args.labels
-
+
+    if not args.labels:
+        raise ValueError("Labels must be provided")
+
+    labels = [label.strip() for label in args.labels.strip("'").split()]
+    return args.data_json, args.model_dir, args.num_epochs, labels
 
 # This is used for parsing the dataset file (produced and stored in Viam),
 # parse it to get the label annotations
@@ -215,8 +222,7 @@ def parse_filenames_and_bboxes_from_json(
 
 # Build the model
 def build_and_compile_model(
-    labels: ty.List[str], model_type: str, input_shape: ty.Tuple[int, int, int]
-) -> Model:
+    labels: ty.List[str], model_type: str, input_shape: ty.Tuple[int, int, int]) -> Model:
     """Builds and compiles a model
     Args:
         labels: list of string lists, where each string list contains up to
@@ -255,12 +261,16 @@ def save_model(
         model_dir: output directory for model artifacts
         model_name: name of saved model
     """
-    file_type = ""
-
-    # Save the model to the output directory.
+    file_type = "tflite"  # Add proper file type
     filename = os.path.join(model_dir, f"{model_name}.{file_type}")
+
+    # Example: Convert to TFLite
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    tflite_model = converter.convert()
+
+    # Save the model
     with open(filename, "wb") as f:
-        f.write(model)
+        f.write(tflite_model)
 
 
 if __name__ == "__main__":
@@ -275,14 +285,19 @@ if __name__ == "__main__":
     image_filenames, image_labels = parse_filenames_and_labels_from_json(
         DATA_JSON, LABELS, model_type)
 
+    # Validate epochs
+    epochs = (
+        DEFAULT_EPOCHS if NUM_EPOCHS is None or NUM_EPOCHS <= 0 else int(NUM_EPOCHS)
+    )
+
     # Build and compile model on data
-    model = build_and_compile_model()
+    model = build_and_compile_model(image_labels, model_type, IMG_SIZE + (3,))
 
     # Save labels.txt file
     save_labels(LABELS + [unknown_label], MODEL_DIR)
     # Convert the model to tflite
     save_model(
-        model, MODEL_DIR, "classification_model", IMG_SIZE + (3,)
+        model, MODEL_DIR, "classification_model"
     )
 ```