src/EmotionRecognition/utils/common.py

import os
from box.exceptions import BoxValueError
import yaml
from EmotionRecognition import logger
import json
from ensure import ensure_annotations
from box import ConfigBox
from pathlib import Path
from typing import Any
import json
import tensorflow as tf

@ensure_annotations
def read_yaml(path_to_yaml: Path) -> ConfigBox:
    """reads yaml file and returns

    Args:
        path_to_yaml (str): path like input

    Raises:
        ValueError: if yaml file is empty
        e: empty file

    Returns:
        ConfigBox: ConfigBox type
    """
    try:
        with open(path_to_yaml) as yaml_file:
            content = yaml.safe_load(yaml_file)
            logger.info(f"yaml file: {path_to_yaml} loaded successfully")
            return ConfigBox(content)
    except BoxValueError:
        raise ValueError("yaml file is empty")
    except Exception as e:
        raise e

@ensure_annotations
def create_directories(path_to_directories: list, verbose=True):
    """create list of directories

    Args:
        path_to_directories (list): list of path of directories
        ignore_log (bool, optional): ignore if multiple dirs is to be created. Defaults to False.
    """
    for path in path_to_directories:
        os.makedirs(path, exist_ok=True)
        if verbose:
            logger.info(f"created directory at: {path}")


def save_json(path: Path, data: dict):
    with open(path, "w") as f:
        json.dump(data, f, indent=4)
    logger.info(f"json file saved at: {path}")



def create_mobilenetv2_model(input_shape, num_classes, dropout_rate, is_training=True): # <--- ADD ARGUMENT
    """
    Builds the MobileNetV2 model with our custom head.
    This centralized function ensures consistency.
    """
    base_model = tf.keras.applications.MobileNetV2(
        input_shape=input_shape, include_top=False, weights='imagenet'
    )
    
    inputs = tf.keras.Input(shape=input_shape)
    # --- CRITICAL CHANGE ---
    # Pass the is_training flag to the base model call
    x = base_model(inputs, training=is_training) 
    # --- END CHANGE ---
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01))(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)
    outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
    
    model = tf.keras.Model(inputs, outputs)
    return model