"""
Configuration settings for the Emotion Recognition System.
"""
import os
from pathlib import Path

# Project paths
PROJECT_ROOT = Path(__file__).parent.parent
DATA_DIR = PROJECT_ROOT / "data"
TRAIN_DIR = DATA_DIR / "train"
TEST_DIR = DATA_DIR / "test"
MODELS_DIR = PROJECT_ROOT / "models"

# Create models directory if it doesn't exist
MODELS_DIR.mkdir(exist_ok=True)

# Image settings
IMAGE_SIZE = (48, 48)
IMAGE_SIZE_TRANSFER = (96, 96)  # For transfer learning models
NUM_CHANNELS = 1  # Grayscale
NUM_CHANNELS_RGB = 3  # For transfer learning

# Emotion classes (7 classes from FER dataset)
EMOTION_CLASSES = [
    "angry",
    "disgusted",
    "fearful",
    "happy",
    "neutral",
    "sad",
    "surprised"
]
NUM_CLASSES = len(EMOTION_CLASSES)

# Emotion to index mapping
EMOTION_TO_IDX = {emotion: idx for idx, emotion in enumerate(EMOTION_CLASSES)}
IDX_TO_EMOTION = {idx: emotion for idx, emotion in enumerate(EMOTION_CLASSES)}

# Training hyperparameters
BATCH_SIZE = 64
EPOCHS = 50
LEARNING_RATE = 0.001
LEARNING_RATE_FINE_TUNE = 0.0001
VALIDATION_SPLIT = 0.2

# Data augmentation parameters
AUGMENTATION_CONFIG = {
    "rotation_range": 15,
    "width_shift_range": 0.1,
    "height_shift_range": 0.1,
    "horizontal_flip": True,
    "zoom_range": 0.1,
    "brightness_range": (0.9, 1.1),
    "fill_mode": "nearest"
}

# Model save paths
CUSTOM_CNN_PATH = MODELS_DIR / "custom_cnn.h5"
MOBILENET_PATH = MODELS_DIR / "mobilenet_v2.h5"
VGG_PATH = MODELS_DIR / "vgg19.h5"

# Training callbacks
EARLY_STOPPING_PATIENCE = 10
REDUCE_LR_PATIENCE = 5
REDUCE_LR_FACTOR = 0.5

# Intensity thresholds
INTENSITY_HIGH_THRESHOLD = 0.8
INTENSITY_MEDIUM_THRESHOLD = 0.5

# API settings
API_HOST = "0.0.0.0"
API_PORT = 8000

# Streamlit settings
STREAMLIT_PORT = 8501