import os

# Base directory
BASE_DIR = os.path.dirname(os.path.abspath(__file__))

# Model Repositories (Hugging Face)
PATH_FOUNDATION_ID = "google/path-foundation"
MEDSIGLIP_ID = "google/medsiglip-448"
MEDGEMMA_ID = "google/medgemma-1.5-4b-it"

# Feature Dimensions
DIM_PATH = 384
DIM_SIGLIP = 1152
DIM_FUSED = DIM_PATH + DIM_SIGLIP # 1536

# Training Hyperparameters
RANDOM_SEED = 42
EPOCHS = 10
BATCH_SIZE = 16
LEARNING_RATE = 1e-3
WEIGHT_DECAY = 1e-4

# Directories
MODELS_DIR = os.path.join(BASE_DIR, "weights")
DATA_DIR = os.path.join(BASE_DIR, "data")
OUTPUT_DIR = os.path.join(BASE_DIR, "output")

for d in [MODELS_DIR, DATA_DIR, OUTPUT_DIR]:
    os.makedirs(d, exist_ok=True)

# FAISS Configuration
FAISS_INDEX_PATH = os.path.join(MODELS_DIR, "cases_faiss.index")
METADATA_PATH = os.path.join(DATA_DIR, "cases_metadata.json")

# Classification Labels
LABEL_MAP = {
    0: "MSS",
    1: "MSI-High"
}

# Force Demo/Simulation Mode (True if CPU only or memory constrained)
FORCE_SIMULATION = True