from typing import Dict, Any

# FastText model paths configuration
FASTTEXT_CONFIG = {
    "use_huggingface": True,  # Set to True in production
    "repo_id": "talhasarit41/fasttext",  # HuggingFace repository ID
    "huggingface_paths": {
        "fasttext_default": "fasttext_raw.bin",
        "fasttext_preprocessed": "fasttext_preprocessed.bin",
        "word_n_gram_1": "word_n_gram_1.bin",
        "word_n_gram_2": "word_n_gram_2.bin",
        "word_n_gram_3": "word_n_gram_3.bin",
        "low_overfit": "low_overfit.bin"
    },
    "local_paths": {
        "fasttext_default": "/home/seeknndestroy/jetlink/bitbucket/fasttext_related/saved_models/fasttext_raw.bin",
        "fasttext_preprocessed": "/home/seeknndestroy/jetlink/bitbucket/fasttext_related/saved_models/fasttext_preprocessed.bin",
        "word_n_gram_1": "/home/seeknndestroy/jetlink/bitbucket/fasttext_related/saved_models/manual_configs/word_n_gram_1.bin",
        "word_n_gram_2": "/home/seeknndestroy/jetlink/bitbucket/fasttext_related/saved_models/manual_configs/word_n_gram_2.bin",
        "word_n_gram_3": "/home/seeknndestroy/jetlink/bitbucket/fasttext_related/saved_models/manual_configs/word_n_gram_3.bin",
        "low_overfit": "/home/seeknndestroy/jetlink/bitbucket/fasttext_related/saved_models/manual_configs/low_overfit.bin"
    }
}

# Model enablement configuration
MODEL_CONFIG = {
    "FastText Default": True,
    "Fasttext Low Overfit": True,
    "Fasttext WordnNGram 1": True,
    "Fasttext WordnNGram 2": True,
    "Fasttext WordnNGram 3": True,
    "E5 Classifier": False,
    "E5-Instruct Classifier": False,
    "Azure Classifier": False,
    "Azure KNN Classifier": False,
    "GTE Classifier": False
}

def get_fasttext_path(model_name: str) -> str:
    """Get the appropriate FastText model path based on configuration."""
    if FASTTEXT_CONFIG["use_huggingface"]:
        from huggingface_hub import hf_hub_download
        return hf_hub_download(
            repo_id=FASTTEXT_CONFIG["repo_id"],
            filename=FASTTEXT_CONFIG["huggingface_paths"][model_name]
        )
    else:
        return FASTTEXT_CONFIG["local_paths"][model_name]

def is_model_enabled(model_name: str) -> bool:
    """Check if a model is enabled in the configuration."""
    return MODEL_CONFIG.get(model_name, False)