import src.utils.embedding_utils as embedding_utils
from crosswalk.crosswalk_utils import CrosswalkBuilder
import src.codes.code_funcs as code_funcs

import os
import pandas as pd

from sentence_transformers import SentenceTransformer

# Load model
roberta_model = SentenceTransformer("all-roberta-large-v1")

mapping_dir = "crosswalk/mapping_csvs/proc_cd"
pkl_dir = "embeddings"
for filename in os.listdir(mapping_dir):
    if filename.endswith(".csv") and "level" in filename:
        print(f"Creating embeddings for {filename}")
        stripped_filename = filename.replace(".csv", "")
        mapping_df = pd.read_csv(os.path.join(mapping_dir, filename))
        proc_crosswalk = CrosswalkBuilder().from_df(mapping_df, from_col="Code", to_col="Description")
        proc_choices = [x for x in proc_crosswalk.mapping.values()]
        os.makedirs(os.path.join(pkl_dir, stripped_filename), exist_ok=True)
        proc_index = embedding_utils.create_faiss_index(choices=proc_choices, 
                                                        model=roberta_model, 
                                                        save_path=os.path.join(pkl_dir, stripped_filename, "faiss_index.bin"),
                                                        embedding_path=os.path.join(pkl_dir, stripped_filename, "embeddings.npy"),
                                                        choices_path=os.path.join(pkl_dir, stripped_filename, "choices.pkl")
                                                        )