import glob
import os
import textwrap

import google.cloud.dlp
import openai
from google.cloud import bigquery
from google.cloud.dlp import CharsToIgnore

GCP_PROJECT = os.environ.get("GCP_PROJECT")


def extract_content(file_path):
    content = ""

    with open(file_path, "r", encoding="utf-8") as f:
        lines = f.readlines()
        start_flag = False

        for line in lines:
            if "## Columns" in line:
                start_flag = True
                continue

            if "## Relations" in line:
                start_flag = False
                continue

            if start_flag:
                content += line
    return content


def mask_content(
    project,
    input_str,
    info_types,
    masking_character=None,
    number_to_mask=0,
    ignore_commpn=None,
):
    dlp = google.cloud.dlp_v2.DlpServiceClient()
    parent = f"projects/{project}"
    item = {"value": input_str}

    inspect_config = {"info_types": [{"name": info_type} for info_type in info_types]}
    deidentify_config = {
        "info_type_transformations": {
            "transformations": [
                {
                    "primitive_transformation": {
                        "character_mask_config": {
                            "masking_character": masking_character,
                            "number_to_mask": number_to_mask,
                            "characters_to_ignore": [
                                {"common_characters_to_ignore": ignore_commpn}
                            ],
                        }
                    }
                }
            ]
        }
    }

    response = dlp.deidentify_content(
        request={
            "parent": parent,
            "deidentify_config": deidentify_config,
            "inspect_config": inspect_config,
            "item": item,
        }
    )
    return response.item.value


def get_records(dataset_id, table_id, record_limit=10):
    client = bigquery.Client()
    table_ref = client.dataset(dataset_id).table(table_id)
    table = client.get_table(table_ref)

    query = f"SELECT * FROM `{table.project}.{table.dataset_id}.{table.table_id}` LIMIT {record_limit}"
    query_job = client.query(query)

    return query_job.result()


def replace_columns_to_relations(file_path, replacement_content):
    with open(file_path, "r") as file:
        content = file.read()

    start_tag = "## Columns"
    end_tag = "## Relations"

    # 置き換える範囲を見つける
    start = content.find(start_tag) + len(start_tag)
    end = content.find(end_tag)

    # 置き換える範囲を指定の値に置き換える
    modified_content = (
        content[:start]
        + "\n"
        + replacement_content
        + "\n"
        + "*Descritpion列はGPT-4で生成したものです*"
        + "\n"
        + content[end:]
    )

    # 置き換えた内容をファイルに書き戻す
    with open(file_path, "w") as file:
        file.write(modified_content)


def has_done(data):
    lines = data.strip().split("\n")
    for line in lines[2:]:
        # split the line into cells
        cells = line.split("|")

        return cells[-1].strip() != ""


def main():
    target_directory = f"{GCP_PROJECT}/*/*.md"
    file_paths = glob.glob(target_directory)

    datasets = []
    for file_path in file_paths:
        print(f"Processing file: {file_path}")
        paths = file_path.split(os.sep)
        content = extract_content(file_path)
        dataset = paths[1]
        if "test" in dataset:
            continue
        table = paths[2].split(".")[0]
        datasets.append(
            {
                "dataset": dataset,
                "table": table,
                "content": content,
                "file_path": file_path,
            }
        )

    for dataset in datasets:
        if has_done(dataset["content"]):
            print(f"Already done. Skip. {dataset['file_path']}")
            continue
        masked_rows = []
        for row in get_records(dataset["dataset"], dataset["table"], 10):
            masked_rows.append(
                mask_content(
                    GCP_PROJECT,
                    ",".join([str(value)[:50] for value in row]),
                    [
                        "PERSON_NAME",
                        "EMAIL_ADDRESS",
                        "PHONE_NUMBER",
                        "CREDIT_CARD_NUMBER",
                        "LOCATION",
                        "MALE_NAME",
                        "FEMALE_NAME",
                        "AUTH_TOKEN",
                        "AWS_CREDENTIALS",
                        "BASIC_AUTH_HEADER",
                        "GCP_API_KEY",
                        "ENCRYPTION_KEY",
                        "GCP_CREDENTIALS",
                        "OAUTH_CLIENT_SECRET",
                        "PASSWORD",
                        "JAPAN_BANK_ACCOUNT",
                    ],
                    masking_character="*",
                    ignore_commpn=CharsToIgnore.CommonCharsToIgnore.PUNCTUATION.value,
                )
            )

        context = """
        ## 入力仕様
        - あるテーブルの構造をマークダウンで表現したものと、そのテーブルのサンプルデータ10件をCSVで入力します。

        ## 指示
        - テーブルの構造のマークダウンのDescriptionを埋めて、マークダウンで定義されたテーブルの修正後の内容だけを返却してください。
        - 日本語で回答してください。

        ## テーブルの構造
        {table_struct}
        ## サンプルデータ
        {sample_data}
        """

        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[
                {
                    "role": "user",
                    "content": textwrap.dedent(context).format(
                        table_struct=dataset["content"],
                        sample_data="\n".join(masked_rows),
                    ),
                },
            ],
        )

        print("result: ", response.choices[0]["message"]["content"])
        replace_columns_to_relations(
            dataset["file_path"], response.choices[0]["message"]["content"]
        )
        print(f"Done. {dataset['file_path']}")


if __name__ == "__main__":
    main()