import json
import os
import pandas as pd


def convert_json_to_dataframe() -> None:
    directory = "source\Data"

    df_all_global_info = pd.DataFrame()
    df_all_languages = pd.DataFrame()
    df_all_main_skills = pd.DataFrame()
    df_all_skills = pd.DataFrame()
    df_all_projects = pd.DataFrame()
    df_all_project_tasks = pd.DataFrame()
    df_all_emphasis = pd.DataFrame()
    df_all_publications = pd.DataFrame()
    df_all_certificates = pd.DataFrame()

    for entry in os.scandir(directory):
        if entry.is_file() and entry.name.endswith(".json"):
            path = entry.path
            short = entry.name.removesuffix(".json")

        with open(path, "r") as f:
            data = json.loads(f.read())

        # collects all global information on a person
        df_global_info = pd.json_normalize(data.get("global", []))
        df_global_info["graduation"] = data.get("de").get("graduation")
        df_global_info["career"] = data.get("de").get("career")
        if hasattr(df_global_info, "yearOfBirth"):
            df_global_info = df_global_info.drop(["yearOfBirth"], axis=1)
        df_global_info["short"] = short
        df_all_global_info = pd.concat([df_global_info, df_all_global_info])

        # collects all the languages a person speaks
        df_languages = pd.DataFrame(data["de"]["languages"], columns=["language"])
        df_languages["short"] = short
        df_all_languages = pd.concat([df_languages, df_all_languages])

        # collects the main skills and the according scores
        df_main_skills = pd.json_normalize(data["de"]["mainSkills"])
        try:
            main_skill1 = df_main_skills[["mainSkill1Title", "mainSkill1Quality"]]
            main_skill1 = main_skill1.rename(
                {"mainSkill1Title": "skillName", "mainSkill1Quality": "skillLevel"},
                axis=1,
            )
        except:
            main_skill1 = pd.DataFrame()
        try:
            main_skill2 = df_main_skills[["mainSkill2Title", "mainSkill2Quality"]]
            main_skill2 = main_skill2.rename(
                {"mainSkill2Title": "skillName", "mainSkill2Quality": "skillLevel"},
                axis=1,
            )
        except:
            main_skill2 = pd.DataFrame()
        try:
            main_skill3 = df_main_skills[["mainSkill3Title", "mainSkill3Quality"]]
            main_skill3 = main_skill3.rename(
                {"mainSkill3Title": "skillName", "mainSkill3Quality": "skillLevel"},
                axis=1,
            )
        except:
            main_skill3 = pd.DataFrame()
        df_main_skills = pd.concat([main_skill1, main_skill2, main_skill3])
        df_main_skills["short"] = short
        df_all_main_skills = pd.concat([df_main_skills, df_all_main_skills])

        # collects all skills by skill type
        df_skill_all = pd.json_normalize(data.get("de", []).get("skills", []))

        df_skills = (
            pd.DataFrame(df_skill_all.items(), columns=["skillType", "skill"])
            .explode("skill")
            .explode("skill")
        )
        df_skills["short"] = short
        df_all_skills = pd.concat([df_skills, df_all_skills])

        # collects all projects
        df_projects = pd.json_normalize(data.get("de", []).get("projects", []))
        if hasattr(df_projects, "favorite"):
            df_projects = df_projects.drop(["favorite"], axis=1)
        if hasattr(df_projects, "tasks"):
            df_projects = df_projects.drop(["tasks"], axis=1)
        df_projects["short"] = short
        df_all_projects = pd.concat([df_projects, df_all_projects])

        # collects all tasks per project and person
        df_project_tasks = pd.json_normalize(
            data.get("de", []).get("projects", []),
            record_path=["tasks"],
            meta=["title"],
        )
        df_project_tasks.rename({0: "task"}, axis=1, inplace=True)
        df_project_tasks["short"] = short
        df_all_project_tasks = pd.concat([df_project_tasks, df_all_project_tasks])

        # collects all tasks per main emphasis
        df_emphasis_all = pd.json_normalize(data.get("de", []).get("emphasis", []))
        df_emphasis = (
            pd.DataFrame(df_emphasis_all.items(), columns=["emphasis", "skill"])
            .explode("skill")
            .explode("skill")
        )
        df_emphasis["short"] = short
        df_all_emphasis = pd.concat([df_emphasis, df_all_emphasis])

        # collects all publications from a person
        # if there are no publications, an empty list is created
        df_publications = pd.json_normalize(data.get("de", []).get("publications", []))
        df_publications["short"] = short
        df_all_publications = pd.concat([df_publications, df_all_publications])

        # collects all certificates a person has
        df_certificates = pd.json_normalize(data.get("de", []).get("certificates", []))
        df_certificates["short"] = short
        df_all_certificates = pd.concat([df_certificates, df_all_certificates])

    df_all_global_info.to_csv("source\Tables\global_info.csv")
    df_all_languages.to_csv("source\Tables\languages.csv")
    df_all_main_skills.to_csv("source\Tables\main_skills.csv")
    df_all_skills.to_csv("source\Tables\skills.csv")
    df_all_projects.to_csv("source\Tables\projects.csv")
    df_all_project_tasks.to_csv("source\Tables\project_tasks.csv")
    df_all_emphasis.to_csv("source\Tables\emphasis.csv")
    df_all_publications.to_csv("source\Tables\publications.csv")
    df_all_certificates.to_csv("source\Tables\certificates.csv")