#!/usr/bin/env python3

import argparse
from pathlib import Path
from pprint import pprint

import pandas as pd

import LogParser
import utils
import tab_switch

argparser = argparse.ArgumentParser(description="OCR-Logfile evaluation")
argparser.add_argument("vp_dir", help="Directory with all VPs")

args = argparser.parse_args()

data_path = Path(args.vp_dir)
all_vp = [x for x in data_path.iterdir() if x.is_dir()]

vp_results = []
tab_results = []
log_tab_results = []
for vp_path in all_vp:
    log = LogParser.extract_activities(LogParser.get_log_data(vp_path))
    log = LogParser.generate_log(log)
    history = LogParser.get_history_db(vp_path)
    log = LogParser.match_urls(history, log)
    LogParser.write_logfile(vp_path, log)

    video_path = next(vp_path.glob("*.mkv"))
    ocr_path = vp_path / "analysis_results.csv"
    log_path = vp_path / "logs.csv"

    df = utils.combine_ocr_logs(video_path, ocr_path, log_path)
    df = df.fillna("")
    df["vp_code"] = vp_path.stem

    df = utils.calc_levenshtein_distance(df)

    url_groups = utils.group_urls(list(df["url"].values))
    pprint(len(url_groups))

    df.to_csv(f"{vp_path}/metrics.csv")
    utils.write_grouped_metrics(df, url_groups, vp_path)

    df = pd.read_csv(f"{vp_path}/metrics_grps.csv")

    tab_df = tab_switch.tab_switches_per_type(df, "group_index")
    tab_results.append(tab_df)
    tab_df.to_csv(f"{vp_path}/tabswitches.csv")

    log_tab_df = tab_switch.tab_switches_per_type(df, "log_url")
    log_tab_results.append(log_tab_df)
    log_tab_df.to_csv(f"{vp_path}/log_tabswitches.csv")

    vp_results.append(df)

evals = utils.evaluate_results(tab_results, log_tab_results)
evals.to_csv(f"{data_path}/evaluation.csv")