import os

import pandas as pd
from dotenv import load_dotenv

from calculate_personal_scores import get_discord_username

load_dotenv()

# Constants
DATA_DIR = "data"
PARTICIPANTS_CSV = os.path.join(DATA_DIR, "participants.csv")
EQUIPOS_CSV = os.path.join(DATA_DIR, "equipos.csv")
LEADERBOARD_PERSONAL_CSV = "leaderboard_personal.csv"
LEADERBOARD_EQUIPOS_CSV = "leaderboard_equipos.csv"

# Column mappings for teams info
TEAM_COLUMNS = {
    "team_name": "Nombre del equipo",
    "email_1": "Email 1",
    "email_2": "Email 2",
    "email_3": "Email 3",
    "email_4": "Email 4",
    "email_5": "Email 5",
}

# INCLUDE: Cada 100 preguntas validadas extraídas por equipo = 0.5 ptos (máx 1 pto)
# Estereotipos: Cada 10 estereotipos validados por equipo = 0.1 ptos (máx 1 pto)
# BLEND: Cada 50 preguntas por equipo = 0.5 ptos (máx 2 ptos)
# Arena: Cada 50 prompts por equipo = 0.5 ptos (máx 2 ptos)

POINTS = {
    "INCLUDE": {"points": 0.5, "max_points": 1, "questions": 100},
    "ESTEREOTIPOS": {"points": 0.1, "max_points": 1, "questions": 10},
    "BLEND": {"points": 0.5, "max_points": 2, "questions": 50},
    "ARENA": {"points": 0.5, "max_points": 2, "questions": 50},
}


def calculate_challenge_points(total_score, challenge_name):
    """Calculate points for a specific challenge based on the POINTS rules."""
    if challenge_name not in POINTS:
        return 0

    challenge_rules = POINTS[challenge_name]
    points_per_unit = challenge_rules["points"]
    max_points = challenge_rules["max_points"]
    questions_per_unit = challenge_rules["questions"]

    calculated_points = (total_score / questions_per_unit) * points_per_unit
    final_points = min(calculated_points, max_points)
    return round(final_points, 2)


def get_team_leaderboard(personal_leaderboard_df):
    """Calculate team leaderboard based on personal scores."""
    if not os.path.exists(EQUIPOS_CSV):
        return pd.DataFrame()

    try:
        teams_df = pd.read_csv(EQUIPOS_CSV)
        team_leaderboard = []

        for _, team_row in teams_df.iterrows():
            team_name = team_row.get(TEAM_COLUMNS["team_name"], "")
            if not team_name:
                continue

            # Get team member emails
            team_emails = []
            for i in range(1, 6):
                email_col = TEAM_COLUMNS[f"email_{i}"]
                email = team_row.get(email_col, "")
                if pd.notna(email) and email.strip():
                    team_emails.append(email.lower())

            if not team_emails:
                continue

            # Map emails to Discord usernames and get scores
            discord_usernames = []
            team_scores = {"arena": 0, "blend_es": 0, "estereotipos": 0, "include": 0}

            for email in team_emails:
                # Get Discord username from email
                discord_username = get_discord_username(email)
                discord_usernames.append(discord_username)

                # Find this user in the personal leaderboard
                user_scores = personal_leaderboard_df[
                    personal_leaderboard_df["Username"].str.lower()
                    == discord_username.lower()
                ]

                if not user_scores.empty:
                    team_scores["arena"] += user_scores.iloc[0]["Arena"]
                    team_scores["blend_es"] += user_scores.iloc[0]["Blend-ES"]
                    team_scores["estereotipos"] += user_scores.iloc[0]["Estereotipos"]
                    team_scores["include"] += user_scores.iloc[0]["INCLUDE"]

            # Pad Discord usernames list to 5 elements
            while len(discord_usernames) < 5:
                discord_usernames.append("")

            # Calculate points for each challenge
            ptos_arena = calculate_challenge_points(team_scores["arena"], "ARENA")
            ptos_blend_es = calculate_challenge_points(team_scores["blend_es"], "BLEND")
            ptos_estereotipos = calculate_challenge_points(
                team_scores["estereotipos"], "ESTEREOTIPOS"
            )
            ptos_include = calculate_challenge_points(team_scores["include"], "INCLUDE")

            # Calculate total points
            ptos_total = ptos_arena + ptos_blend_es + ptos_estereotipos + ptos_include

            # Create team row
            team_row_data = {
                "team_name": team_name,
                "discord_1": discord_usernames[0],
                "discord_2": discord_usernames[1],
                "discord_3": discord_usernames[2],
                "discord_4": discord_usernames[3],
                "discord_5": discord_usernames[4],
                "total_arena": team_scores["arena"],
                "ptos_arena": ptos_arena,
                "total_blend_es": team_scores["blend_es"],
                "ptos_blend_es": ptos_blend_es,
                "total_estereotipos": team_scores["estereotipos"],
                "ptos_estereotipos": ptos_estereotipos,
                "total_include": team_scores["include"],
                "ptos_include": ptos_include,
                "ptos_total": ptos_total,
            }

            team_leaderboard.append(team_row_data)

        # Create DataFrame and sort by total points (ptos_total) instead of just arena
        if team_leaderboard:
            team_df = pd.DataFrame(team_leaderboard)
            team_df.sort_values("ptos_total", ascending=False, inplace=True)
            return team_df
        else:
            return pd.DataFrame()

    except Exception as e:
        print(f"Error calculating team leaderboard: {e}")
        return pd.DataFrame()


def calculate_team_scores():
    personal_leaderboard_df = pd.read_csv(LEADERBOARD_PERSONAL_CSV)
    team_leaderboard_df = get_team_leaderboard(personal_leaderboard_df)
    team_leaderboard_df.to_csv(LEADERBOARD_EQUIPOS_CSV, index=False, encoding="utf-8")
    print("Team scores calculated and saved successfully!")
    print(f"Generated leaderboard with {len(team_leaderboard_df)} teams")


if __name__ == "__main__":
    calculate_team_scores()