import time
from pymongo import MongoClient
import pandas as pd
import math
import os

# MongoDB连接配置
client = MongoClient(os.getenv("client_link"))
db = client.get_database('roleplay')  
collection = db.get_collection('model_stats')  

def update_model_stats(model1_name, model2_name, winner, turn, anony, language):
    # 获取当前时间戳
    tstamp = time.time()
    
    # 插入数据到MongoDB
    collection.insert_one({
        "Model 1": model1_name,
        "Model 2": model2_name,
        "Winner": winner,
        "Turn": turn,
        "Anony": anony,
        "Language": language,
        "tstamp": tstamp
    })

def calculate_elo(winner_elo, loser_elo, k=30, outcome=1):
    """
    winner_elo: Elo score of the winner before the game
    loser_elo: Elo score of the loser before the game
    k: K-factor in Elo calculation
    outcome: 1 if winner won, 0.5 if tie, 0 if loser won (inverted)
    """
    expected_win = 1 / (1 + math.pow(10, (loser_elo - winner_elo) / 400))
    new_winner_elo = winner_elo + k * (outcome - expected_win)
    return new_winner_elo

def load_dataframe():
    # 从MongoDB读取数据
    cursor = collection.find({})
    
    # 将游标中的数据转换为DataFrame
    data = pd.DataFrame(list(cursor))

    # 创建模型名称的唯一列表
    models = pd.unique(data[['Model 1', 'Model 2']].values.ravel('K'))

    # 初始化结果字典
    results = {'模型名称': [], '参赛次数': [], '胜利次数': [], 'ELO': []}
    elo_dict = {model: 1000 for model in models}  # 初始化ELO分数为1000

    for _, row in data.iterrows():
        model1 = row['Model 1']
        model2 = row['Model 2']
        winner = row['Winner']

        if winner == 'Model 1':
            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=1)
            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0)
        elif winner == 'Model 2':
            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=1)
            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0)
        elif winner == 'tie':
            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0.8)
            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0.8)
        elif winner == 'bothbad':
            elo_dict[model1] = calculate_elo(elo_dict[model1], elo_dict[model2], outcome=0.05)
            elo_dict[model2] = calculate_elo(elo_dict[model2], elo_dict[model1], outcome=0.05)

    for model in models:
        count = data['Model 1'].value_counts().get(model, 0) + data['Model 2'].value_counts().get(model, 0)
        if count > 5:  # Only include models with more than 5 participations
            win_count = 0
            win_count += len(data[(data['Winner'] == 'Model 1') & (data['Model 1'] == model)])
            win_count += len(data[(data['Winner'] == 'Model 2') & (data['Model 2'] == model)])
            win_count += len(data[(data['Winner'] == 'tie') & ((data['Model 1'] == model) | (data['Model 2'] == model))])
            results['模型名称'].append(model)
            results['参赛次数'].append(count)
            results['胜利次数'].append(win_count)
            results['ELO'].append(round(elo_dict[model]))

    # 将结果字典转换为DataFrame
    result_df = pd.DataFrame(results)

    # 计算胜率并排序
    result_df["模型胜率"] = (result_df['胜利次数'] / result_df['参赛次数']) * 100
    result_df = result_df.sort_values(by="模型胜率", ascending=False)
    result_df["模型胜率"] = result_df["模型胜率"].map("{:.2f}%".format)
    
    return result_df

def change_name(old,new):
    collection.update_many(
        { "Model 1": old },
        { "$set": { "Model 1": new } }
    )

    # 更新 Model 2 字段
    collection.update_many(
        { "Model 2": old },
        { "$set": { "Model 2": new } }
    )