import asyncio
from datetime import datetime
import aiohttp
import pickle

import pandas as pd

from utils import normalize_audio_loudness

import os
from dotenv import load_dotenv
from pymongo import MongoClient
from bson import Binary, ObjectId
import zlib

# 尝试直接获取环境变量
BASE_URL = os.environ.get("BASE_URL")
AUDIO_URL = os.environ.get("AUDIO_URL")
MONGO_URI = os.environ.get("MONGO_URI")
DATABASE_NAME = os.environ.get("DATABASE_NAME")
COLLECTION_NAME = os.environ.get("COLLECTION_NAME")
CREATE_COLLECTION = os.environ.get("CREATE_COLLECTION")

# 如果直接获取不到，则从.env文件加载
if BASE_URL is None or AUDIO_URL is None or MONGO_URI is None or DATABASE_NAME is None or COLLECTION_NAME is None or CREATE_COLLECTION is None:
    print("从.env文件加载环境变量")
    load_dotenv()
    BASE_URL = os.getenv("BASE_URL")
    AUDIO_URL = os.getenv("AUDIO_URL")
    MONGO_URI = os.getenv("MONGO_URI")
    DATABASE_NAME = os.getenv("DATABASE_NAME")
    COLLECTION_NAME = os.getenv("COLLECTION_NAME")
    CREATE_COLLECTION = os.getenv("CREATE_COLLECTION")
    
client = MongoClient(MONGO_URI)
db = client[DATABASE_NAME]
collection = db[COLLECTION_NAME]
create_collection = db[CREATE_COLLECTION]

async def generate_api(voice_ids, text):
    timeout = aiohttp.ClientTimeout(total=10)  # 设置10秒的总超时时间
    try:
        async with aiohttp.ClientSession(timeout=timeout) as session:
            async with session.post(BASE_URL+"tts", json={"ids": voice_ids, "text": text}) as response:
                if response.status == 200:
                    # 读取响应内容
                    audio_data = await response.read()
                    # print(type(audio_data))
                    audio_data = normalize_audio_loudness(audio_data)
                    return audio_data
                else:
                    print(response)
                    return f"合成失败: {response.status}"
    except asyncio.TimeoutError:
        return "请求超时，请稍后重试"
    except aiohttp.ClientError as e:
        return f"网络错误: {str(e)}"
    
async def get_audio(voice_id):
    url = AUDIO_URL + voice_id + ".ogg"
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get(url) as response:
                if response.status == 200:
                    return await response.read()
                else:
                    return f"获取音频失败: {response.status}"
    except asyncio.TimeoutError:
        return "请求超时，请稍后重试"
    except aiohttp.ClientError as e:
        return f"网络错误: {str(e)}"

def load_characters_csv(lang):
    # 从MongoDB集合中获取数据
    cursor = collection.find({"language": lang, "is_public": True})
    
    # 将查询结果转换为列表
    data = list(cursor)
    
    # 创建一个空的DataFrame
    df = pd.DataFrame(columns=["类别", "id", "名称", "情绪", "头像", "voice_id"])
    
    # 遍历数据并填充DataFrame
    for item in data:
        df = pd.concat([df, pd.DataFrame({
            "类别": [item["category"]],
            "id": [str(item["id"])],  # 确保id是字符串类型
            "名称": [item["name"]],
            "情绪": [item["emotion"]],
            "头像": [item["avatar"]],
            "voice_id": [item["voice_id"]]
        })], ignore_index=True)
    
    指定顺序 = {
        "zh": ["原神", "崩坏星穹铁道", "绝区零", "鸣潮"],
        "en": ["Genshin Impact", "Honkai: Star Rail", "Zenless Zone Zero", "Wuthering Waves"],
        "ja": ["原神[げんしん", "崩壊：スターレイル", "ゼンレスゾーンゼロ", "Wuthering Waves"],
        "ko": ["원신", "붕괴: 스타레일", "젠레스 존 제로", "Wuthering Waves"]
    }
    当前语言顺序 = 指定顺序.get(lang, 指定顺序["en"])
    其他类别 = sorted(set(df['类别'].unique()) - set(当前语言顺序))
    unique_categories = 当前语言顺序 + 其他类别
    
    return df, unique_categories



async def generate_voice(avatar, name, emotion, tags, gender, audio_data, language):
    # 将图像数据转换为二进制
    avatar_binary = zlib.compress(pickle.dumps(avatar))
    # 将音频数据转换为二进制
    audio_binary = zlib.compress(pickle.dumps(audio_data))

    # 创建声音对象
    voice = {
        "avatar": Binary(avatar_binary),
        "name": name,
        "emotion": emotion,
        "tags": tags,
        "gender": gender,
        "audio_data": Binary(audio_binary),
        "language": language,
        "create_at": datetime.now().isoformat(),
        "is_public": False,
        "is_reviewed": False
    }
    result = create_collection.insert_one(voice)
    return result.inserted_id