import os

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from flask import Flask, jsonify
from flask import request
# from linshi import autotitle
import requests
import redis
import uuid
import json
from threading import Thread
import time
import re
import logging
from config_llama_api import Config
import numpy as np
import math
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch
from vllm import LLM, SamplingParams

config = Config()

model_path = '/home/majiahui/models-LLM/openbuddy-llama-7b-finetune-v3'
# model_path = '/home/majiahui/models-LLM/openbuddy-openllama-7b-v5-fp16'
# model_path = '/home/majiahui/models-LLM/baichuan-vicuna-chinese-7b'
# model_path = '/home/majiahui/models-LLM/openbuddy-llama-7b-v1.4-fp16'

sampling_params = SamplingParams(temperature=0.95, top_p=0.7,presence_penalty=0.9,stop="</s>")
models_path = model_path
llm = LLM(model=models_path)


# WEIGHTS_NAME = "adapter_model.bin"
# checkpoint_dir = "/home/majiahui/project2/LLaMA-Efficient-Tuning/path_to_sft_checkpoint_paper_prompt_freeze_checkpoint_new_48000/checkpoint-16000"
# weights_file = os.path.join(checkpoint_dir, WEIGHTS_NAME)
# assert os.path.exists(weights_file), f"Provided path ({checkpoint_dir}) does not contain the pretrained weights."
# model_state_dict = torch.load(weights_file, map_location="cuda")
# model.load_state_dict(model_state_dict, strict=False)  # skip missing keys
# model = model.cuda()

redis_title = "redis_title"
pool = redis.ConnectionPool(host=config.reids_ip, port=config.reids_port, max_connections=50, db=config.reids_db)
redis_ = redis.Redis(connection_pool=pool, decode_responses=True)

app = Flask(__name__)
app.config["JSON_AS_ASCII"] = False

# mulu_prompt = "为论文题目“{}”生成目录，要求只有一级标题和二级标题，一级标题使用中文数字 例如一、xxx；二级标题使用阿拉伯数字 例如1.1 xxx；一级标题不少于7个；每个一级标题至少包含3个二级标题"
# first_title_prompt = "论文题目是“{}”，目录是“{}”，请把其中的大标题“{}”的内容补充完整，补充内容字数在{}字左右"
# small_title_prompt = "论文题目是“{}”，目录是“{}”，请把其中的小标题“{}”的内容补充完整，补充内容字数在{}字左右"
# references_prompt = "论文题目是“{}”，目录是“{}”，请为这篇论文生成15篇左右的参考文献，要求其中有有中文参考文献不低于12篇，英文参考文献不低于2篇"
# thank_prompt = "请以“{}”为题写一篇论文的致谢"
# kaitibaogao_prompt = "请以《{}》为题目生成研究的主要的内容、背景、目的、意义，要求不少于1500字"
# chinese_abstract_prompt = "请以《{}》为题目生成论文摘要，要求生成的字数在600字左右"
# english_abstract_prompt = "请把“{}”这段文字翻译成英文"
# chinese_keyword_prompt = "请为“{}”这段论文摘要生成3-5个关键字，使用阿拉伯数字作为序号标注，例如“1.xxx \\n2.xxx \\n3.xxx \\n4.xxx \\n5.xxx \\n"
# english_keyword_prompt = "请把“{}”这几个关键字翻译成英文"


def normal_distribution(x):
    y = np.exp(-(x - config.u) ** 2 / (2 * config.sig ** 2)) / (math.sqrt(2 * math.pi) * config.sig)
    return y


def request_chatglm(prompt):
    outputs = llm.generate([prompt], sampling_params)
    generated_text = outputs[0].outputs[0].text
    return generated_text


def chat_kaitibaogao(main_parameter):
    response = request_chatglm(config.kaitibaogao_prompt.format(main_parameter[0]))

    return response


def chat_abstract_keyword(main_parameter):
    # 生成中文摘要

    chinese_abstract = request_chatglm(config.chinese_abstract_prompt.format(main_parameter[0],main_parameter[1]))

    # 生成英文的摘要

    english_abstract = request_chatglm(config.english_abstract_prompt.format(chinese_abstract))

    # 生成中文关键字

    chinese_keyword = request_chatglm(config.chinese_keyword_prompt.format(chinese_abstract))

    # 生成英文关键字
    english_keyword = request_chatglm(config.english_keyword_prompt.format(chinese_keyword))

    paper_abstract_keyword = {
        "中文摘要": chinese_abstract,
        "英文摘要": english_abstract,
        "中文关键词": chinese_keyword,
        "英文关键词": english_keyword
    }

    return paper_abstract_keyword


def chat_content(main_parameter):
    '''

    :param api_key:
    :param uuid:
    :param main_parameter:
    :return:
    '''
    content_index = main_parameter[0]
    title = main_parameter[1]
    mulu = main_parameter[2]
    subtitle = main_parameter[3]
    prompt = main_parameter[4]
    word_count = main_parameter[5]

    if subtitle[:2] == "@@":
        response = subtitle[2:]
    else:
        response = request_chatglm(prompt.format(title, mulu, subtitle, word_count))
        if subtitle not in response:
            response = subtitle + "\n" + response

    print(prompt.format(title, mulu, subtitle, word_count), response)
    return response


def chat_thanks(main_parameter):
    '''

    :param api_key:
    :param uuid:
    :param main_parameter:
    :return:
    '''
    # title,
    # thank_prompt
    title = main_parameter[0]
    prompt = main_parameter[1]

    response = request_chatglm(prompt.format(title))

    return response


def chat_references(main_parameter):
    '''

    :param api_key:
    :param uuid:
    :param main_parameter:
    :return:
    '''
    # title,
    # mulu,
    # references_prompt
    title = main_parameter[0]
    mulu = main_parameter[1]
    prompt = main_parameter[2]

    response = request_chatglm(prompt.format(title, mulu))

    # 加锁 读取resis并存储结果

    return response


def small_title_tesk(small_title):
    '''
    顺序读取子任务
    :return:
    '''
    task_type = small_title["task_type"]
    main_parameter = small_title["main_parameter"]

    # "task_type": "paper_content",
    # "uuid": uuid,
    # "main_parameter": [
    # "task_type": "paper_content",
    # "task_type": "chat_abstract",
    # "task_type": "kaitibaogao",

    if task_type == "kaitibaogao":
        # result = chat_kaitibaogao(main_parameter)
        result = ""

    elif task_type == "chat_abstract":
        result= chat_abstract_keyword(main_parameter)


    elif task_type == "paper_content":
        result = chat_content(main_parameter)

    elif task_type == "thanks_task":
        # result = chat_thanks(main_parameter)
        result = ""

    elif task_type == "references_task":
        # result = chat_references(main_parameter)
        result = ""
    else:
        result = ""

    print(result, task_type, main_parameter)
    return result, task_type


def main_prrcess(title):
    mulu = request_chatglm(config.mulu_prompt.format(title))
    mulu_list = mulu.split("\n")
    mulu_list = [i.strip() for i in mulu_list if i != ""]
    # mulu_str = "@".join(mulu_list)


    mulu_list_bool = []
    for i in mulu_list:
        result_biaoti_list = re.findall(config.pantten_biaoti, i)
        if result_biaoti_list != []:
            mulu_list_bool.append((i, "一级标题"))
        else:
            mulu_list_bool.append((i, "二级标题"))

    mulu_list_bool_part = mulu_list_bool[:3]

    if mulu_list_bool_part[0][1] != "一级标题":
        redis_.lpush(redis_title, json.dumps({"id": uuid, "title": title}, ensure_ascii=False))  # 加入redis
        redis_.persist(redis_title)
        return
    if mulu_list_bool_part[0][1] == mulu_list_bool_part[1][1] == mulu_list_bool_part[2][1] == "一级标题":
        redis_.lpush(redis_title, json.dumps({"id": uuid, "title": title}, ensure_ascii=False))  # 加入redis
        redis_.persist(redis_title)
        return

    table_of_contents = []

    thanks_references_bool_table = mulu_list_bool[-5:]

    # thanks = "致谢"
    # references = "参考文献"
    for i in thanks_references_bool_table:
        if config.references in i[0]:
            mulu_list_bool.remove(i)
        if config.thanks in i[0]:
            mulu_list_bool.remove(i)
        if config.excursus in i[0]:
            mulu_list_bool.remove(i)

    title_key = ""
    # for i in mulu_list_bool:
    #     if i[1] == "一级标题":
    #         table_of_contents["@@" + i[0]] = []
    #         title_key = "@@" + i[0]
    #     else:
    #         table_of_contents[title_key].append(i[0])

    for i in mulu_list_bool:
        if i[1] == "一级标题":
            paper_dan = {
                "title": "@@" + i[0],
                "small_title": [],
                "word_count": 0
            }
            table_of_contents.append(paper_dan)
        else:
            table_of_contents[-1]["small_title"].append(i[0])

    x_list = [0]
    y_list = [normal_distribution(0)]

    gradient = config.zong_gradient / len(table_of_contents)
    for i in range(len(table_of_contents) - 1):
        x_gradient = x_list[-1] + gradient
        x_list.append(x_gradient)
        y_list.append(normal_distribution(x_list[-1]))

    dan_gradient = config.paper_word_count / sum(y_list)

    for i in range(len(y_list)):
        table_of_contents[i]["word_count"] = dan_gradient * y_list[i]

    print(table_of_contents)

    print(len(table_of_contents))

    table_of_contents_new = []
    for dabiaoti_index in range(len(table_of_contents)):
        dabiaoti_dict = table_of_contents[dabiaoti_index]
        table_of_contents_new.append([dabiaoti_dict["title"], 0])
        for xiaobiaoti in dabiaoti_dict["small_title"]:
            table_of_contents_new.append(
                [xiaobiaoti, int(dabiaoti_dict["word_count"] / len(dabiaoti_dict["small_title"]))])

    small_task_list = []
    # api_key,
    # index,
    # title,
    # mulu,
    # subtitle,
    # prompt
    kaitibaogao_task = {
        "task_type": "kaitibaogao",
        "uuid": uuid,
        "main_parameter": [title]
    }

    chat_abstract_task = {
        "task_type": "chat_abstract",
        "uuid": uuid,
        "main_parameter": [title, mulu]
    }
    small_task_list.append(kaitibaogao_task)
    small_task_list.append(chat_abstract_task)
    content_index = 0
    while True:
        if content_index == len(table_of_contents_new):
            break
        subtitle, word_count = table_of_contents_new[content_index]
        prompt = config.small_title_prompt
        print(table_of_contents_new[1][0])
        if content_index == 0 and table_of_contents_new[1][0][:2] == "@@" and subtitle[:2] == "@@":
            subtitle, prompt, word_count = subtitle[2:], config.first_title_prompt, 800

        if content_index == len(table_of_contents_new) - 1 and subtitle[:2] == "@@":
            subtitle, prompt, word_count = subtitle[2:], config.first_title_prompt, 800

        print("请求的所有参数",
              content_index,
              title,
              subtitle,
              prompt,
              word_count)

        paper_content = {
            "task_type": "paper_content",
            "uuid": uuid,
            "main_parameter": [
                content_index,
                title,
                mulu,
                subtitle,
                prompt,
                word_count
            ]
        }

        small_task_list.append(paper_content)
        content_index += 1

    thanks_task = {
        "task_type": "thanks_task",
        "uuid": uuid,
        "main_parameter": [
            title,
            config.thank_prompt
        ]
    }

    references_task = {
        "task_type": "references_task",
        "uuid": uuid,
        "main_parameter": [
            title,
            mulu,
            config.references_prompt
        ]
    }

    small_task_list.append(thanks_task)
    small_task_list.append(references_task)

    res = {
        "num_small_task": len(small_task_list),
        "tasking_num": 0,
        "标题": title,
        "目录": mulu,
        "开题报告": "",
        "任务书": "",
        "中文摘要": "",
        "英文摘要": "",
        "中文关键词": "",
        "英文关键词": "",
        "正文": "",
        "致谢": "",
        "参考文献": "",
        "table_of_contents": [""] * len(table_of_contents_new)
    }

    for small_task in small_task_list:
        result, task_type = small_title_tesk(small_task)

        if task_type == "kaitibaogao":
            res["开题报告"] = result

        elif task_type == "chat_abstract":
            for i in result:
                res[i] = result[i]

        elif task_type == "paper_content":
            content_index = small_task["main_parameter"][0]
            res["table_of_contents"][content_index] = result

        elif task_type == "thanks_task":
            res["致谢"] = result

        elif task_type == "references_task":
            res["参考文献"] = result

    return res


def classify():  # 调用模型，设置最大batch_size
    while True:
        if redis_.llen(redis_title) == 0:  # 若队列中没有元素就继续获取
            time.sleep(3)
            continue
        query = redis_.lpop(redis_title).decode('UTF-8')  # 获取query的text
        query = json.loads(query)

        uuid = query['id']
        texts = query["text"]

        response = main_prrcess(texts)
        print("res", response)
        return_text = str({"texts": response, "probabilities": None, "status_code": 200})

        uuid_path = os.path.join(config.project_data_txt_path, uuid)

        os.makedirs(uuid_path)

        paper_content_path = os.path.join(uuid_path, "paper_content.json")
        print(uuid)
        with open(paper_content_path, "w") as outfile:
            json.dump(response, outfile)

        save_word_paper = os.path.join(uuid_path, "paper.docx")
        save_word_paper_start = os.path.join(uuid_path, "paper_start.docx")
        os.system(
            "java -Dfile.encoding=UTF-8 -jar '/home/majiahui/projert/chatglm/aiXieZuoPro.jar' '{}' '{}' '{}'".format(
                paper_content_path,
                save_word_paper,
                save_word_paper_start))
        redis_.set(uuid, return_text, 28800)


@app.route("/predict", methods=["POST"])
def handle_query():
    print(request.remote_addr)
    texts = request.json["texts"]
    if texts is None:
        return_text = {"texts": "输入了空值", "probabilities": None, "status_code": 402}
        return jsonify(return_text)

    id_ = str(uuid.uuid1())  # 为query生成唯一标识
    d = {'id': id_, 'text': texts}  # 绑定文本和query id

    redis_.rpush(redis_title, json.dumps(d))  # 加入redis
    while True:
        result = redis_.get(id_)  # 获取该query的模型结果
        if result is not None:
            result_text = {'code': "200", 'data': result.decode('UTF-8')}
            break
        else:
            time.sleep(1)

    return jsonify(result_text)  # 返回结果


t = Thread(target=classify)
t.start()

if __name__ == "__main__":
    fh = logging.FileHandler(mode='a', encoding='utf-8', filename='chitchat.log')
    logging.basicConfig(
        handlers=[fh],
        level=logging.DEBUG,
        format='%(asctime)s - %(levelname)s - %(message)s',
        datefmt='%a, %d %b %Y %H:%M:%S',
    )
    app.run(host="0.0.0.0", port=15000, threaded=True, debug=False)