# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目

"""
    该文件中主要包含三个函数

    不具备多线程能力的函数：
    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程

    具备多线程调用能力的函数
    2. predict_no_ui_long_connection：支持多线程
"""

import json
import time
import gradio as gr
import logging
import traceback
import requests
import importlib
import random

# config_private.py放自己的秘密如API和代理网址
# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
from toolbox import ChatBotWithCookies
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')

timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'

def get_full_error(chunk, stream_response):
    """
        获取完整的从Cohere返回的报错
    """
    while True:
        try:
            chunk += next(stream_response)
        except:
            break
    return chunk

def decode_chunk(chunk):
    # 提前读取一些信息 （用于判断异常）
    chunk_decoded = chunk.decode()
    chunkjson = None
    has_choices = False
    choice_valid = False
    has_content = False
    has_role = False
    try:
        chunkjson = json.loads(chunk_decoded)
        has_choices = 'choices' in chunkjson
        if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
        if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"])
        if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
        if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
    except:
        pass
    return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role

from functools import lru_cache
@lru_cache(maxsize=32)
def verify_endpoint(endpoint):
    """
        检查endpoint是否可用
    """
    if "你亲手写的api名称" in endpoint:
        raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
    return endpoint

def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
    """
    发送，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
    inputs：
        是本次问询的输入
    sys_prompt:
        系统静默prompt
    llm_kwargs：
        内部调优参数
    history：
        是之前的对话列表
    observe_window = None：
        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
    """
    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
    retry = 0
    while True:
        try:
            # make a POST request to the API endpoint, stream=False
            from .bridge_all import model_info
            endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
            response = requests.post(endpoint, headers=headers, proxies=proxies,
                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
        except requests.exceptions.ReadTimeout as e:
            retry += 1
            traceback.print_exc()
            if retry > MAX_RETRY: raise TimeoutError
            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')

    stream_response = response.iter_lines()
    result = ''
    json_data = None
    while True:
        try: chunk = next(stream_response)
        except StopIteration:
            break
        except requests.exceptions.ConnectionError:
            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
        chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
        if chunkjson['event_type'] == 'stream-start': continue
        if chunkjson['event_type'] == 'text-generation':
            result += chunkjson["text"]
            if not console_slience: print(chunkjson["text"], end='')
            if observe_window is not None:
                # 观测窗，把已经获取的数据显示出去
                if len(observe_window) >= 1:
                    observe_window[0] += chunkjson["text"]
                # 看门狗，如果超过期限没有喂狗，则终止
                if len(observe_window) >= 2:
                    if (time.time()-observe_window[1]) > watch_dog_patience:
                        raise RuntimeError("用户取消了程序。")
        if chunkjson['event_type'] == 'stream-end': break
    return result


def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
    """
    发送至chatGPT，流式获取输出。
    用于基础的对话功能。
    inputs 是本次问询的输入
    top_p, temperature是chatGPT的内部调优参数
    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
    additional_fn代表点击的哪个按钮，按钮见functional.py
    """
    # if is_any_api_key(inputs):
    #     chatbot._cookies['api_key'] = inputs
    #     chatbot.append(("输入已识别为Cohere的api_key", what_keys(inputs)))
    #     yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
    #     return
    # elif not is_any_api_key(chatbot._cookies['api_key']):
    #     chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
    #     yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
    #     return

    user_input = inputs
    if additional_fn is not None:
        from core_functional import handle_core_functionality
        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)

    raw_input = inputs
    # logging.info(f'[raw_input] {raw_input}')
    chatbot.append((inputs, ""))
    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面

    # check mis-behavior
    if is_the_upload_folder(user_input):
        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
        time.sleep(2)

    try:
        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
    except RuntimeError as e:
        chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
        yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
        return

    # 检查endpoint是否合法
    try:
        from .bridge_all import model_info
        endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
    except:
        tb_str = '```\n' + trimmed_format_exc() + '```'
        chatbot[-1] = (inputs, tb_str)
        yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
        return

    history.append(inputs); history.append("")

    retry = 0
    while True:
        try:
            # make a POST request to the API endpoint, stream=True
            response = requests.post(endpoint, headers=headers, proxies=proxies,
                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
        except:
            retry += 1
            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
            if retry > MAX_RETRY: raise TimeoutError

    gpt_replying_buffer = ""

    is_head_of_the_stream = True
    if stream:
        stream_response =  response.iter_lines()
        while True:
            try:
                chunk = next(stream_response)
            except StopIteration:
                # 非Cohere官方接口的出现这样的报错，Cohere和API2D不会走这里
                chunk_decoded = chunk.decode()
                error_msg = chunk_decoded
                # 其他情况，直接返回报错
                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
                yield from update_ui(chatbot=chatbot, history=history, msg="非Cohere官方接口返回了错误:" + chunk.decode()) # 刷新界面
                return

            # 提前读取一些信息 （用于判断异常）
            chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)

            if chunkjson:
                try:
                    if chunkjson['event_type'] == 'stream-start':
                        continue
                    if chunkjson['event_type'] == 'text-generation':
                        gpt_replying_buffer = gpt_replying_buffer + chunkjson["text"]
                        history[-1] = gpt_replying_buffer
                        chatbot[-1] = (history[-2], history[-1])
                        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
                    if chunkjson['event_type'] == 'stream-end':
                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
                        history[-1] = gpt_replying_buffer
                        chatbot[-1] = (history[-2], history[-1])
                        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
                        break
                except Exception as e:
                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
                    chunk = get_full_error(chunk, stream_response)
                    chunk_decoded = chunk.decode()
                    error_msg = chunk_decoded
                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
                    print(error_msg)
                    return

def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
    from .bridge_all import model_info
    Cohere_website = ' 请登录Cohere查看详情 https://platform.Cohere.com/signup'
    if "reduce the length" in error_msg:
        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
                                               max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
    elif "does not exist" in error_msg:
        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
    elif "Incorrect API key" in error_msg:
        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. Cohere以提供了不正确的API_KEY为由, 拒绝服务. " + Cohere_website)
    elif "exceeded your current quota" in error_msg:
        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. Cohere以账户额度不足为由, 拒绝服务." + Cohere_website)
    elif "account is not active" in error_msg:
        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
    elif "associated with a deactivated account" in error_msg:
        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
    elif "API key has been deactivated" in error_msg:
        chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
    elif "bad forward key" in error_msg:
        chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
    elif "Not enough point" in error_msg:
        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
    else:
        from toolbox import regular_txt_to_markdown
        tb_str = '```\n' + trimmed_format_exc() + '```'
        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
    return chatbot, history

def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
    """
    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
    """
    # if not is_any_api_key(llm_kwargs['api_key']):
    #     raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")

    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])

    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    if API_ORG.startswith('org-'): headers.update({"Cohere-Organization": API_ORG})
    if llm_kwargs['llm_model'].startswith('azure-'):
        headers.update({"api-key": api_key})
        if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
            azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
            headers.update({"api-key": azure_api_key_unshared})

    conversation_cnt = len(history) // 2

    messages = [{"role": "SYSTEM", "message": system_prompt}]
    if conversation_cnt:
        for index in range(0, 2*conversation_cnt, 2):
            what_i_have_asked = {}
            what_i_have_asked["role"] = "USER"
            what_i_have_asked["message"] = history[index]
            what_gpt_answer = {}
            what_gpt_answer["role"] = "CHATBOT"
            what_gpt_answer["message"] = history[index+1]
            if what_i_have_asked["message"] != "":
                if what_gpt_answer["message"] == "": continue
                if what_gpt_answer["message"] == timeout_bot_msg: continue
                messages.append(what_i_have_asked)
                messages.append(what_gpt_answer)
            else:
                messages[-1]['message'] = what_gpt_answer['message']

    model = llm_kwargs['llm_model']
    if model.startswith('cohere-'): model = model[len('cohere-'):]
    payload = {
        "model": model,
        "message": inputs,
        "chat_history": messages,
        "temperature": llm_kwargs['temperature'],  # 1.0,
        "top_p": llm_kwargs['top_p'],  # 1.0,
        "n": 1,
        "stream": stream,
        "presence_penalty": 0,
        "frequency_penalty": 0,
    }

    return headers,payload