predict.py

# 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：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
    3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
"""

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

# 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
proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL = \
    get_conf('proxies', 'API_URL', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'LLM_MODEL')

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Openai返回的报错
    """
    while True:
        try:
            chunk += next(stream_response)
        except:
            break
    return chunk

def predict_no_ui(inputs, top_p, temperature, history=[], sys_prompt=""):
    """
        发送至chatGPT，等待回复，一次性完成，不显示中间过程。
        predict函数的简化版。
        用于payload比较大的情况，或者用于实现多线、带嵌套的复杂功能。

        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，然后raise ConnectionAbortedError）
    """
    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt=sys_prompt, stream=False)

    retry = 0
    while True:
        try:
            # make a POST request to the API endpoint, stream=False
            response = requests.post(API_URL, headers=headers, proxies=proxies,
                                    json=payload, stream=False, timeout=TIMEOUT_SECONDS*2); 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}) ……')

    try:
        result = json.loads(response.text)["choices"][0]["message"]["content"]
        return result
    except Exception as e:
        if "choices" not in response.text: print(response.text)
        raise ConnectionAbortedError("Json解析不合常规，可能是文本过长" + response.text)


def predict_no_ui_long_connection(inputs, top_p, temperature, history=[], sys_prompt=""):
    """
        发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免有人中途掐网线。
    """
    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt=sys_prompt, stream=True)

    retry = 0
    while True:
        try:
            # make a POST request to the API endpoint, stream=False
            response = requests.post(API_URL, 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 = ''
    while True:
        try: chunk = next(stream_response).decode()
        except StopIteration: break
        if len(chunk)==0: continue
        if not chunk.startswith('data:'): 
            chunk = get_full_error(chunk.encode('utf8'), stream_response)
            raise ConnectionAbortedError("OpenAI拒绝了请求:" + chunk.decode())
        delta = json.loads(chunk.lstrip('data:'))['choices'][0]["delta"]
        if len(delta) == 0: break
        if "role" in delta: continue
        if "content" in delta: result += delta["content"]; print(delta["content"], end='')
        else: raise RuntimeError("意外Json结构："+delta)
    return result


def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', 
            stream = True, additional_fn=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 additional_fn is not None:
        import functional
        importlib.reload(functional)    # 热更新prompt
        functional = functional.get_functionals()
        if "PreProcess" in functional[additional_fn]: inputs = functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
        inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]

    if stream:
        raw_input = inputs
        logging.info(f'[raw_input] {raw_input}')
        chatbot.append((inputs, ""))
        yield chatbot, history, "等待响应"

    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
    history.append(inputs); history.append(" ")

    retry = 0
    while True:
        try:
            # make a POST request to the API endpoint, stream=True
            response = requests.post(API_URL, 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 chatbot, history, "请求超时"+retry_msg
            if retry > MAX_RETRY: raise TimeoutError

    gpt_replying_buffer = ""
    
    is_head_of_the_stream = True
    if stream:
        stream_response =  response.iter_lines()
        while True:
            chunk = next(stream_response)
            # print(chunk.decode()[6:])
            if is_head_of_the_stream:
                # 数据流的第一帧不携带content
                is_head_of_the_stream = False; continue
            
            if chunk:
                try:
                    if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
                        # 判定为数据流的结束，gpt_replying_buffer也写完了
                        logging.info(f'[response] {gpt_replying_buffer}')
                        break
                    # 处理数据流的主体
                    chunkjson = json.loads(chunk.decode()[6:])
                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
                    gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
                    history[-1] = gpt_replying_buffer
                    chatbot[-1] = (history[-2], history[-1])
                    yield chatbot, history, status_text

                except Exception as e:
                    traceback.print_exc()
                    yield chatbot, history, "Json解析不合常规"
                    chunk = get_full_error(chunk, stream_response)
                    error_msg = chunk.decode()
                    if "reduce the length" in error_msg:
                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Input (or history) is too long, please reduce input or clear history by refreshing this page.")
                        history = []
                    elif "Incorrect API key" in error_msg:
                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key provided.")
                    else:
                        from toolbox import regular_txt_to_markdown
                        tb_str = regular_txt_to_markdown(traceback.format_exc())
                        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Json Error \n\n {tb_str} \n\n {regular_txt_to_markdown(chunk.decode()[4:])}")
                    yield chatbot, history, "Json解析不合常规" + error_msg
                    return

def generate_payload(inputs, top_p, temperature, 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
    """
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    }

    conversation_cnt = len(history) // 2

    messages = [{"role": "system", "content": 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["content"] = history[index]
            what_gpt_answer = {}
            what_gpt_answer["role"] = "assistant"
            what_gpt_answer["content"] = history[index+1]
            if what_i_have_asked["content"] != "":
                if what_gpt_answer["content"] == "": continue
                if what_gpt_answer["content"] == timeout_bot_msg: continue
                messages.append(what_i_have_asked)
                messages.append(what_gpt_answer)
            else:
                messages[-1]['content'] = what_gpt_answer['content']

    what_i_ask_now = {}
    what_i_ask_now["role"] = "user"
    what_i_ask_now["content"] = inputs
    messages.append(what_i_ask_now)

    payload = {
        "model": LLM_MODEL,
        "messages": messages, 
        "temperature": temperature,  # 1.0,
        "top_p": top_p,  # 1.0,
        "n": 1,
        "stream": stream,
        "presence_penalty": 0,
        "frequency_penalty": 0,
    }
    
    print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
    return headers,payload