Delete predict.py

predict.py

@@ -1,191 +0,0 @@
-# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
-
-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
-try: from config_private import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
-except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
-
-timeout_bot_msg = '[local] Request timeout, network error. please check proxy settings in config.py.'
-
-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=[]):
-    """
-        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="", 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 MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
-            if retry > MAX_RETRY: raise TimeoutError
-
-    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(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)
-        functional = functional.get_functionals()
-        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 refleshing 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
-
-