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Delete bin_public/utils/utils.py
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bin_public/utils/utils.py
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# -*- coding:utf-8 -*-
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from __future__ import annotations
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from typing import TYPE_CHECKING, List, Tuple
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import logging
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import json
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import gradio as gr
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# import openai
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import os
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import requests
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# import markdown
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import csv
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import mdtex2html
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from pypinyin import lazy_pinyin
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from bin_public.config.presets import *
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import tiktoken
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from tqdm import tqdm
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from duckduckgo_search import ddg
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from bin_public.utils.utils_db import *
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import datetime
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
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if TYPE_CHECKING:
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from typing import TypedDict
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class DataframeData(TypedDict):
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headers: List[str]
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data: List[List[str | int | bool]]
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initial_prompt = "You are a helpful assistant."
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API_URL = "https://api.openai.com/v1/chat/completions"
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HISTORY_DIR = "history"
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TEMPLATES_DIR = "templates"
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def postprocess(
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self, y: List[Tuple[str | None, str | None]]
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) -> List[Tuple[str | None, str | None]]:
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"""
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Parameters:
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y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
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Returns:
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List of tuples representing the message and response. Each message and response will be a string of HTML.
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"""
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if y is None:
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return []
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for i, (message, response) in enumerate(y):
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y[i] = (
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# None if message is None else markdown.markdown(message),
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# None if response is None else markdown.markdown(response),
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None if message is None else mdtex2html.convert((message)),
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None if response is None else mdtex2html.convert(response),
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)
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return y
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def count_token(input_str):
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encoding = tiktoken.get_encoding("cl100k_base")
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length = len(encoding.encode(input_str))
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return length
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def parse_text(text):
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lines = text.split("\n")
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lines = [line for line in lines if line != ""]
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count = 0
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for i, line in enumerate(lines):
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if "```" in line:
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count += 1
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items = line.split('`')
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if count % 2 == 1:
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lines[i] = f'<pre><code class="language-{items[-1]}">'
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else:
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lines[i] = f'<br></code></pre>'
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else:
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if i > 0:
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if count % 2 == 1:
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line = line.replace("`", "\`")
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line = line.replace("<", "<")
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line = line.replace(">", ">")
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line = line.replace(" ", " ")
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line = line.replace("*", "*")
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line = line.replace("_", "_")
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line = line.replace("-", "-")
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line = line.replace(".", ".")
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line = line.replace("!", "!")
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line = line.replace("(", "(")
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line = line.replace(")", ")")
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line = line.replace("$", "$")
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lines[i] = "<br>"+line
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text = "".join(lines)
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return text
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def construct_text(role, text):
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return {"role": role, "content": text}
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def construct_user(text):
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return construct_text("user", text)
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def construct_system(text):
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return construct_text("system", text)
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def construct_assistant(text):
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return construct_text("assistant", text)
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def construct_token_message(token, stream=False):
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return f"Token 计数: {token}"
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def get_response(openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model):
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {openai_api_key}"
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}
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history = [construct_system(system_prompt), *history]
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payload = {
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"model": selected_model,
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"messages": history, # [{"role": "user", "content": f"{inputs}"}],
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"temperature": temperature, # 1.0,
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"top_p": top_p, # 1.0,
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"n": 1,
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"stream": stream,
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"presence_penalty": 0,
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"frequency_penalty": 0,
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}
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if stream:
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timeout = timeout_streaming
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else:
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timeout = timeout_all
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response = requests.post(API_URL, headers=headers, json=payload, stream=True, timeout=timeout)
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return response
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def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model):
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def get_return_value():
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return chatbot, history, status_text, all_token_counts
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logging.info("实时回答模式")
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partial_words = ""
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counter = 0
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status_text = "开始实时传输回答……"
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history.append(construct_user(inputs))
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history.append(construct_assistant(""))
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chatbot.append((parse_text(inputs), ""))
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user_token_count = 0
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if len(all_token_counts) == 0:
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system_prompt_token_count = count_token(system_prompt)
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user_token_count = count_token(inputs) + system_prompt_token_count
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else:
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user_token_count = count_token(inputs)
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all_token_counts.append(user_token_count)
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logging.info(f"输入token计数: {user_token_count}")
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yield get_return_value()
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try:
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response = get_response(openai_api_key, system_prompt, history, temperature, top_p, True, selected_model)
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except requests.exceptions.ConnectTimeout:
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status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
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yield get_return_value()
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return
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except requests.exceptions.ReadTimeout:
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status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
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yield get_return_value()
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return
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yield get_return_value()
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error_json_str = ""
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for chunk in tqdm(response.iter_lines()):
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if counter == 0:
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counter += 1
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continue
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counter += 1
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# check whether each line is non-empty
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if chunk:
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chunk = chunk.decode()
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chunklength = len(chunk)
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try:
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chunk = json.loads(chunk[6:])
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except json.JSONDecodeError:
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logging.info(chunk)
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error_json_str += chunk
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status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
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yield get_return_value()
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continue
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# decode each line as response data is in bytes
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if chunklength > 6 and "delta" in chunk['choices'][0]:
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finish_reason = chunk['choices'][0]['finish_reason']
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status_text = construct_token_message(sum(all_token_counts), stream=True)
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if finish_reason == "stop":
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yield get_return_value()
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break
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try:
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partial_words = partial_words + chunk['choices'][0]["delta"]["content"]
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except KeyError:
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status_text = standard_error_msg + "API回复中找不到内容。很可能是Token计数达到上限。请重置对话。当前Token计数: " + str(sum(all_token_counts))
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yield get_return_value()
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break
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history[-1] = construct_assistant(partial_words)
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chatbot[-1] = (parse_text(inputs), parse_text(partial_words))
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all_token_counts[-1] += 1
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yield get_return_value()
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def predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model):
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logging.info("一次性回答模式")
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history.append(construct_user(inputs))
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history.append(construct_assistant(""))
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chatbot.append((parse_text(inputs), ""))
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all_token_counts.append(count_token(inputs))
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try:
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response = get_response(openai_api_key, system_prompt, history, temperature, top_p, False, selected_model)
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except requests.exceptions.ConnectTimeout:
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status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
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return chatbot, history, status_text, all_token_counts
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except requests.exceptions.ProxyError:
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status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
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return chatbot, history, status_text, all_token_counts
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except requests.exceptions.SSLError:
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status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt
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return chatbot, history, status_text, all_token_counts
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response = json.loads(response.text)
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content = response["choices"][0]["message"]["content"]
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history[-1] = construct_assistant(content)
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chatbot[-1] = (parse_text(inputs), parse_text(content))
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total_token_count = response["usage"]["total_tokens"]
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all_token_counts[-1] = total_token_count - sum(all_token_counts)
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status_text = construct_token_message(total_token_count)
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return chatbot, history, status_text, all_token_counts
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def predict(openai_api_key, invite_code, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, stream=False, selected_model = MODELS[0], use_websearch_checkbox = False, should_check_token_count = True): # repetition_penalty, top_k
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# logging.info("输入为:" +colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
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if use_websearch_checkbox:
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results = ddg(inputs, max_results=3)
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web_results = []
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for idx, result in enumerate(results):
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logging.info(f"搜索结果{idx + 1}:{result}")
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web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}')
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web_results = "\n\n".join(web_results)
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today = datetime.datetime.today().strftime("%Y-%m-%d")
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inputs = websearch_prompt.replace("{current_date}", today).replace("{query}", inputs).replace("{web_results}", web_results)
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if len(openai_api_key) != 51:
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status_text = standard_error_msg + no_apikey_msg
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logging.info(status_text)
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chatbot.append((parse_text(inputs), ""))
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if len(history) == 0:
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history.append(construct_user(inputs))
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history.append("")
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all_token_counts.append(0)
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else:
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history[-2] = construct_user(inputs)
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yield chatbot, history, status_text, all_token_counts
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return
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if stream:
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yield chatbot, history, "开始生成回答……", all_token_counts
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if stream:
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logging.info("使用流式传输")
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iter = stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model)
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for chatbot, history, status_text, all_token_counts in iter:
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yield chatbot, history, status_text, all_token_counts
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else:
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logging.info("不使用流式传输")
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chatbot, history, status_text, all_token_counts = predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model)
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yield chatbot, history, status_text, all_token_counts
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logging.info(f"传输完毕。当前token计数为{all_token_counts}")
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if len(history) > 1 and history[-1]['content'] != inputs:
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# logging.info("回答为:" +colorama.Fore.BLUE + f"{history[-1]['content']}" + colorama.Style.RESET_ALL)
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try:
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token = all_token_counts[-1]
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except:
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token = 0
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holo_query_insert_chat_message(invite_code, inputs, history[-1]['content'], token, history)
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if stream:
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max_token = max_token_streaming
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else:
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max_token = max_token_all
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if sum(all_token_counts) > max_token and should_check_token_count:
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status_text = f"精简token中{all_token_counts}/{max_token}"
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logging.info(status_text)
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yield chatbot, history, status_text, all_token_counts
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iter = reduce_token_size(openai_api_key, invite_code, system_prompt, history, chatbot, all_token_counts, top_p, temperature, stream=False, selected_model=selected_model, hidden=True)
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for chatbot, history, status_text, all_token_counts in iter:
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status_text = f"Token 达到上限,已自动降低Token计数至 {status_text}"
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yield chatbot, history, status_text, all_token_counts
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def retry(openai_api_key, invite_code, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, selected_model = MODELS[0]):
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logging.info("重试中……")
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if len(history) == 0:
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yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
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return
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history.pop()
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inputs = history.pop()["content"]
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token_count.pop()
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iter = predict(openai_api_key, invite_code, system_prompt, history, inputs, chatbot, token_count, top_p, temperature, stream=stream, selected_model=selected_model)
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logging.info("重试完毕")
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for x in iter:
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yield x
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def reduce_token_size(openai_api_key, invite_code, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, selected_model = MODELS[0], hidden=False):
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logging.info("开始减少token数量……")
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iter = predict(openai_api_key, invite_code, system_prompt, history, summarize_prompt, chatbot, token_count, top_p, temperature, stream=stream, selected_model = selected_model, should_check_token_count=False)
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logging.info(f"chatbot: {chatbot}")
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for chatbot, history, status_text, previous_token_count in iter:
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history = history[-2:]
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token_count = previous_token_count[-1:]
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if hidden:
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chatbot.pop()
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yield chatbot, history, construct_token_message(sum(token_count), stream=stream), token_count
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logging.info("减少token数量完毕")
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def delete_last_conversation(chatbot, history, previous_token_count):
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if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
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logging.info("由于包含报错信息,只删除chatbot记录")
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chatbot.pop()
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return chatbot, history
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if len(history) > 0:
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logging.info("删除了一组对话历史")
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history.pop()
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history.pop()
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if len(chatbot) > 0:
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logging.info("删除了一组chatbot对话")
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chatbot.pop()
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if len(previous_token_count) > 0:
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logging.info("删除了一组对话的token计数记录")
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previous_token_count.pop()
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return chatbot, history, previous_token_count, construct_token_message(sum(previous_token_count))
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def save_file(filename, system, history, chatbot):
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logging.info("保存对话历史中……")
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os.makedirs(HISTORY_DIR, exist_ok=True)
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if filename.endswith(".json"):
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json_s = {"system": system, "history": history, "chatbot": chatbot}
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print(json_s)
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with open(os.path.join(HISTORY_DIR, filename), "w") as f:
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json.dump(json_s, f)
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elif filename.endswith(".md"):
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md_s = f"system: \n- {system} \n"
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for data in history:
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md_s += f"\n{data['role']}: \n- {data['content']} \n"
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with open(os.path.join(HISTORY_DIR, filename), "w", encoding="utf8") as f:
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f.write(md_s)
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logging.info("保存对话历史完毕")
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354 |
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return os.path.join(HISTORY_DIR, filename)
|
355 |
-
|
356 |
-
|
357 |
-
def save_chat_history(filename, system, history, chatbot):
|
358 |
-
if filename == "":
|
359 |
-
return
|
360 |
-
if not filename.endswith(".json"):
|
361 |
-
filename += ".json"
|
362 |
-
return save_file(filename, system, history, chatbot)
|
363 |
-
|
364 |
-
|
365 |
-
def export_markdown(filename, system, history, chatbot):
|
366 |
-
if filename == "":
|
367 |
-
return
|
368 |
-
if not filename.endswith(".md"):
|
369 |
-
filename += ".md"
|
370 |
-
return save_file(filename, system, history, chatbot)
|
371 |
-
|
372 |
-
|
373 |
-
def load_chat_history(filename, system, history, chatbot):
|
374 |
-
logging.info("加载对话历史中……")
|
375 |
-
if type(filename) != str:
|
376 |
-
filename = filename.name
|
377 |
-
try:
|
378 |
-
with open(os.path.join(HISTORY_DIR, filename), "r") as f:
|
379 |
-
json_s = json.load(f)
|
380 |
-
try:
|
381 |
-
if type(json_s["history"][0]) == str:
|
382 |
-
logging.info("历史记录格式为旧版,正在转换……")
|
383 |
-
new_history = []
|
384 |
-
for index, item in enumerate(json_s["history"]):
|
385 |
-
if index % 2 == 0:
|
386 |
-
new_history.append(construct_user(item))
|
387 |
-
else:
|
388 |
-
new_history.append(construct_assistant(item))
|
389 |
-
json_s["history"] = new_history
|
390 |
-
logging.info(new_history)
|
391 |
-
except:
|
392 |
-
# 没有对话历史
|
393 |
-
pass
|
394 |
-
logging.info("加载对话历史完毕")
|
395 |
-
return filename, json_s["system"], json_s["history"], json_s["chatbot"]
|
396 |
-
except FileNotFoundError:
|
397 |
-
logging.info("没有找到对话历史文件,不执行任何操作")
|
398 |
-
return filename, system, history, chatbot
|
399 |
-
|
400 |
-
|
401 |
-
def sorted_by_pinyin(list):
|
402 |
-
return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
|
403 |
-
|
404 |
-
|
405 |
-
def get_file_names(dir, plain=False, filetypes=[".json"]):
|
406 |
-
logging.info(f"获取文件名列表,目录为{dir},文件类型为{filetypes},是否为纯文本列表{plain}")
|
407 |
-
files = []
|
408 |
-
try:
|
409 |
-
for type in filetypes:
|
410 |
-
files += [f for f in os.listdir(dir) if f.endswith(type)]
|
411 |
-
except FileNotFoundError:
|
412 |
-
files = []
|
413 |
-
files = sorted_by_pinyin(files)
|
414 |
-
if files == []:
|
415 |
-
files = [""]
|
416 |
-
if plain:
|
417 |
-
return files
|
418 |
-
else:
|
419 |
-
return gr.Dropdown.update(choices=files)
|
420 |
-
|
421 |
-
|
422 |
-
def get_history_names(plain=False):
|
423 |
-
logging.info("获取历史记录文件名列表")
|
424 |
-
return get_file_names(HISTORY_DIR, plain)
|
425 |
-
|
426 |
-
|
427 |
-
def load_template(filename, mode=0):
|
428 |
-
logging.info(f"加载模板文件{filename},模式为{mode}(0为返回字典和下拉菜单,1为返回下拉菜单,2为返回字典)")
|
429 |
-
lines = []
|
430 |
-
logging.info("Loading template...")
|
431 |
-
if filename.endswith(".json"):
|
432 |
-
with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f:
|
433 |
-
lines = json.load(f)
|
434 |
-
lines = [[i["act"], i["prompt"]] for i in lines]
|
435 |
-
else:
|
436 |
-
with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as csvfile:
|
437 |
-
reader = csv.reader(csvfile)
|
438 |
-
lines = list(reader)
|
439 |
-
lines = lines[1:]
|
440 |
-
if mode == 1:
|
441 |
-
return sorted_by_pinyin([row[0] for row in lines])
|
442 |
-
elif mode == 2:
|
443 |
-
return {row[0]:row[1] for row in lines}
|
444 |
-
else:
|
445 |
-
choices = sorted_by_pinyin([row[0] for row in lines])
|
446 |
-
return {row[0]:row[1] for row in lines}, gr.Dropdown.update(choices=choices, value=choices[0])
|
447 |
-
|
448 |
-
|
449 |
-
def get_template_names(plain=False):
|
450 |
-
logging.info("获取模板文件名列表")
|
451 |
-
return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
|
452 |
-
|
453 |
-
|
454 |
-
def get_template_content(templates, selection, original_system_prompt):
|
455 |
-
logging.info(f"应用模板中,选择为{selection},原始系统提示为{original_system_prompt}")
|
456 |
-
try:
|
457 |
-
return templates[selection]
|
458 |
-
except:
|
459 |
-
return original_system_prompt
|
460 |
-
|
461 |
-
|
462 |
-
def reset_state():
|
463 |
-
logging.info("重置状态")
|
464 |
-
return [], [], [], construct_token_message(0)
|
465 |
-
|
466 |
-
|
467 |
-
def reset_textbox():
|
468 |
-
return gr.update(value='')
|
469 |
-
|
470 |
-
|
471 |
-
def reset_file(file):
|
472 |
-
return file.clear(reset_textbox, [], [])
|
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