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Running
on
CPU Upgrade
Commit
•
440cff3
1
Parent(s):
dc90d99
去除UI微信化
Browse files- app.py +11 -65
- presets.py +47 -15
- requirements.txt +3 -1
- utils.py +24 -418
app.py
CHANGED
@@ -1,14 +1,17 @@
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# -*- coding:utf-8 -*-
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import gradio as gr
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import os
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import logging
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import sys
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-
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from utils import *
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from presets import *
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logging.basicConfig(
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level=logging.
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format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
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)
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@@ -49,72 +52,13 @@ else:
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authflag = True
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gr.Chatbot.postprocess = postprocess
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with open("custom.css", "r", encoding="utf-8") as f:
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customCSS = f.read()
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with gr.Blocks(
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css=customCSS,
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theme=gr.themes.Soft(
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primary_hue=gr.themes.Color(
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c50="#02C160",
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c100="rgba(2, 193, 96, 0.2)",
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c200="#02C160",
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c300="rgba(2, 193, 96, 0.32)",
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c400="rgba(2, 193, 96, 0.32)",
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c500="rgba(2, 193, 96, 1.0)",
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c600="rgba(2, 193, 96, 1.0)",
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c700="rgba(2, 193, 96, 0.32)",
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c800="rgba(2, 193, 96, 0.32)",
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c900="#02C160",
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c950="#02C160",
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),
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secondary_hue=gr.themes.Color(
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c50="#576b95",
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c100="#576b95",
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c200="#576b95",
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c300="#576b95",
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c400="#576b95",
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c500="#576b95",
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c600="#576b95",
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c700="#576b95",
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c800="#576b95",
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c900="#576b95",
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c950="#576b95",
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),
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neutral_hue=gr.themes.Color(
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name="gray",
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c50="#f9fafb",
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c100="#f3f4f6",
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c200="#e5e7eb",
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c300="#d1d5db",
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c400="#B2B2B2",
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c500="#808080",
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c600="#636363",
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c700="#515151",
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c800="#393939",
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c900="#272727",
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c950="#171717",
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),
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radius_size=gr.themes.sizes.radius_sm,
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).set(
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button_primary_background_fill="#06AE56",
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button_primary_background_fill_dark="#06AE56",
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button_primary_background_fill_hover="#07C863",
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button_primary_border_color="#06AE56",
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button_primary_border_color_dark="#06AE56",
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button_primary_text_color="#FFFFFF",
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button_primary_text_color_dark="#FFFFFF",
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button_secondary_background_fill="#F2F2F2",
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button_secondary_background_fill_dark="#2B2B2B",
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button_secondary_text_color="#393939",
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button_secondary_text_color_dark="#FFFFFF",
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# background_fill_primary="#F7F7F7",
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# background_fill_primary_dark="#1F1F1F",
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block_title_text_color="*primary_500",
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block_title_background_fill = "*primary_100",
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input_background_fill="#F6F6F6",
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),
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) as demo:
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history = gr.State([])
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token_count = gr.State([])
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value=hide_middle_chars(my_api_key),
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type="password",
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visible=not HIDE_MY_KEY,
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label="API-Key",
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)
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model_select_dropdown = gr.Dropdown(
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label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
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label="实时传输回答", value=True, visible=enable_streaming_option
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)
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use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
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index_files = gr.
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with gr.Tab(label="Prompt"):
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systemPromptTxt = gr.Textbox(
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use_streaming_checkbox,
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model_select_dropdown,
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use_websearch_checkbox,
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],
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[chatbot, history, status_display, token_count],
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show_progress=True,
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use_streaming_checkbox,
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model_select_dropdown,
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use_websearch_checkbox,
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],
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[chatbot, history, status_display, token_count],
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show_progress=True,
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# -*- coding:utf-8 -*-
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import os
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import logging
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import sys
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import gradio as gr
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from utils import *
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from presets import *
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from overwrites import *
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from chat_func import *
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
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)
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authflag = True
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gr.Chatbot.postprocess = postprocess
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PromptHelper.compact_text_chunks = compact_text_chunks
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with open("custom.css", "r", encoding="utf-8") as f:
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customCSS = f.read()
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with gr.Blocks(
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css=customCSS,
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) as demo:
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history = gr.State([])
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token_count = gr.State([])
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value=hide_middle_chars(my_api_key),
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type="password",
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visible=not HIDE_MY_KEY,
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label="API-Key(按Enter提交)",
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)
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model_select_dropdown = gr.Dropdown(
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label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
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label="实时传输回答", value=True, visible=enable_streaming_option
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)
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use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
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index_files = gr.Files(label="上传索引文件", type="file", multiple=True)
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with gr.Tab(label="Prompt"):
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systemPromptTxt = gr.Textbox(
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use_streaming_checkbox,
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model_select_dropdown,
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use_websearch_checkbox,
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index_files
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],
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[chatbot, history, status_display, token_count],
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show_progress=True,
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use_streaming_checkbox,
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model_select_dropdown,
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use_websearch_checkbox,
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index_files
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],
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[chatbot, history, status_display, token_count],
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show_progress=True,
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presets.py
CHANGED
@@ -1,4 +1,23 @@
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# -*- coding:utf-8 -*-
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title = """<h1 align="left" style="min-width:200px; margin-top:0;">川虎ChatGPT 🚀</h1>"""
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description = """\
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<div align="center" style="margin:16px 0">
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"""
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summarize_prompt = "你是谁?我们刚才聊了什么?" # 总结对话时的 prompt
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MODELS = [
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-0301",
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"gpt-4-32k-0314",
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] # 可选的模型
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Web search results:
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{web_results}
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Query: {query}
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Reply in 中文"""
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-
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# -*- coding:utf-8 -*-
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# 错误信息
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standard_error_msg = "☹️发生了错误:" # 错误信息的标准前缀
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error_retrieve_prompt = "请检查网络连接,或者API-Key是否有效。" # 获取对话时发生错误
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connection_timeout_prompt = "连接超时,无法获取对话。" # 连接超时
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read_timeout_prompt = "读取超时,无法获取对话。" # 读取超时
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proxy_error_prompt = "代理错误,无法获取对话。" # 代理错误
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ssl_error_prompt = "SSL错误,无法获取对话。" # SSL 错误
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no_apikey_msg = "API key长度不是51位,请检查是否输入正确。" # API key 长度不足 51 位
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max_token_streaming = 3500 # 流式对话时的最大 token 数
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timeout_streaming = 30 # 流式对话时的超时时间
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max_token_all = 3500 # 非流式对话时的最大 token 数
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timeout_all = 200 # 非流式对话时的超时时间
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enable_streaming_option = True # 是否启用选择选择是否实时显示回答的勾选框
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HIDE_MY_KEY = False # 如果你想在UI中隐藏你的 API 密钥,将此值设置为 True
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SIM_K = 5
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INDEX_QUERY_TEMPRATURE = 1.0
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title = """<h1 align="left" style="min-width:200px; margin-top:0;">川虎ChatGPT 🚀</h1>"""
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description = """\
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<div align="center" style="margin:16px 0">
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"""
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summarize_prompt = "你是谁?我们刚才聊了什么?" # 总结对话时的 prompt
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MODELS = [
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-0301",
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"gpt-4-32k-0314",
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] # 可选的模型
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WEBSEARCH_PTOMPT_TEMPLATE = """\
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Web search results:
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{web_results}
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Query: {query}
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Reply in 中文"""
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PROMPT_TEMPLATE = """\
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Context information is below.
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---------------------
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{context_str}
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---------------------
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Current date: {current_date}.
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Using the provided context information, write a comprehensive reply to the given query.
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Make sure to cite results using [number] notation after the reference.
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If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
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Use prior knowledge only if the given context didn't provide enough information.
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Answer the question: {query_str}
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Reply in 中文
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"""
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REFINE_TEMPLATE = """\
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The original question is as follows: {query_str}
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We have provided an existing answer: {existing_answer}
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We have the opportunity to refine the existing answer
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(only if needed) with some more context below.
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------------
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{context_msg}
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------------
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Given the new context, refine the original answer to better
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Answer in the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch.
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If the context isn't useful, return the original answer.
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"""
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requirements.txt
CHANGED
@@ -6,4 +6,6 @@ socksio
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tqdm
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colorama
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duckduckgo_search
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Pygments
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tqdm
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colorama
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duckduckgo_search
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Pygments
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llama_index
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langchain
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utils.py
CHANGED
@@ -3,23 +3,16 @@ from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
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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
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import
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# import markdown
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import csv
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from pypinyin import lazy_pinyin
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from presets import *
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import tiktoken
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import
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from duckduckgo_search import ddg
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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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@@ -37,27 +30,6 @@ 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 message,
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None if response is None else mdtex2html.convert(response, extensions=['fenced_code','codehilite','tables']),
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)
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return y
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def count_token(message):
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encoding = tiktoken.get_encoding("cl100k_base")
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input_str = f"role: {message['role']}, content: {message['content']}"
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@@ -102,389 +74,6 @@ def construct_token_message(token, stream=False):
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return f"Token 计数: {token}"
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-
def get_response(
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openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model
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):
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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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# 获取环境变量中的代理设置
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http_proxy = os.environ.get("HTTP_PROXY") or os.environ.get("http_proxy")
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https_proxy = os.environ.get("HTTPS_PROXY") or os.environ.get("https_proxy")
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# 如果存在代理设置,使用它们
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proxies = {}
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if http_proxy:
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logging.info(f"Using HTTP proxy: {http_proxy}")
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proxies["http"] = http_proxy
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if https_proxy:
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logging.info(f"Using HTTPS proxy: {https_proxy}")
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141 |
-
proxies["https"] = https_proxy
|
142 |
-
|
143 |
-
# 如果有代理,使用代理发送请求,否则使用默认设置发送请求
|
144 |
-
if proxies:
|
145 |
-
response = requests.post(
|
146 |
-
API_URL,
|
147 |
-
headers=headers,
|
148 |
-
json=payload,
|
149 |
-
stream=True,
|
150 |
-
timeout=timeout,
|
151 |
-
proxies=proxies,
|
152 |
-
)
|
153 |
-
else:
|
154 |
-
response = requests.post(
|
155 |
-
API_URL,
|
156 |
-
headers=headers,
|
157 |
-
json=payload,
|
158 |
-
stream=True,
|
159 |
-
timeout=timeout,
|
160 |
-
)
|
161 |
-
return response
|
162 |
-
|
163 |
-
|
164 |
-
def stream_predict(
|
165 |
-
openai_api_key,
|
166 |
-
system_prompt,
|
167 |
-
history,
|
168 |
-
inputs,
|
169 |
-
chatbot,
|
170 |
-
all_token_counts,
|
171 |
-
top_p,
|
172 |
-
temperature,
|
173 |
-
selected_model,
|
174 |
-
):
|
175 |
-
def get_return_value():
|
176 |
-
return chatbot, history, status_text, all_token_counts
|
177 |
-
|
178 |
-
logging.info("实时回答模式")
|
179 |
-
partial_words = ""
|
180 |
-
counter = 0
|
181 |
-
status_text = "开始实时传输回答……"
|
182 |
-
history.append(construct_user(inputs))
|
183 |
-
history.append(construct_assistant(""))
|
184 |
-
chatbot.append((parse_text(inputs), ""))
|
185 |
-
user_token_count = 0
|
186 |
-
if len(all_token_counts) == 0:
|
187 |
-
system_prompt_token_count = count_token(construct_system(system_prompt))
|
188 |
-
user_token_count = (
|
189 |
-
count_token(construct_user(inputs)) + system_prompt_token_count
|
190 |
-
)
|
191 |
-
else:
|
192 |
-
user_token_count = count_token(construct_user(inputs))
|
193 |
-
all_token_counts.append(user_token_count)
|
194 |
-
logging.info(f"输入token计数: {user_token_count}")
|
195 |
-
yield get_return_value()
|
196 |
-
try:
|
197 |
-
response = get_response(
|
198 |
-
openai_api_key,
|
199 |
-
system_prompt,
|
200 |
-
history,
|
201 |
-
temperature,
|
202 |
-
top_p,
|
203 |
-
True,
|
204 |
-
selected_model,
|
205 |
-
)
|
206 |
-
except requests.exceptions.ConnectTimeout:
|
207 |
-
status_text = (
|
208 |
-
standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
|
209 |
-
)
|
210 |
-
yield get_return_value()
|
211 |
-
return
|
212 |
-
except requests.exceptions.ReadTimeout:
|
213 |
-
status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
|
214 |
-
yield get_return_value()
|
215 |
-
return
|
216 |
-
|
217 |
-
yield get_return_value()
|
218 |
-
error_json_str = ""
|
219 |
-
|
220 |
-
for chunk in tqdm(response.iter_lines()):
|
221 |
-
if counter == 0:
|
222 |
-
counter += 1
|
223 |
-
continue
|
224 |
-
counter += 1
|
225 |
-
# check whether each line is non-empty
|
226 |
-
if chunk:
|
227 |
-
chunk = chunk.decode()
|
228 |
-
chunklength = len(chunk)
|
229 |
-
try:
|
230 |
-
chunk = json.loads(chunk[6:])
|
231 |
-
except json.JSONDecodeError:
|
232 |
-
logging.info(chunk)
|
233 |
-
error_json_str += chunk
|
234 |
-
status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
|
235 |
-
yield get_return_value()
|
236 |
-
continue
|
237 |
-
# decode each line as response data is in bytes
|
238 |
-
if chunklength > 6 and "delta" in chunk["choices"][0]:
|
239 |
-
finish_reason = chunk["choices"][0]["finish_reason"]
|
240 |
-
status_text = construct_token_message(
|
241 |
-
sum(all_token_counts), stream=True
|
242 |
-
)
|
243 |
-
if finish_reason == "stop":
|
244 |
-
yield get_return_value()
|
245 |
-
break
|
246 |
-
try:
|
247 |
-
partial_words = (
|
248 |
-
partial_words + chunk["choices"][0]["delta"]["content"]
|
249 |
-
)
|
250 |
-
except KeyError:
|
251 |
-
status_text = (
|
252 |
-
standard_error_msg
|
253 |
-
+ "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: "
|
254 |
-
+ str(sum(all_token_counts))
|
255 |
-
)
|
256 |
-
yield get_return_value()
|
257 |
-
break
|
258 |
-
history[-1] = construct_assistant(partial_words)
|
259 |
-
chatbot[-1] = (parse_text(inputs), parse_text(partial_words))
|
260 |
-
all_token_counts[-1] += 1
|
261 |
-
yield get_return_value()
|
262 |
-
|
263 |
-
|
264 |
-
def predict_all(
|
265 |
-
openai_api_key,
|
266 |
-
system_prompt,
|
267 |
-
history,
|
268 |
-
inputs,
|
269 |
-
chatbot,
|
270 |
-
all_token_counts,
|
271 |
-
top_p,
|
272 |
-
temperature,
|
273 |
-
selected_model,
|
274 |
-
):
|
275 |
-
logging.info("一次性回答模式")
|
276 |
-
history.append(construct_user(inputs))
|
277 |
-
history.append(construct_assistant(""))
|
278 |
-
chatbot.append((parse_text(inputs), ""))
|
279 |
-
all_token_counts.append(count_token(construct_user(inputs)))
|
280 |
-
try:
|
281 |
-
response = get_response(
|
282 |
-
openai_api_key,
|
283 |
-
system_prompt,
|
284 |
-
history,
|
285 |
-
temperature,
|
286 |
-
top_p,
|
287 |
-
False,
|
288 |
-
selected_model,
|
289 |
-
)
|
290 |
-
except requests.exceptions.ConnectTimeout:
|
291 |
-
status_text = (
|
292 |
-
standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
|
293 |
-
)
|
294 |
-
return chatbot, history, status_text, all_token_counts
|
295 |
-
except requests.exceptions.ProxyError:
|
296 |
-
status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
|
297 |
-
return chatbot, history, status_text, all_token_counts
|
298 |
-
except requests.exceptions.SSLError:
|
299 |
-
status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt
|
300 |
-
return chatbot, history, status_text, all_token_counts
|
301 |
-
response = json.loads(response.text)
|
302 |
-
content = response["choices"][0]["message"]["content"]
|
303 |
-
history[-1] = construct_assistant(content)
|
304 |
-
chatbot[-1] = (parse_text(inputs), parse_text(content))
|
305 |
-
total_token_count = response["usage"]["total_tokens"]
|
306 |
-
all_token_counts[-1] = total_token_count - sum(all_token_counts)
|
307 |
-
status_text = construct_token_message(total_token_count)
|
308 |
-
return chatbot, history, status_text, all_token_counts
|
309 |
-
|
310 |
-
|
311 |
-
def predict(
|
312 |
-
openai_api_key,
|
313 |
-
system_prompt,
|
314 |
-
history,
|
315 |
-
inputs,
|
316 |
-
chatbot,
|
317 |
-
all_token_counts,
|
318 |
-
top_p,
|
319 |
-
temperature,
|
320 |
-
stream=False,
|
321 |
-
selected_model=MODELS[0],
|
322 |
-
use_websearch_checkbox=False,
|
323 |
-
should_check_token_count=True,
|
324 |
-
): # repetition_penalty, top_k
|
325 |
-
logging.info("输入为:" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
|
326 |
-
if use_websearch_checkbox:
|
327 |
-
results = ddg(inputs, max_results=3)
|
328 |
-
web_results = []
|
329 |
-
for idx, result in enumerate(results):
|
330 |
-
logging.info(f"搜索结果{idx + 1}:{result}")
|
331 |
-
web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}')
|
332 |
-
web_results = "\n\n".join(web_results)
|
333 |
-
today = datetime.datetime.today().strftime("%Y-%m-%d")
|
334 |
-
inputs = (
|
335 |
-
websearch_prompt.replace("{current_date}", today)
|
336 |
-
.replace("{query}", inputs)
|
337 |
-
.replace("{web_results}", web_results)
|
338 |
-
)
|
339 |
-
if len(openai_api_key) != 51:
|
340 |
-
status_text = standard_error_msg + no_apikey_msg
|
341 |
-
logging.info(status_text)
|
342 |
-
chatbot.append((parse_text(inputs), ""))
|
343 |
-
if len(history) == 0:
|
344 |
-
history.append(construct_user(inputs))
|
345 |
-
history.append("")
|
346 |
-
all_token_counts.append(0)
|
347 |
-
else:
|
348 |
-
history[-2] = construct_user(inputs)
|
349 |
-
yield chatbot, history, status_text, all_token_counts
|
350 |
-
return
|
351 |
-
if stream:
|
352 |
-
yield chatbot, history, "开始生成回答……", all_token_counts
|
353 |
-
if stream:
|
354 |
-
logging.info("使用流式传输")
|
355 |
-
iter = stream_predict(
|
356 |
-
openai_api_key,
|
357 |
-
system_prompt,
|
358 |
-
history,
|
359 |
-
inputs,
|
360 |
-
chatbot,
|
361 |
-
all_token_counts,
|
362 |
-
top_p,
|
363 |
-
temperature,
|
364 |
-
selected_model,
|
365 |
-
)
|
366 |
-
for chatbot, history, status_text, all_token_counts in iter:
|
367 |
-
yield chatbot, history, status_text, all_token_counts
|
368 |
-
else:
|
369 |
-
logging.info("不使用流式传输")
|
370 |
-
chatbot, history, status_text, all_token_counts = predict_all(
|
371 |
-
openai_api_key,
|
372 |
-
system_prompt,
|
373 |
-
history,
|
374 |
-
inputs,
|
375 |
-
chatbot,
|
376 |
-
all_token_counts,
|
377 |
-
top_p,
|
378 |
-
temperature,
|
379 |
-
selected_model,
|
380 |
-
)
|
381 |
-
yield chatbot, history, status_text, all_token_counts
|
382 |
-
logging.info(f"传输完毕。当前token计数为{all_token_counts}")
|
383 |
-
if len(history) > 1 and history[-1]["content"] != inputs:
|
384 |
-
logging.info(
|
385 |
-
"回答为:"
|
386 |
-
+ colorama.Fore.BLUE
|
387 |
-
+ f"{history[-1]['content']}"
|
388 |
-
+ colorama.Style.RESET_ALL
|
389 |
-
)
|
390 |
-
if stream:
|
391 |
-
max_token = max_token_streaming
|
392 |
-
else:
|
393 |
-
max_token = max_token_all
|
394 |
-
if sum(all_token_counts) > max_token and should_check_token_count:
|
395 |
-
status_text = f"精简token中{all_token_counts}/{max_token}"
|
396 |
-
logging.info(status_text)
|
397 |
-
yield chatbot, history, status_text, all_token_counts
|
398 |
-
iter = reduce_token_size(
|
399 |
-
openai_api_key,
|
400 |
-
system_prompt,
|
401 |
-
history,
|
402 |
-
chatbot,
|
403 |
-
all_token_counts,
|
404 |
-
top_p,
|
405 |
-
temperature,
|
406 |
-
stream=False,
|
407 |
-
selected_model=selected_model,
|
408 |
-
hidden=True,
|
409 |
-
)
|
410 |
-
for chatbot, history, status_text, all_token_counts in iter:
|
411 |
-
status_text = f"Token 达到上限,已自动降低Token计数至 {status_text}"
|
412 |
-
yield chatbot, history, status_text, all_token_counts
|
413 |
-
|
414 |
-
|
415 |
-
def retry(
|
416 |
-
openai_api_key,
|
417 |
-
system_prompt,
|
418 |
-
history,
|
419 |
-
chatbot,
|
420 |
-
token_count,
|
421 |
-
top_p,
|
422 |
-
temperature,
|
423 |
-
stream=False,
|
424 |
-
selected_model=MODELS[0],
|
425 |
-
):
|
426 |
-
logging.info("重试中……")
|
427 |
-
if len(history) == 0:
|
428 |
-
yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
|
429 |
-
return
|
430 |
-
history.pop()
|
431 |
-
inputs = history.pop()["content"]
|
432 |
-
token_count.pop()
|
433 |
-
iter = predict(
|
434 |
-
openai_api_key,
|
435 |
-
system_prompt,
|
436 |
-
history,
|
437 |
-
inputs,
|
438 |
-
chatbot,
|
439 |
-
token_count,
|
440 |
-
top_p,
|
441 |
-
temperature,
|
442 |
-
stream=stream,
|
443 |
-
selected_model=selected_model,
|
444 |
-
)
|
445 |
-
logging.info("重试完毕")
|
446 |
-
for x in iter:
|
447 |
-
yield x
|
448 |
-
|
449 |
-
|
450 |
-
def reduce_token_size(
|
451 |
-
openai_api_key,
|
452 |
-
system_prompt,
|
453 |
-
history,
|
454 |
-
chatbot,
|
455 |
-
token_count,
|
456 |
-
top_p,
|
457 |
-
temperature,
|
458 |
-
stream=False,
|
459 |
-
selected_model=MODELS[0],
|
460 |
-
hidden=False,
|
461 |
-
):
|
462 |
-
logging.info("开始减少token数量……")
|
463 |
-
iter = predict(
|
464 |
-
openai_api_key,
|
465 |
-
system_prompt,
|
466 |
-
history,
|
467 |
-
summarize_prompt,
|
468 |
-
chatbot,
|
469 |
-
token_count,
|
470 |
-
top_p,
|
471 |
-
temperature,
|
472 |
-
stream=stream,
|
473 |
-
selected_model=selected_model,
|
474 |
-
should_check_token_count=False,
|
475 |
-
)
|
476 |
-
logging.info(f"chatbot: {chatbot}")
|
477 |
-
for chatbot, history, status_text, previous_token_count in iter:
|
478 |
-
history = history[-2:]
|
479 |
-
token_count = previous_token_count[-1:]
|
480 |
-
if hidden:
|
481 |
-
chatbot.pop()
|
482 |
-
yield chatbot, history, construct_token_message(
|
483 |
-
sum(token_count), stream=stream
|
484 |
-
), token_count
|
485 |
-
logging.info("减少token数量完毕")
|
486 |
-
|
487 |
-
|
488 |
def delete_last_conversation(chatbot, history, previous_token_count):
|
489 |
if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
|
490 |
logging.info("由于包含报错信息,只删除chatbot记录")
|
@@ -643,6 +232,7 @@ def reset_state():
|
|
643 |
def reset_textbox():
|
644 |
return gr.update(value="")
|
645 |
|
|
|
646 |
def reset_default():
|
647 |
global API_URL
|
648 |
API_URL = "https://api.openai.com/v1/chat/completions"
|
@@ -650,6 +240,7 @@ def reset_default():
|
|
650 |
os.environ.pop("https_proxy", None)
|
651 |
return gr.update(value=API_URL), gr.update(value=""), "API URL 和代理已重置"
|
652 |
|
|
|
653 |
def change_api_url(url):
|
654 |
global API_URL
|
655 |
API_URL = url
|
@@ -657,22 +248,37 @@ def change_api_url(url):
|
|
657 |
logging.info(msg)
|
658 |
return msg
|
659 |
|
|
|
660 |
def change_proxy(proxy):
|
661 |
os.environ["HTTPS_PROXY"] = proxy
|
662 |
msg = f"代理更改为了{proxy}"
|
663 |
logging.info(msg)
|
664 |
return msg
|
665 |
|
|
|
666 |
def hide_middle_chars(s):
|
667 |
if len(s) <= 8:
|
668 |
return s
|
669 |
else:
|
670 |
head = s[:4]
|
671 |
tail = s[-4:]
|
672 |
-
hidden =
|
673 |
return head + hidden + tail
|
674 |
|
|
|
675 |
def submit_key(key):
|
|
|
676 |
msg = f"API密钥更改为了{hide_middle_chars(key)}"
|
677 |
logging.info(msg)
|
678 |
return key, msg
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
3 |
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
|
4 |
import logging
|
5 |
import json
|
|
|
|
|
|
|
6 |
import os
|
7 |
+
import datetime
|
8 |
+
import hashlib
|
|
|
|
|
9 |
import csv
|
10 |
+
|
11 |
+
import gradio as gr
|
12 |
from pypinyin import lazy_pinyin
|
|
|
13 |
import tiktoken
|
14 |
+
|
15 |
+
from presets import *
|
|
|
|
|
16 |
|
17 |
# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
|
18 |
|
|
|
30 |
TEMPLATES_DIR = "templates"
|
31 |
|
32 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
33 |
def count_token(message):
|
34 |
encoding = tiktoken.get_encoding("cl100k_base")
|
35 |
input_str = f"role: {message['role']}, content: {message['content']}"
|
|
|
74 |
return f"Token 计数: {token}"
|
75 |
|
76 |
|
|
|
|
|
|
|
|
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|
77 |
def delete_last_conversation(chatbot, history, previous_token_count):
|
78 |
if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
|
79 |
logging.info("由于包含报错信息,只删除chatbot记录")
|
|
|
232 |
def reset_textbox():
|
233 |
return gr.update(value="")
|
234 |
|
235 |
+
|
236 |
def reset_default():
|
237 |
global API_URL
|
238 |
API_URL = "https://api.openai.com/v1/chat/completions"
|
|
|
240 |
os.environ.pop("https_proxy", None)
|
241 |
return gr.update(value=API_URL), gr.update(value=""), "API URL 和代理已重置"
|
242 |
|
243 |
+
|
244 |
def change_api_url(url):
|
245 |
global API_URL
|
246 |
API_URL = url
|
|
|
248 |
logging.info(msg)
|
249 |
return msg
|
250 |
|
251 |
+
|
252 |
def change_proxy(proxy):
|
253 |
os.environ["HTTPS_PROXY"] = proxy
|
254 |
msg = f"代理更改为了{proxy}"
|
255 |
logging.info(msg)
|
256 |
return msg
|
257 |
|
258 |
+
|
259 |
def hide_middle_chars(s):
|
260 |
if len(s) <= 8:
|
261 |
return s
|
262 |
else:
|
263 |
head = s[:4]
|
264 |
tail = s[-4:]
|
265 |
+
hidden = "*" * (len(s) - 8)
|
266 |
return head + hidden + tail
|
267 |
|
268 |
+
|
269 |
def submit_key(key):
|
270 |
+
key = key.strip()
|
271 |
msg = f"API密钥更改为了{hide_middle_chars(key)}"
|
272 |
logging.info(msg)
|
273 |
return key, msg
|
274 |
+
|
275 |
+
|
276 |
+
def sha1sum(filename):
|
277 |
+
sha1 = hashlib.sha1()
|
278 |
+
sha1.update(filename.encode("utf-8"))
|
279 |
+
return sha1.hexdigest()
|
280 |
+
|
281 |
+
|
282 |
+
def replace_today(prompt):
|
283 |
+
today = datetime.datetime.today().strftime("%Y-%m-%d")
|
284 |
+
return prompt.replace("{current_date}", today)
|