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app.py
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from helper import *
import streamlit as st
import uuid
import copy
import pandas as pd
import openai
from requests.models import ChunkedEncodingError
from streamlit.components import v1
from voice_toolkit import voice_toolkit
st.set_page_config(page_title='ChatGPT Assistant', layout='wide', page_icon='🤖')
# 自定义元素样式
st.markdown(css_code, unsafe_allow_html=True)
if "initial_settings" not in st.session_state:
# 历史聊天窗口
st.session_state["path"] = 'history_chats_file'
st.session_state['history_chats'] = get_history_chats(st.session_state["path"])
# ss参数初始化
st.session_state['delete_dict'] = {}
st.session_state['delete_count'] = 0
st.session_state['voice_flag'] = ''
st.session_state['user_voice_value'] = ''
st.session_state['error_info'] = ''
st.session_state["current_chat_index"] = 0
st.session_state['user_input_content'] = ''
# 读取全局设置
if os.path.exists('./set.json'):
with open('./set.json', 'r', encoding='utf-8') as f:
data_set = json.load(f)
for key, value in data_set.items():
st.session_state[key] = value
# 设置完成
st.session_state["initial_settings"] = True
with st.sidebar:
st.markdown("# 🤖 聊天窗口")
# 创建容器的目的是配合自定义组件的监听操作
chat_container = st.container()
with chat_container:
current_chat = st.radio(
label='历史聊天窗口',
format_func=lambda x: x.split('_')[0] if '_' in x else x,
options=st.session_state['history_chats'],
label_visibility='collapsed',
index=st.session_state["current_chat_index"],
key='current_chat' + st.session_state['history_chats'][st.session_state["current_chat_index"]],
# on_change=current_chat_callback # 此处不适合用回调，无法识别到窗口增减的变动
)
st.write("---")
# 数据写入文件
def write_data(new_chat_name=current_chat):
if "apikey" in st.secrets:
st.session_state["paras"] = {
"temperature": st.session_state["temperature" + current_chat],
"top_p": st.session_state["top_p" + current_chat],
"presence_penalty": st.session_state["presence_penalty" + current_chat],
"frequency_penalty": st.session_state["frequency_penalty" + current_chat],
}
st.session_state["contexts"] = {
"context_select": st.session_state["context_select" + current_chat],
"context_input": st.session_state["context_input" + current_chat],
"context_level": st.session_state["context_level" + current_chat],
}
save_data(st.session_state["path"], new_chat_name, st.session_state["history" + current_chat],
st.session_state["paras"], st.session_state["contexts"])
def reset_chat_name_fun(chat_name):
chat_name = chat_name + '_' + str(uuid.uuid4())
new_name = filename_correction(chat_name)
current_chat_index = st.session_state['history_chats'].index(current_chat)
st.session_state['history_chats'][current_chat_index] = new_name
st.session_state["current_chat_index"] = current_chat_index
# 写入新文件
write_data(new_name)
# 转移数据
st.session_state['history' + new_name] = st.session_state['history' + current_chat]
for item in ["context_select", "context_input", "context_level", *initial_content_all['paras']]:
st.session_state[item + new_name + "value"] = st.session_state[item + current_chat + "value"]
remove_data(st.session_state["path"], current_chat)
def create_chat_fun():
st.session_state['history_chats'] = ['New Chat_' + str(uuid.uuid4())] + st.session_state['history_chats']
st.session_state["current_chat_index"] = 0
def delete_chat_fun():
if len(st.session_state['history_chats']) == 1:
chat_init = 'New Chat_' + str(uuid.uuid4())
st.session_state['history_chats'].append(chat_init)
pre_chat_index = st.session_state['history_chats'].index(current_chat)
if pre_chat_index > 0:
st.session_state["current_chat_index"] = st.session_state['history_chats'].index(current_chat) - 1
else:
st.session_state["current_chat_index"] = 0
st.session_state['history_chats'].remove(current_chat)
remove_data(st.session_state["path"], current_chat)
with st.sidebar:
c1, c2 = st.columns(2)
create_chat_button = c1.button('新建', use_container_width=True, key='create_chat_button')
if create_chat_button:
create_chat_fun()
st.experimental_rerun()
delete_chat_button = c2.button('删除', use_container_width=True, key='delete_chat_button')
if delete_chat_button:
delete_chat_fun()
st.experimental_rerun()
with st.sidebar:
if ("set_chat_name" in st.session_state) and st.session_state['set_chat_name'] != '':
reset_chat_name_fun(st.session_state['set_chat_name'])
st.session_state['set_chat_name'] = ''
st.experimental_rerun()
st.write("\n")
st.write("\n")
st.text_input("设定窗口名称：", key="set_chat_name", placeholder="点击输入")
st.selectbox("选择模型：", index=0, options=['gpt-3.5-turbo-16k', 'gpt-4'], key="select_model")
st.write("\n")
st.caption("""
- 双击页面可直接定位输入栏
- Ctrl + Enter 可快捷提交问题
- 重要‼️ 此程序可能有多人使用，所以若你的信息隐秘，请及时删除对话， 否则会被记录。
""")
st.markdown('<a href="https://github.com/PierXuY/ChatGPT-Assistant" target="_blank" rel="ChatGPT-Assistant">'
'<img src="https://badgen.net/badge/icon/GitHub?icon=github&label=ChatGPT Assistant" alt="GitHub">'
'</a>', unsafe_allow_html=True)
# 加载数据
if "history" + current_chat not in st.session_state:
for key, value in load_data(st.session_state["path"], current_chat).items():
if key == 'history':
st.session_state[key + current_chat] = value
else:
for k, v in value.items():
st.session_state[k + current_chat + "value"] = v
# 保证不同chat的页面层次一致，否则会导致自定义组件重新渲染
container_show_messages = st.container()
container_show_messages.write("")
# 对话展示
with container_show_messages:
show_messages(current_chat, st.session_state["history" + current_chat])
# 核查是否有对话需要删除
if any(st.session_state['delete_dict'].values()):
for key, value in st.session_state['delete_dict'].items():
try:
deleteCount = value.get("deleteCount")
except AttributeError:
deleteCount = None
if deleteCount == st.session_state['delete_count']:
delete_keys = key
st.session_state['delete_count'] = deleteCount + 1
delete_current_chat, idr = delete_keys.split('>')
df_history_tem = pd.DataFrame(st.session_state["history" + delete_current_chat])
df_history_tem.drop(index=df_history_tem.query("role=='user'").iloc[[int(idr)], :].index, inplace=True)
df_history_tem.drop(index=df_history_tem.query("role=='assistant'").iloc[[int(idr)], :].index, inplace=True)
st.session_state["history" + delete_current_chat] = df_history_tem.to_dict('records')
write_data()
st.experimental_rerun()
def callback_fun(arg):
# 连续快速点击新建与删除会触发错误回调，增加判断
if ("history" + current_chat in st.session_state) and ("frequency_penalty" + current_chat in st.session_state):
write_data()
st.session_state[arg + current_chat + "value"] = st.session_state[arg + current_chat]
def clear_button_callback():
st.session_state['history' + current_chat] = copy.deepcopy(initial_content_history)
write_data()
def save_set(arg):
st.session_state[arg + "_value"] = st.session_state[arg]
if "apikey" in st.secrets:
with open("./set.json", 'w', encoding='utf-8') as f:
json.dump({"open_text_toolkit_value": st.session_state["open_text_toolkit"],
"open_voice_toolkit_value": st.session_state['open_voice_toolkit'],
}, f)
# 输入内容展示
area_user_svg = st.empty()
area_user_content = st.empty()
# 回复展示
area_gpt_svg = st.empty()
area_gpt_content = st.empty()
# 报错展示
area_error = st.empty()
st.write("\n")
st.header('ChatGPT Assistant')
tap_input, tap_context, tap_model, tab_func = st.tabs(['💬 聊天', '🗒️ 预设', '⚙️ 模型', '🛠️ 功能'])
with tap_context:
set_context_list = list(set_context_all.keys())
context_select_index = set_context_list.index(st.session_state['context_select' + current_chat + "value"])
st.selectbox(
label='选择上下文',
options=set_context_list,
key='context_select' + current_chat,
index=context_select_index,
on_change=callback_fun,
args=("context_select",))
st.caption(set_context_all[st.session_state['context_select' + current_chat]])
st.text_area(
label='补充或自定义上下文：', key="context_input" + current_chat,
value=st.session_state['context_input' + current_chat + "value"],
on_change=callback_fun, args=("context_input",))
with tap_model:
st.markdown("OpenAI API Key (可选)")
st.text_input("OpenAI API Key (可选)", type='password', key='apikey_input', label_visibility='collapsed')
st.caption(
"此Key仅在当前网页有效，且优先级高于Secrets中的配置，仅自己可用，他人无法共享。[官网获取](https://platform.openai.com/account/api-keys)")
st.markdown("包含对话次数：")
st.slider(
"Context Level", 0, 10,
st.session_state['context_level' + current_chat + "value"], 1,
on_change=callback_fun,
key='context_level' + current_chat, args=('context_level',),
help="表示每次会话中包含的历史对话次数，预设内容不计算在内。")
st.markdown("模型参数：")
st.slider("Temperature", 0.0, 2.0, st.session_state["temperature" + current_chat + "value"], 0.1,
help="""在0和2之间，应该使用什么样的采样温度？较高的值（如0.8）会使输出更随机，而较低的值（如0.2）则会使其更加集中和确定性。
我们一般建议只更改这个参数或top_p参数中的一个，而不要同时更改两个。""",
on_change=callback_fun, key='temperature' + current_chat, args=('temperature',))
st.slider("Top P", 0.1, 1.0, st.session_state["top_p" + current_chat + "value"], 0.1,
help="""一种替代采用温度进行采样的方法，称为“基于核心概率”的采样。在该方法中，模型会考虑概率最高的top_p个标记的预测结果。
因此，当该参数为0.1时，只有包括前10%概率质量的标记将被考虑。我们一般建议只更改这个参数或采样温度参数中的一个，而不要同时更改两个。""",
on_change=callback_fun, key='top_p' + current_chat, args=('top_p',))
st.slider("Presence Penalty", -2.0, 2.0,
st.session_state["presence_penalty" + current_chat + "value"], 0.1,
help="""该参数的取值范围为-2.0到2.0。正值会根据新标记是否出现在当前生成的文本中对其进行惩罚，从而增加模型谈论新话题的可能性。""",
on_change=callback_fun, key='presence_penalty' + current_chat, args=('presence_penalty',))
st.slider("Frequency Penalty", -2.0, 2.0,
st.session_state["frequency_penalty" + current_chat + "value"], 0.1,
help="""该参数的取值范围为-2.0到2.0。正值会根据新标记在当前生成的文本中的已有频率对其进行惩罚，从而减少模型直接重复相同语句的可能性。""",
on_change=callback_fun, key='frequency_penalty' + current_chat, args=('frequency_penalty',))
st.caption("[官网参数说明](https://platform.openai.com/docs/api-reference/completions/create)")
with tab_func:
c1, c2 = st.columns(2)
with c1:
st.button("清空聊天记录", use_container_width=True, on_click=clear_button_callback)
with c2:
btn = st.download_button(
label="导出聊天记录",
data=download_history(st.session_state['history' + current_chat]),
file_name=f'{current_chat.split("_")[0]}.md',
mime="text/markdown",
use_container_width=True
)
st.write("\n")
st.markdown("自定义功能：")
c1, c2 = st.columns(2)
with c1:
if "open_text_toolkit_value" in st.session_state:
default = st.session_state["open_text_toolkit_value"]
else:
default = True
st.checkbox("开启文本下的功能组件", value=default, key='open_text_toolkit',
on_change=save_set, args=("open_text_toolkit",))
with c2:
if "open_voice_toolkit_value" in st.session_state:
default = st.session_state["open_voice_toolkit_value"]
else:
default = True
st.checkbox("开启语音输入组件", value=default, key='open_voice_toolkit',
on_change=save_set, args=('open_voice_toolkit',))
with tap_input:
def input_callback():
if st.session_state['user_input_area'] != "":
# 修改窗口名称
user_input_content = st.session_state['user_input_area']
df_history = pd.DataFrame(st.session_state["history" + current_chat])
if (len(df_history.query('role!="system"')) == 0) and (current_chat.split('_')[0] == 'New Chat'):
new_name = extract_chars(user_input_content, 18)
reset_chat_name_fun(new_name)
with st.form("input_form", clear_on_submit=True):
user_input = st.text_area("**输入：**", key="user_input_area",
help="内容将以Markdown格式在页面展示，建议遵循相关语言规范，同样有利于GPT正确读取，例如："
"\n- 代码块写在三个反引号内，并标注语言类型"
"\n- 以英文冒号开头的内容或者正则表达式等写在单反引号内",
value=st.session_state['user_voice_value'])
submitted = st.form_submit_button("确认提交", use_container_width=True, on_click=input_callback)
if submitted:
st.session_state['user_input_content'] = user_input
st.session_state['user_voice_value'] = ''
st.experimental_rerun()
if "open_voice_toolkit_value" not in st.session_state or st.session_state["open_voice_toolkit_value"]:
# 语音输入功能
vocie_result = voice_toolkit()
# vocie_result会保存最后一次结果
if (vocie_result and vocie_result['voice_result']['flag'] == 'interim') or st.session_state['voice_flag'] == "interim":
st.session_state['voice_flag'] = 'interim'
st.session_state['user_voice_value'] = vocie_result['voice_result']['value']
if vocie_result['voice_result']['flag'] == 'final':
st.session_state['voice_flag'] = 'final'
st.experimental_rerun()
def get_model_input():
# 需输入的历史记录
context_level = st.session_state['context_level' + current_chat]
history = (get_history_input(st.session_state["history" + current_chat], context_level) +
[{"role": "user", "content": st.session_state['pre_user_input_content']}])
for ctx in [st.session_state['context_input' + current_chat],
set_context_all[st.session_state['context_select' + current_chat]]]:
if ctx != "":
history = [{"role": "system", "content": ctx}] + history
# 设定的模型参数
paras = {
"temperature": st.session_state["temperature" + current_chat],
"top_p": st.session_state["top_p" + current_chat],
"presence_penalty": st.session_state["presence_penalty" + current_chat],
"frequency_penalty": st.session_state["frequency_penalty" + current_chat],
}
return history, paras
if st.session_state['user_input_content'] != '':
if 'r' in st.session_state:
st.session_state.pop("r")
st.session_state[current_chat + 'report'] = ""
st.session_state['pre_user_input_content'] = st.session_state['user_input_content']
st.session_state['user_input_content'] = ''
# 临时展示
show_each_message(st.session_state['pre_user_input_content'], 'user', 'tem',
[area_user_svg.markdown, area_user_content.markdown])
# 模型输入
history_need_input, paras_need_input = get_model_input()
# 调用接口
with st.spinner("🤔"):
try:
if apikey := st.session_state['apikey_input']:
openai.api_key = apikey
else:
openai.api_key = st.secrets["apikey"]
r = openai.ChatCompletion.create(model=st.session_state["select_model"], messages=history_need_input,
stream=True,
**paras_need_input)
except (FileNotFoundError, KeyError):
area_error.error("缺失 OpenAI API Key，请在复制项目后配置Secrets，或者在模型选项中进行临时配置。"
"详情见[项目仓库](https://github.com/PierXuY/ChatGPT-Assistant)。")
except openai.error.AuthenticationError:
area_error.error("无效的 OpenAI API Key。")
except openai.error.APIConnectionError as e:
area_error.error("连接超时，请重试。报错： \n" + str(e.args[0]))
except openai.error.InvalidRequestError as e:
area_error.error("无效的请求，请重试。报错： \n" + str(e.args[0]))
except openai.error.RateLimitError as e:
area_error.error("请求速率过快，请重试。报错： \n" + str(e.args[0]))
else:
st.session_state["chat_of_r"] = current_chat
st.session_state["r"] = r
st.experimental_rerun()
if ("r" in st.session_state) and (current_chat == st.session_state["chat_of_r"]):
if current_chat + 'report' not in st.session_state:
st.session_state[current_chat + 'report'] = ""
try:
for e in st.session_state["r"]:
if "content" in e["choices"][0]["delta"]:
st.session_state[current_chat + 'report'] += e["choices"][0]["delta"]["content"]
show_each_message(st.session_state['pre_user_input_content'], 'user', 'tem',
[area_user_svg.markdown, area_user_content.markdown])
show_each_message(st.session_state[current_chat + 'report'], 'assistant', 'tem',
[area_gpt_svg.markdown, area_gpt_content.markdown])
except ChunkedEncodingError:
area_error.error("网络状况不佳，请刷新页面重试。")
# 应对stop情形
except Exception:
pass
else:
# 保存内容
st.session_state["history" + current_chat].append(
{"role": "user", "content": st.session_state['pre_user_input_content']})
st.session_state["history" + current_chat].append(
{"role": "assistant", "content": st.session_state[current_chat + 'report']})
write_data()
# 用户在网页点击stop时，ss某些情形下会暂时为空
if current_chat + 'report' in st.session_state:
st.session_state.pop(current_chat + 'report')
if 'r' in st.session_state:
st.session_state.pop("r")
st.experimental_rerun()
# 添加事件监听
v1.html(js_code, height=0)