127 lines
5.1 KiB
Python
127 lines
5.1 KiB
Python
import gradio as gr
|
||
import os
|
||
import shutil
|
||
import knowledge_based_chatglm as kb
|
||
|
||
|
||
def get_file_list():
|
||
if not os.path.exists("content"):
|
||
return []
|
||
return [f for f in os.listdir("content")]
|
||
|
||
|
||
file_list = get_file_list()
|
||
|
||
embedding_model_dict_list = list(kb.embedding_model_dict.keys())
|
||
|
||
llm_model_dict_list = list(kb.llm_model_dict.keys())
|
||
|
||
|
||
def upload_file(file):
|
||
if not os.path.exists("content"):
|
||
os.mkdir("content")
|
||
filename = os.path.basename(file.name)
|
||
shutil.move(file.name, "content/" + filename)
|
||
# file_list首位插入新上传的文件
|
||
file_list.insert(0, filename)
|
||
return gr.Dropdown.update(choices=file_list, value=filename)
|
||
|
||
|
||
def get_answer(query, vector_store, history):
|
||
resp, history = kb.get_knowledge_based_answer(
|
||
query=query, vector_store=vector_store, chat_history=history)
|
||
return history, history
|
||
|
||
|
||
def get_model_status(history):
|
||
return history + [[None, "模型已完成加载，请选择要加载的文档"]]
|
||
|
||
|
||
def get_file_status(history):
|
||
return history + [[None, "文档已完成加载，请开始提问"]]
|
||
|
||
|
||
with gr.Blocks(css="""
|
||
.importantButton {
|
||
background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
|
||
border: none !important;
|
||
}
|
||
|
||
.importantButton:hover {
|
||
background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
|
||
border: none !important;
|
||
}
|
||
|
||
""") as demo:
|
||
gr.Markdown(
|
||
f"""
|
||
# 🎉langchain-ChatGLM WebUI🎉
|
||
|
||
👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
|
||
|
||
""")
|
||
with gr.Row():
|
||
with gr.Column(scale=2):
|
||
chatbot = gr.Chatbot([[None, """欢迎使用 langchain-ChatGLM Web UI，开始提问前，请依次如下 3 个步骤：
|
||
1. 选择语言模型、Embedding 模型及相关参数后点击"step.1: setting"，并等待加载完成提示
|
||
2. 上传或选择已有文件作为本地知识文档输入后点击"step.2 loading"，并等待加载完成提示
|
||
3. 输入要提交的问题后点击"step.3 asking" """]],
|
||
elem_id="chat-box",
|
||
show_label=False).style(height=600)
|
||
with gr.Column(scale=1):
|
||
with gr.Column():
|
||
llm_model = gr.Radio(
|
||
llm_model_dict_list, label="llm model",
|
||
value="chatglm-6b", interactive=True)
|
||
LLM_HISTORY_LEN = gr.Slider(
|
||
1, 10, value=3, step=1, label="LLM history len", interactive=True)
|
||
embedding_model = gr.Radio(
|
||
embedding_model_dict_list, label="embedding model",
|
||
value="text2vec", interactive=True)
|
||
VECTOR_SEARCH_TOP_K = gr.Slider(
|
||
1, 20, value=6, step=1, label="vector search top k", interactive=True)
|
||
load_model_button = gr.Button("step.1：setting")
|
||
load_model_button.click(lambda *args:
|
||
kb.init_cfg(args[0], args[1], args[2], args[3]),
|
||
show_progress=True,
|
||
api_name="init_cfg",
|
||
inputs=[llm_model, embedding_model, VECTOR_SEARCH_TOP_K, LLM_HISTORY_LEN]
|
||
).then(
|
||
get_model_status, chatbot, chatbot
|
||
)
|
||
|
||
with gr.Column():
|
||
with gr.Tab("select"):
|
||
selectFile = gr.Dropdown(
|
||
file_list, label="content file", interactive=True,
|
||
value=file_list[0] if len(file_list) > 0 else None)
|
||
with gr.Tab("upload"):
|
||
file = gr.File(label="content file", file_types=[
|
||
'.txt', '.md', '.docx']).style(height=100)
|
||
# 将上传的文件保存到content文件夹下,并更新下拉框
|
||
file.upload(upload_file, inputs=file, outputs=selectFile)
|
||
history = gr.State([])
|
||
vector_store = gr.State()
|
||
load_button = gr.Button("step.2：loading")
|
||
load_button.click(lambda fileName:
|
||
kb.init_knowledge_vector_store(
|
||
"content/" + fileName),
|
||
show_progress=True,
|
||
api_name="init_knowledge_vector_store",
|
||
inputs=selectFile, outputs=vector_store).then(
|
||
get_file_status, chatbot, chatbot, show_progress=True,
|
||
)
|
||
|
||
with gr.Row():
|
||
with gr.Column(scale=2):
|
||
query = gr.Textbox(show_label=False, placeholder="Prompts", lines=1, value="用200字总结一下").style(
|
||
container=False)
|
||
with gr.Column(scale=1):
|
||
generate_button = gr.Button(
|
||
"step.3：asking", elem_classes="importantButton")
|
||
generate_button.click(get_answer, [query, vector_store, chatbot],
|
||
[chatbot, history], api_name="get_knowledge_based_answer")
|
||
|
||
demo.queue(concurrency_count=3).launch(
|
||
server_name='0.0.0.0', share=False, inbrowser=False)
|