179 lines
8.0 KiB
Python
179 lines
8.0 KiB
Python
"""
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===================================
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@Auther：WenZ
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@Company: BooWay
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@project：booway_dm
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===================================
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"""
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import streamlit as st
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from dialogue_management import QuestionInfo, DialogType, DialogInfo, QuestionType, NLUInfo, ScenInfo, TalkInfo, ChatRecord
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from chains_ceshi import Vertical_classification, small_talk, intention_judge, domain_judge, judge_5W2H, extract_keywords, answer_questions
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from kg_management import retriever_txt_faiss1
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from kg_management import retriever_txt_faiss2
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from kg_management import retriever_txt_faiss3
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from kg_management import retriever_txt_faiss4
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from kg_management import retriever_txt_faiss5
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from kg_management import retriever_txt_faiss6
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from kg_management import retriever_txt_faiss7
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from kg_management import retriever_txt_faiss8
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from kg_management import retriever_txt_faiss9
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from kg_management import input_index_csv_path
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from kg_management import xizang_input_csv_path
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from kg_management import cuceng_input_csv_path
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from kg_management import jigai_input_csv_path
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from kg_management import process_domain_category
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chain_vertical = Vertical_classification() # 垂直/开放分类
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chain_intention = intention_judge() # 意图分类
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chain_domain = domain_judge() # 领域分类
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chain_5w2h = judge_5W2H() # 5W2H分类
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chain_keywords = extract_keywords() # 关键语义提取
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chains_qa = answer_questions() # 检索回答
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# import streamlit as st
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#
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# chain_vertical = Vertical_classification() # 垂直/开放分类
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# chain_intention = intention_judge() # 意图分类
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# chain_domain = domain_judge() # 领域分类
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# chain_5w2h = judge_5W2H() # 5W2H分类
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# chain_keywords = extract_keywords() # 关键语义提取
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# chains_qa = answer_questions() # 检索回答
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domain_mapping = {
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'西藏造价软件Z1': (retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3, xizang_input_csv_path),
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'新型储能计价通C1': (retriever_txt_faiss4, retriever_txt_faiss5, retriever_txt_faiss6, cuceng_input_csv_path),
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'技改检修计价通T1': (retriever_txt_faiss7, retriever_txt_faiss8, retriever_txt_faiss9, jigai_input_csv_path)
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}
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import streamlit as st
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# 假设这些是你已有的模块
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# from your_module import chain_vertical, chain_intention, chain_domain, chain_5w2h, chain_keywords, chains_qa
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# from your_module import process_domain_category, NLUInfo, domain_mapping, input_index_csv_path
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# 页面配置
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st.set_page_config(page_title="booway 软件助手", layout="wide")
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# 助手简介
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st.markdown("""
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# 🤖 booway 软件助手
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欢迎使用 **booway 软件助手**，这是一个用于协助用户进行电力造价软件相关问题咨询的智能系统。
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**目前可咨询软件为：**
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- 西藏造价软件Z1
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- 新型储能计价通C1
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- 技改检修计价通T1
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**使用方法：**
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直接在下方输入你的问题，例如：
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- “你好，想问下储能的C1那个软件。初设的基本预备费费率想调整一下,但是没有找到能调整的地方”
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- “你好，初设的基本预备费费率想调整一下,但是没有找到能调整的地方”（多轮测试）
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- “如何把西藏老定额工程升级成西藏Z1的新定额工程”
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- “储能软件勾选了卸车，总价不变呢”
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- “请问技改检修软件里其他费中的设计费的取费基数和费率，你们设置时肯定要依据吧”
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**注意：多轮对话**
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- 目前多轮对话中 当机器人询问用户什么软件，则必须是以上软件名字
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**注意：垂直分类不对***
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- 技改拆除的安全文明施工费费率是多少 分类： 闲聊
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- 技改软件拆除的安全文明施工费费率是多少 分类：软件咨询(+ 软件)
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- 如：技改项目从老版本升级到新版本，是不是定额也跟着跟新了
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- 改为：技改软件项目从老版本升级到新版本，是不是定额也跟着跟新了
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如果你输入的是闲聊内容，系统将提示仅内测用户可用。
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""")
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# 用户输入
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user_input = st.text_input("👤 用户：", "")
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if user_input:
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if user_input.lower() in ["退出", "bye", "再见"]:
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st.success("booway软件助手：再见 👋")
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else:
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vertical_category = chain_vertical.invoke(user_input)
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if vertical_category == '闲聊':
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st.warning("booway软件助手：闲聊服务只提供给内测用户")
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elif vertical_category == '软件咨询':
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# 初始化 NLUInfo
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nlu_info = NLUInfo(vertical_category="软件咨询")
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# 意图识别 & 领域识别
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nlu_info.intent_category = chain_intention.invoke(user_input)
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domain_info = chain_domain.invoke(user_input)
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nlu_info.domain_category = domain_info
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# 多轮询问软件名称
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if domain_info == '未知':
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import random
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prompts = [
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"🤔 Booway软件助手：请问您指的是哪个软件？",
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"🤔 Booway软件助手：请提供软件名称，以便更好地帮助您。",
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"🤔 Booway软件助手：请问您使用的是什么软件？",
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"🤔 Booway软件助手：请告诉我您要查询的软件名称。",
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"🤔 Booway软件助手：请问是哪款软件？"
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]
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# 随机选择一个提示
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random_prompt = random.choice(prompts)
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# 生成输入框
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software_name = st.text_input(random_prompt, "")
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# software_name = st.text_input("🤔 booway软件助手：请问是什么软件？", "")
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if software_name and software_name in domain_mapping:
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domain_info = software_name
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nlu_info.domain_category = software_name
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elif software_name:
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st.error("booway软件助手：请输入一个有效的软件名称")
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if nlu_info.domain_category in domain_mapping:
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# 问题类型 + 关键词提取
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nlu_info.question_type = chain_5w2h.invoke(user_input)
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nlu_info.retrieve_keywords = chain_keywords.invoke({
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"software_name": nlu_info.domain_category,
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"_5w2h_type": nlu_info.question_type,
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"query": user_input
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})
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# 检索分析
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retrievers, input_csv_path = (domain_mapping[nlu_info.domain_category][:3],
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domain_mapping[nlu_info.domain_category][3])
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kg = process_domain_category(nlu_info, retrievers, input_csv_path, input_index_csv_path)
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if kg:
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# 检索内容
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extracted_index = [item[5:] for item in kg if item.startswith("检索知识")]
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st.subheader("📚 检索内容")
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st.write(extracted_index)
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# 回答内容
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response = chains_qa.invoke({"query": user_input, "kg": '\n'.join(kg)})
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st.subheader("💬 回答内容")
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st.write(response)
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# NLU信息
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st.subheader("🧠 NLU_info")
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st.json({
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"vertical_category": nlu_info.vertical_category,
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"intent_category": nlu_info.intent_category,
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"domain_category": nlu_info.domain_category,
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"question_type": nlu_info.question_type,
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"retrieve_keywords": nlu_info.retrieve_keywords,
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})
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else:
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st.error("booway软件助手：无相关检索知识，请重新提出问题")
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else:
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st.warning("booway软件助手：抱歉，我不明白你的问题，请尝试重新表述")
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# streamlit run streamlit_main.py --server.port 2335
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