app.py

import streamlit as st
from PIL import Image
import base64
import requests
import json
from voice_toolkit import voice_toolkit
import traceback
import streamlit as st
from PIL import Image
import base64
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
from docx import Document
import os
from openai import OpenAI


icon_path = "images/院徽.ico"

ICON = Image.open(icon_path)
with open(icon_path, "rb") as img_file:
    ICON_base64 = base64.b64encode(img_file.read()).decode()

st.set_page_config(
    page_title="ikun-对话",
    layout="centered",
    page_icon=ICON,
    menu_items={
        'About'
        : '广西警察学院'
    }
)

with st.sidebar:
    icon_text = f"""
        <div class="icon-text-container" style="text-align: center;">
            <img src='data:image/png;base64,{ICON_base64}' alt='icon' style='width: 70px; height: 70px; margin: 0 auto; display: block;'>
            <span style='font-size: 24px;'>课程助手--ikun</span>
        </div>
        """
    st.markdown(
        icon_text,
        unsafe_allow_html=True,
    )

st.sidebar.title('输入')
option2 = st.sidebar.selectbox('方式', ['键盘', '语音'])

# 添加滑动条
st.sidebar.title('参数')
with st.sidebar.expander("内容生成"):
    if "max_new_tokens" not in st.session_state:
        st.session_state["max_new_tokens"] = 800
        st.session_state["top_p"] = 0.9
        st.session_state["temperature"] = 0.2
        st.session_state["repetition_penalty"] = 1.1
    parameter_1 = st.slider('max_new_tokens', min_value=50, max_value=1000,
                            value=st.session_state.max_new_tokens,
                            step=50)
    parameter_2 = st.slider('top_p', min_value=0.5, max_value=0.95, value=st.session_state.top_p, step=0.01)
    parameter_3 = st.slider('temperature', min_value=0.1, max_value=3.0, value=st.session_state.temperature,
                            step=0.1)
    parameter_4 = st.slider('repetition_penalty', min_value=0.5, max_value=5.0,
                            value=st.session_state.repetition_penalty, step=0.1)

    st.session_state["max_new_tokens"] = parameter_1
    st.session_state["top_p"] = parameter_2
    st.session_state["temperature"] = parameter_3
    st.session_state["repetition_penalty"] = parameter_4

st.title("🪶 智课灵犀")
st.caption("🌈 由广西警察学院开发（声明：因校园网络波动，可能暂时无法连接到服务器，请稍后再试）")

# 状态
if "chat_type" not in st.session_state or st.session_state["chat_type"] != "chat":
    st.session_state["chat_type"] = "chat"

if "is_recording" not in st.session_state:
    st.session_state.is_recording = False

if "user_input_area" not in st.session_state:
    st.session_state.user_input_area = ""

if "user_voice_value" not in st.session_state:
    st.session_state.user_voice_value = ""

if "voice_flag" not in st.session_state:
    st.session_state["voice_flag"] = ""

if "messages" not in st.session_state:
    st.session_state["messages"] = [{"role": "assistant", "message": "你好，我是广西警察学院课程知识答疑小助手“ikun”。"}]

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["message"])




# 新增RAG相关配置和初始化
WORD_DOC_PATH = "知识库.docx"
VECTOR_INDEX_PATH = "faiss_index.index"
TEXT_DATA_PATH = "text_data.npy"
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"

client = OpenAI(
    base_url='https://api-inference.modelscope.cn/v1/',
    api_key='7ed44f86-e2c6-4b85-9c4a-26eacfc2e5ee',
)
embedder = SentenceTransformer(EMBEDDING_MODEL)


# 初始化向量存储
def create_vector_store():
    if os.path.exists(VECTOR_INDEX_PATH):
        return

    doc = Document(WORD_DOC_PATH)
    chunks = []
    current_chunk = []

    for para in doc.paragraphs:
        text = para.text.strip()
        if text:
            if text.startswith("第") and "条" in text:
                if current_chunk:
                    chunks.append(" ".join(current_chunk))
                    current_chunk = []
            current_chunk.append(text)

    if current_chunk:
        chunks.append(" ".join(current_chunk))

    embeddings = embedder.encode(chunks, convert_to_tensor=False)
    embeddings = np.array(embeddings).astype('float32')

    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings)

    faiss.write_index(index, VECTOR_INDEX_PATH)
    np.save(TEXT_DATA_PATH, np.array(chunks))


create_vector_store()


def search_knowledge(query, top_k=6):
    index = faiss.read_index(VECTOR_INDEX_PATH)
    text_data = np.load(TEXT_DATA_PATH, allow_pickle=True)

    query_embedding = embedder.encode([query], convert_to_tensor=False)
    query_embedding = np.array(query_embedding).astype('float32')

    distances, indices = index.search(query_embedding, top_k)
    return "\n".join([text_data[i] for i in indices[0]])


# 修改后的消息处理函数
def generate_response(prompt):
    # 检索相关知识
    context = search_knowledge(prompt)

    # 构建对话消息
    messages = [
        {"role": "system", "content": f"基于以下知识回答问题，如果不知道就说不知道：\n{context}"},
        {"role": "user", "content": prompt}
    ]

    # 流式生成响应
    full_response = ""
    response_container = st.empty()

    # 先显示知识库内容
    knowledge_content = f"🔍 知识库相关内容：\n{context}\n\n💡 深度思考：\n"
    response_container.markdown(knowledge_content)

    # 生成回答
    stream = client.chat.completions.create(
        model='deepseek-ai/DeepSeek-R1',
        messages=messages,
        stream=True,
        max_tokens=st.session_state.max_new_tokens,
        temperature=st.session_state.temperature,
        top_p=st.session_state.top_p
    )

    # 处理流式输出
    thinking_done = False
    for chunk in stream:
        content = chunk.choices[0].delta.content or ""
        reasoning = getattr(chunk.choices[0].delta, "reasoning_content", "") or ""

        if reasoning:
            knowledge_content += reasoning
            response_container.markdown(knowledge_content + "▌")

        if content and not reasoning:
            if not thinking_done:
                knowledge_content += "\n\n✅ 最终答案：\n"
                thinking_done = True
            knowledge_content += content
            response_container.markdown(knowledge_content + "▌")

    response_container.markdown(knowledge_content)
    return knowledge_content


# 修改后的发送消息函数
def send_message():
    # 生成响应并更新消息记录
    full_response = generate_response(st.session_state.messages[-1]["message"])
    st.session_state.messages[-1] = {"role": "assistant", "message": full_response}


# 界面部分保持不变（只修改键盘输入处理）
if option2 == "键盘":
    if prompt := st.chat_input(placeholder="输入..."):
        st.session_state.messages.append({"role": "user", "message": prompt})
        st.chat_message("user").write(prompt)

        # 先添加空白的助手消息占位符
        st.session_state.messages.append({"role": "assistant", "message": ""})

        # 生成并更新响应
        send_message()
        st.rerun()
elif option2 == "语音":
    # 文本输入表单
    with st.form("input_form", clear_on_submit=True):
        prompt = st.text_area(
            "**输入：**",
            key="user_input_area",
            value=st.session_state.user_voice_value,
            help="在此输入文本或通过语音输入。"
        )
        submitted = st.form_submit_button("确认提交")

    # 处理提交
    if submitted:
        st.session_state.messages.append({"role": "user", "message": prompt})
        st.chat_message("user").write(prompt)
        answer = send_message()
        st.session_state.messages.append({"role": "assistant", "message": answer["response_text"]})
        st.chat_message("assistant").write(answer["response_text"])

        # print(st.session_state)

        st.session_state.user_voice_value = ""
        st.rerun()
    # 语音输入
    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.rerun()