app.py

# app.py (最终确认版 - 使用 gr.Blocks)
import gradio as gr
from langchain.prompts import PromptTemplate
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_community.llms import LlamaCpp
from huggingface_hub import hf_hub_download
import os
import time

# --- 1. 配置 (保持不变) ---
VECTOR_STORE_PATH = "vector_store"
EMBEDDING_MODEL = "BAAI/bge-large-zh-v1.5"
# 切换到 CapybaraHermes-2.5-Mistral-7B 模型
GGUF_MODEL_REPO = "TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF"
# 我们选择一个大小适中的4位量化版本
GGUF_MODEL_FILE = "capybarahermes-2.5-mistral-7b.Q4_K_M.gguf"

# --- 2. 加载RAG管道 (保持不变) ---
def load_rag_chain():
    print("开始加载RAG管道...")
    embeddings = HuggingFaceBgeEmbeddings(model_name=EMBEDDING_MODEL, model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
    if not os.path.exists(VECTOR_STORE_PATH): raise FileNotFoundError(f"错误：向量数据库 '{VECTOR_STORE_PATH}' 不存在！")
    vector_store = FAISS.load_local(VECTOR_STORE_PATH, embeddings, allow_dangerous_deserialization=True)
    model_path = hf_hub_download(repo_id=GGUF_MODEL_REPO, filename=GGUF_MODEL_FILE, local_dir="models")
    llm = LlamaCpp(model_path=model_path, n_gpu_layers=0, n_batch=512, n_ctx=4096, f16_kv=True, verbose=False)
    
    # 使用为Mistral模型优化的Prompt模板
    prompt_template = """<|im_start|>system
You are a helpful assistant named "粤小智". Answer the user's question in Chinese based on the provided "Context".
If the context is not sufficient, just say: "抱歉，关于您的问题，我的知识库暂时没有相关信息。". Do not make up answers.
Your answer should be clear and step-by-step if it's an operation guide.<|im_end|>
<|im_start|>user
Context:
{context}

Question:
{question}<|im_end|>
<|im_start|>assistant
"""
    PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
    retriever = vector_store.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.3, 'k': 3}
)

    qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever, # 使用我们新创建的retriever
    chain_type_kwargs={"prompt": PROMPT}
)
    print("✅ RAG管道加载完毕！")
    return qa_chain

# --- 3. Gradio应用逻辑 (适配gr.Blocks) ---
RAG_CHAIN = load_rag_chain()

def user(user_message, history):
    # 将用户消息添加到聊天记录中，并返回一个空的输入框
    return "", history + [[user_message, None]]

def bot(history):
    # 获取最后一条用户消息
    user_message = history[-1][0]
    print(f"收到用户消息: '{user_message}'")
    
    # 调用RAG链获取回答
    result = RAG_CHAIN.invoke({"query": user_message})
    bot_message = result.get('result', "处理出错").strip()
    
    # 模拟打字效果
    history[-1][1] = ""
    for character in bot_message:
        history[-1][1] += character
        time.sleep(0.02) # 每个字之间暂停0.02秒
        yield history
    
    print(f"模型生成回答: '{history[-1][1]}'")

# --- 4. 搭建并启动界面 (使用gr.Blocks手动搭建) ---
with gr.Blocks(theme=gr.themes.Soft(), css="footer {display: none !important}") as demo:
    gr.Markdown("# 粤政云服务智能向导 - 我是粤小智 🤖")
    chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        label="聊天窗口",
        bubble_full_width=True,
        height=600
    )
    with gr.Row():
        txt = gr.Textbox(
            scale=4,
            show_label=False,
            placeholder="在这里输入您的问题，然后按回车键...",
            container=False,
        )
    
    # 定义回车或点击按钮后的事件流
    txt.submit(user, [txt, chatbot], [txt, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )

# 使用queue()来处理流式（打字效果）输出
demo.queue()
demo.launch()