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# app.py
import gradio as gr
import logging
# ==== ① 向量检索 & LLM ====
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain.llms import HuggingFacePipeline
logging.basicConfig(level=logging.INFO)
# --- 1) 加载本地向量库 ---
embedding_model = HuggingFaceEmbeddings(
model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
)
vector_store = Chroma(
persist_directory="vector_store",
embedding_function=embedding_model,
)
# --- 2) 加载（较轻量）LLM ---
# 如果 7B 跑不动，可以先用 openchat-mini 试试
model_id = "openchat/openchat-3.5-0106"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="auto",
)
gen_pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.9,
)
llm = HuggingFacePipeline(pipeline=gen_pipe)
# --- 3) 构建 RAG 问答链 ---
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vector_store.as_retriever(search_kwargs={"k": 3}),
)
# ==== ② Gradio UI ====
def simple_qa(user_query):
"""智能问答：检索 + 生成（单轮演示版）"""
if not user_query.strip():
return "⚠️ 请输入学习问题，例如：什么是定积分？"
try:
answer = qa_chain.run(user_query)
return answer
except Exception as e:
logging.error(f"问答失败: {e}")
return "抱歉，暂时无法回答，请稍后再试。"
def placeholder_fn(*args, **kwargs):
return "功能尚未实现，请等待后续更新。"
with gr.Blocks() as demo:
gr.Markdown("# 智能学习助手 v2.0\n— 大学生专业课学习助手 —")
with gr.Tabs():
# ---------- 智能问答 ----------
with gr.TabItem("智能问答"):
gr.Markdown("> **示例：** 什么是函数的定义域？")
chatbot = gr.Chatbot()
user_msg = gr.Textbox(placeholder="输入您的学习问题，然后按回车或点击发送")
send_btn = gr.Button("发送")
# 单轮：把问答显示在 Chatbot
def update_chat(message, chat_history):
reply = simple_qa(message)
chat_history.append((message, reply))
return "", chat_history
send_btn.click(
fn=update_chat,
inputs=[user_msg, chatbot],
outputs=[user_msg, chatbot],
)
user_msg.submit(
fn=update_chat,
inputs=[user_msg, chatbot],
outputs=[user_msg, chatbot],
)
# ---------- 生成学习大纲 ----------
with gr.TabItem("生成学习大纲"):
gr.Markdown("（学习大纲模块，待开发）")
topic_input = gr.Textbox(label="主题/章节名称", placeholder="如：线性代数 第五章 特征值")
gen_outline_btn = gr.Button("生成大纲")
gen_outline_btn.click(placeholder_fn, inputs=topic_input, outputs=topic_input)
# ---------- 自动出题 ----------
with gr.TabItem("自动出题"):
gr.Markdown("（出题模块，待开发）")
topic2 = gr.Textbox(label="知识点/主题", placeholder="如：高数 第三章 多元函数")
difficulty2 = gr.Dropdown(choices=["简单", "中等", "困难"], label="难度")
count2 = gr.Slider(1, 10, step=1, label="题目数量")
gen_q_btn = gr.Button("开始出题")
gen_q_btn.click(placeholder_fn, inputs=[topic2, difficulty2, count2], outputs=topic2)
# ---------- 答案批改 ----------
with gr.TabItem("答案批改"):
gr.Markdown("（批改模块，待开发）")
std_ans = gr.Textbox(label="标准答案", lines=5)
user_ans = gr.Textbox(label="您的作答", lines=5)
grade_btn = gr.Button("开始批改")
grade_btn.click(placeholder_fn, inputs=[user_ans, std_ans], outputs=user_ans)
gr.Markdown("---\n由 HuggingFace 提供支持 • 版本 2.0")
if __name__ == "__main__":
demo.launch()
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