Update app.py

app.py

@@ -1,109 +1,71 @@
-import os
-import gradio as gr
-import torch
-import logging
-
-# LangChain 0.1.x 系列的导入方式
+import os, gradio as gr, torch, logging
 from langchain_chroma import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings   # ← 新路径
 from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import RetrievalQA
-
-# Transformers 库
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
 logging.basicConfig(level=logging.INFO)
 
-# 设置路径
-# 直接使用 Hugging Face Hub 上的模型 ID，而不是本地缓存路径
-VECTOR_STORE_DIR = "./vector_store" # 这个目录用于 ChromaDB，我们保留
-MODEL_NAME = "uer/gpt2-chinese-cluecorpussmall"  # <--- 修改这里！
-EMBEDDING_MODEL_NAME = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2" # <--- 修改这里！
+VECTOR_STORE_DIR = "./vector_store"
+MODEL_NAME = "uer/gpt2-chinese-cluecorpussmall"
+EMBEDDING_MODEL_NAME = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
 
-# 1. 轻量 LLM（uer/gpt2-chinese-cluecorpussmall）
-print("🔧 加载生成模型...")
-try:
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_NAME,
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        device_map="auto",
-    )
-    gen_pipe = pipeline(
-        task="text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=256,
-        temperature=0.5,
-        top_p=0.9,
-        do_sample=True,
-    )
-    llm = HuggingFacePipeline(pipeline=gen_pipe)
-    print("✅ 生成模型加载成功。")
-except Exception as e:
-    logging.error(f"加载生成模型失败: {e}", exc_info=True)
-    llm = None
-    print("❌ 生成模型加载失败，应用可能无法正常工作。")
+# ─── 1. 加载 LLM ───
+print("🔧 加载生成模型…")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto",
+)
+gen_pipe = pipeline(
+    task="text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=256,
+    temperature=0.5,
+    top_p=0.9,
+    do_sample=True,
+)
+llm = HuggingFacePipeline(pipeline=gen_pipe)
 
-# 2. 向量库和嵌入模型
-print("📚 加载向量库和嵌入模型...")
-try:
-    embeddings = HuggingFaceEmbeddings(
-        model_name=EMBEDDING_MODEL_NAME
-    )
-    vectordb = Chroma(persist_directory=VECTOR_STORE_DIR, embedding_function=embeddings)
-    print("✅ 向量库加载成功。")
-except Exception as e:
-    logging.error(f"加载向量库失败: {e}", exc_info=True)
-    vectordb = None
-    print("❌ 向量库加载失败，RAG功能将无法使用。")
+# ─── 2. 加载向量库 ───
+print("📚 加载向量库…")
+embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
+vectordb = Chroma(persist_directory=VECTOR_STORE_DIR, embedding_function=embeddings)
 
-# 3. RAG 问答链
-qa_chain = None
-if llm and vectordb:
-    try:
-        retriever = vectordb.as_retriever(search_kwargs={"k": 3})
-        qa_chain = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=retriever,
-            return_source_documents=True
-        )
-        print("✅ RAG问答链构建成功。")
-    except Exception as e:
-        logging.error(f"构建RAG问答链失败: {e}", exc_info=True)
-        print("❌ RAG问答链构建失败。")
+# ─── 3. 构建 RAG 问答链 ───
+retriever = vectordb.as_retriever(search_kwargs={"k": 3})
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever,
+    return_source_documents=True,
+)
 
-# 4. 业务函数
-def qa_fn(query):
+# ─── 4. 业务函数 ───
+def qa_fn(query: str):
     if not query.strip():
         return "❌ 请输入问题内容。"
-    if not qa_chain:
-        return "⚠️ 问答系统未完全加载，请稍后再试或检查日志。"
-    try:
-        result = qa_chain({"query": query})
-        answer = result["result"]
-        sources = result.get("source_documents", [])
-        sources_text = "\n\n".join(
-            [f"【片段 {i+1}】\n" + doc.page_content for i, doc in enumerate(sources)]
-        )
-        return f"📌 回答：{answer.strip()}\n\n📚 参考：\n{sources_text}"
-    except Exception as e:
-        logging.exception("问答失败：%s", e)
-        return f"❌ 出现错误：{str(e)}\n请检查日志获取更多信息。"
+    result = qa_chain({"query": query})
+    answer = result["result"]
+    sources = result.get("source_documents", [])
+    sources_text = "\n\n".join(
+        [f"【片段 {i+1}】\n{doc.page_content}" for i, doc in enumerate(sources)]
+    )
+    return f"📌 回答：{answer.strip()}\n\n📚 参考：\n{sources_text}"
 
-# 5. Gradio UI
-with gr.Blocks(title="数学知识问答助手", theme=gr.themes.Base()) as demo:
+# ─── 5. Gradio UI ───
+with gr.Blocks(title="数学知识问答助手") as demo:
     gr.Markdown("## 📘 数学知识问答助手\n输入教材相关问题，例如：“什么是函数的定义域？”")
     with gr.Row():
-        query_input = gr.Textbox(label="问题", placeholder="请输入你的问题", lines=2)
-        output_box = gr.Textbox(label="回答", lines=15)
-    submit_btn = gr.Button("提问")
-
-    submit_btn.click(fn=qa_fn, inputs=query_input, outputs=output_box)
-
-    gr.Markdown("---\n模型：uer/gpt2-chinese-cluecorpussmall + Chroma RAG | Powered by Hugging Face Spaces")
+        query = gr.Textbox(label="问题", placeholder="请输入你的问题", lines=2)
+        answer = gr.Textbox(label="回答", lines=15)
+    gr.Button("提问").click(qa_fn, inputs=query, outputs=answer)
+    gr.Markdown("---\n模型：gpt2-chinese-cluecorpus + Chroma RAG\nPowered by Hugging Face Spaces")
 
 if __name__ == "__main__":
     demo.launch()
 
+