Update app.py

app.py

@@ -1,31 +1,55 @@
+import streamlit as st
 from langchain.llms import HuggingFaceHub
 from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import FAISS
 
 # 1. 初始化 Gemma 模型
-llm = HuggingFaceHub(repo_id="google/gemma-7b-it", model_kwargs={"temperature": 0.5, "max_length": 512})
+try:
+    llm = HuggingFaceHub(repo_id="google/gemma-7b-it", model_kwargs={"temperature": 0.5, "max_length": 512})
+except Exception as e:
+    st.error(f"Gemma 模型加载失败：{e}")
+    st.stop()
 
-# 2. 准备知识库数据
+# 2. 准备知识库数据 (示例)
 knowledge_base = [
     "Gemma 是 Google 开发的大型语言模型。",
     "Gemma 具有强大的自然语言处理能力。",
-    "Gemma 可以用于问答、对话、文本生成等任务。"
+    "Gemma 可以用于问答、对话、文本生成等任务。",
+    "Gemma 基于 Transformer 架构。",
+    "Gemma 支持多种语言。"
 ]
 
-# 3. 构建向量数据库
-embeddings = SentenceTransformerEmbeddings(model_name="all-mpnet-base-v2")
-db = FAISS.from_texts(knowledge_base, embeddings)
+# 3. 构建向量数据库 (如果需要，仅构建一次)
+try:
+    embeddings = SentenceTransformerEmbeddings(model_name="all-mpnet-base-v2")
+    db = FAISS.from_texts(knowledge_base, embeddings)
+except Exception as e:
+    st.error(f"向量数据库构建失败：{e}")
+    st.stop()
 
 # 4. 问答函数
 def answer_question(question):
-    question_embedding = embeddings.embed_query(question)
-    docs_and_scores = db.similarity_search_with_score(question_embedding)
-    context = "\n".join([doc.page_content for doc, _ in docs_and_scores])
-    prompt = f"请根据以下知识库回答问题：\n{context}\n问题：{question}"
-    answer = llm(prompt)
-    return answer
+    try:
+        question_embedding = embeddings.embed_query(question)
+        docs_and_scores = db.similarity_search_with_score(question_embedding)
+        context = "\n".join([doc.page_content for doc, _ in docs_and_scores])
+        prompt = f"请根据以下知识库回答问题：\n{context}\n问题：{question}"
+        answer = llm(prompt)
+        return answer
+    except Exception as e:
+        st.error(f"问答过程出错：{e}")
+        return "An error occurred during the answering process."
 
-# 5. 测试
-question = "Gemma 有哪些特点？"
-answer = answer_question(question)
-print(answer)
+# 5. Streamlit 界面
+st.title("Gemma 知识库问答系统")
+
+question = st.text_area("请输入问题", height=100)
+
+if st.button("提交"):
+    if not question:
+        st.warning("请输入问题！")
+    else:
+        with st.spinner("正在查询..."):
+            answer = answer_question(question)
+            st.write("答案：")
+            st.write(answer)