Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import HuggingFacePipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+st.title("🤖 Excel 問答 AI（ChatGLM 驅動）")
+st.markdown("上傳 Excel（A欄：問題，B欄：答案），開始提問吧！")
+
+uploaded_file = st.file_uploader("上傳你的問答 Excel", type=["xlsx"])
+
+if uploaded_file:
+    df = pd.read_excel(uploaded_file)
+    if not {'問題', '答案'}.issubset(df.columns):
+        st.error("Excel 檔案需包含 '問題' 和 '答案' 欄位")
+    else:
+        texts = [f"問題：{q}\n答案：{a}" for q, a in zip(df['問題'], df['答案'])]
+        st.info("正在建立向量資料庫...")
+        embeddings = HuggingFaceEmbeddings(model_name="shibing624/text2vec-base-chinese")
+        vectorstore = FAISS.from_texts(texts, embedding=embeddings)
+
+        st.info("正在載入 ChatGLM 模型...")
+        tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True)
+        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
+        llm = HuggingFacePipeline(pipeline=pipe)
+
+        qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever(), chain_type="stuff")
+
+        query = st.text_input("請輸入你的問題：")
+        if query:
+            with st.spinner("AI 回答中..."):
+                result = qa.run(query)
+                st.success(result)