app.py

"""
======================================================
📘 金融客服小智（Fintech Assistant）
版本：v2 (重構示範 by Supervisor)
改進重點：
1. 模組化程式結構（易維護）
2. 加入記憶體保存（多輪對話）
3. 改善 Chroma 初始化與 QA 擷取
4. 加強異常處理與容錯提示
======================================================
"""

import os, re, base64
import chromadb
import gradio as gr
from langchain_core.documents import Document
from langchain_chroma import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI

# === 記憶模組相容多版本 ===
try:
    from langchain_memory import ConversationBufferMemory
except ImportError:
    try:
        from langchain.memory import ConversationBufferMemory
    except ImportError:
        from langchain_community.memory import ConversationBufferMemory


# =============================================
# 1️⃣ Embedding 與基礎設定
# =============================================
embedding = HuggingFaceEmbeddings(model_name="BAAI/bge-small-zh-v1.5")

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
QA_PATH = os.path.join(BASE_DIR, "QA_v2.txt")
LOGO_PATH = os.path.join(BASE_DIR, "mega.png")

if not os.path.exists(QA_PATH):
    raise FileNotFoundError("❌ 找不到 QA 檔案 QA_v2.txt，請確認路徑。")

API_KEY = os.getenv("GOOGLE_API_KEY")
if not API_KEY:
    print("⚠️ 尚未設定 GOOGLE_API_KEY，系統將以模擬回覆運行。")


# =============================================
# 2️⃣ QA 載入與分類（改進版正規化）
# =============================================
def load_qa_documents(path: str):
    with open(path, "r", encoding="utf-8") as f:
        text = f.read()

    # 改進版正規表達式，確保每筆 QA 含問題與答案
    pattern = r"(Ｑ[:：].*?Ａ[:：].*?)(?=Ｑ[:：]|$)"
    qas = re.findall(pattern, text, flags=re.S)

    categories = {"證券": [], "期貨": [], "複委託": []}
    for qa in qas:
        doc = Document(page_content=qa.strip())
        if "證券" in qa:
            categories["證券"].append(doc)
        elif "期貨" in qa:
            categories["期貨"].append(doc)
        elif "複委託" in qa:
            categories["複委託"].append(doc)
        else:
            categories["證券"].append(doc)  # 預設分類

    return categories


qa_docs = load_qa_documents(QA_PATH)
print("✅ 已成功載入 QA 檔案，共分為：", {k: len(v) for k, v in qa_docs.items()})


# =============================================
# 3️⃣ 向量資料庫初始化（避免重複寫入）
# =============================================
client = chromadb.PersistentClient(path="./chroma_db")
collection_map = {"證券": "stocks", "期貨": "futures", "複委託": "overseas"}
vectordbs = {}

for cat, docs in qa_docs.items():
    vectordb = Chroma(
        client=client,
        collection_name=collection_map[cat],
        embedding_function=embedding
    )
    if vectordb._collection.count() == 0:
        vectordb.add_documents(docs)
    vectordbs[cat] = vectordb

print("✅ 向量資料庫已建立完成。")


# =============================================
# 4️⃣ 初始化 LLM 與對話記憶
# =============================================
if API_KEY:
    llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key=API_KEY)
else:
    llm = None  # 模擬模式

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)


# =============================================
# 5️⃣ 對話邏輯
# =============================================
def auto_detect_category(text: str):
    """根據關鍵詞自動偵測使用者詢問的業務類別"""
    if any(k in text for k in ["股票", "證券", "開戶", "下單", "交割"]):
        return "證券"
    elif any(k in text for k in ["期貨", "選擇權", "保證金"]):
        return "期貨"
    elif any(k in text for k in ["複委託", "海外", "美股", "港股"]):
        return "複委託"
    else:
        return "證券"


def chat_fn(message, history):
    """核心對話函式"""
    category = auto_detect_category(message)
    vectordb = vectordbs[category]
    docs = vectordb.similarity_search(message, k=2)
    context = "\n\n".join(d.page_content for d in docs) if docs else "查無相關資料"

    prompt = f"""
你是一位金融客服人員，請根據以下QA知識回答：
---
{context}
---
使用者問題：{message}
    """

    try:
        if llm:
            response = llm.invoke(prompt)
            reply = getattr(response, "content", None) or getattr(response, "text", "⚠️ 無回覆")
        else:
            reply = "（模擬模式）這是示範回覆：請確認是否已設定 GOOGLE_API_KEY。"
    except Exception as e:
        reply = f"⚠️ 生成錯誤：{e}"

    # 保存對話記憶
    memory.save_context({"role": "user", "content": message},
                        {"role": "assistant", "content": reply})
    return reply


# =============================================
# 6️⃣ Gradio 介面（重構版）
# =============================================
logo_base64 = ""
if os.path.exists(LOGO_PATH):
    with open(LOGO_PATH, "rb") as f:
        logo_base64 = base64.b64encode(f.read()).decode("utf-8")

with gr.Blocks(
    theme="soft",
    css="""
        #logo-top {
            position: fixed; top: 12px; left: 18px;
            background-color: white; border-radius: 10px;
            padding: 6px 8px; box-shadow: 0 0 8px rgba(0,0,0,0.15);
            pointer-events: none;
        }
        #logo-top img { width: 120px; height: auto; display: block; }

        #footer { text-align:center; font-size:13px; color:#aaa; margin-top: 20px; }
    """
) as demo:
    if logo_base64:
        gr.HTML(f"<div id='logo-top'><img src='data:image/png;base64,{logo_base64}'></div>")

    gr.Markdown("## 👨‍💼 我是小智 · 您的金融好幫手 🫰")
    gr.Markdown("Powered by Gemini & LangChain")

    with gr.Row():
        with gr.Column(scale=4):
            chatbot = gr.Chatbot(label="💬 對話紀錄", type="messages", height=500)

            with gr.Row():
                user_input = gr.Textbox(placeholder="請輸入問題...", show_label=False)
                send_btn = gr.Button("送出", variant="primary")

            def handle_input(message, history):
                if not message.strip():
                    return history, gr.update(value="")
                reply = chat_fn(message, history)
                history += [{"role": "user", "content": message},
                            {"role": "assistant", "content": reply}]
                return history, gr.update(value="")

            user_input.submit(handle_input, [user_input, chatbot], [chatbot, user_input])
            send_btn.click(handle_input, [user_input, chatbot], [chatbot, user_input])

            def clear_all():
                memory.clear()
                return [], gr.update(value="")
            gr.Button("🧹 清除對話").click(clear_all, outputs=[chatbot, user_input])

        with gr.Column(scale=1):
            gr.Markdown("### 🔍 常見問題")
            examples = [
                "未成年可以開戶嗎？",
                "法人開戶要準備什麼？",
                "期貨交易保證金是什麼？",
                "複委託要如何下單？",
                "美股交易時間？",
                "美股可以定期定額嗎？"
            ]
            for q in examples:
                gr.Button(q).click(lambda h, q=q: handle_input(q, h), [chatbot], [chatbot, user_input])

    gr.HTML("<div id='footer'>© Fintech Assistant — 僅業務使用，非官方授權</div>")

demo.launch()