app.py

"""
======================================================
📘 金融客服小智（Fintech Assistant）
版本：v3.4 (📱自動縮放優化版)
更新重點：
1. LLM 三次重試機制（防止 API 錯誤中斷）
2. 整合記憶進 prompt（上下文連貫對話）
3. 安全向量搜尋（避免空 collection 錯誤）
4. lambda 修正（避免共享同一 history）
5. 顯示自動分類提示（可見知識來源）
6. 📱 新增手機縮放與字體比例自適應
======================================================
"""

import os, re, base64, time
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.getcwd()
QA_PATH = os.path.join(BASE_DIR, "QA_v2.txt")
LOGO_PATH = os.path.join(BASE_DIR, "mega.png")

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


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


if os.path.exists(QA_PATH):
    qa_docs = load_qa_documents(QA_PATH)
    print("✅ 已載入 QA 檔案，共分為：", {k: len(v) for k, v in qa_docs.items()})
else:
    print("⚠️ 未找到 QA_v2.txt，啟用空白知識庫模式。")
    qa_docs = {"證券": [], "期貨": [], "複委託": []}


# =============================================
# 3️⃣ 向量資料庫初始化（含安全檢查）
# =============================================
client = chromadb.Client()
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)
    try:
        count = vectordb._collection.count() if hasattr(vectordb._collection, "count") else len(vectordb.get()["ids"])
    except Exception:
        count = 0
    if count == 0 and docs:
        vectordb.add_documents(docs)
    vectordbs[cat] = vectordb
print("✅ 向量資料庫初始化完成。")


# =============================================
# 4️⃣ 初始化 LLM 與記憶體
# =============================================
if API_KEY:
    llm = ChatGoogleGenerativeAI(model="gemini-2.0-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（改進版）
# =============================================
def auto_detect_category(text: str):
    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"
    return "證券"


def safe_similarity_search(vectordb, query, k=2):
    """防止空 collection 錯誤"""
    try:
        results = vectordb.similarity_search(query, k=k)
    except Exception as e:
        print(f"⚠️ 向量搜尋錯誤：{e}")
        results = []
    return results


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

    # ✅ 整合記憶體歷史紀錄
    history_data = memory.load_memory_variables({}).get("chat_history", [])
    history_text = "\n".join(
        [f"{m['role']}: {m['content']}" for m in history_data if isinstance(m, dict)]
    )

    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}
過往對話：
{history_text}
    """

    # ✅ LLM 重試機制（3次）
    if llm:
        for attempt in range(3):
            try:
                response = llm.invoke(prompt)
                reply = getattr(response, "content", None) or getattr(response, "text", "⚠️ 無回覆")
                break
            except Exception as e:
                print(f"⚠️ 第 {attempt+1} 次 LLM 錯誤：{e}")
                time.sleep(2)
                reply = "⚠️ 系統忙碌中，請稍後再試。"
    else:
        reply = "（模擬模式）這是示範回覆，請確認是否已設定 GOOGLE_API_KEY。"

    memory.save_context({"input": message}, {"output": reply})
    return f"📂 類別：{category}\n\n{reply}"


# =============================================
# 6️⃣ Gradio 介面（含手機縮放CSS）
# =============================================
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="""
/* === 📱 全域縮放設定 === */
@media (max-width: 768px) {
    html, body {
        zoom: 0.85;
        -moz-transform: scale(0.85);
        -moz-transform-origin: top left;
    }
}

/* === Logo 與標題自適應 === */
#logo-top img { width: 120px; height: auto; }
@media (max-width: 768px) {
    #logo-top img { width: 80px; }
    h1 { font-size: 20px !important; }
}

/* === 輸入列縮窄設定 === */
@media (max-width: 768px) {
    .gradio-container { padding: 6px; }
    #chat-row { flex-direction: row !important; gap: 4px !important; }
    #chat-row textarea { font-size: 14px !important; height: 42px !important; }
    #send-btn { font-size: 14px !important; height: 42px !important; }
}
"""
) as demo:
    if logo_base64:
        gr.HTML(f"<div id='logo-top'><img src='data:image/png;base64,{logo_base64}'></div>")

    gr.HTML("""
    <h1 style='text-align:center;'>👨‍💼 我是小智 您的金融好幫手 🫰</h1>
    <p style='text-align:center;color:gray;'>Powered by Gemini & LangChain</p>
    """)

    with gr.Row():
        with gr.Column(scale=4):
            chatbot = gr.Chatbot(label="💬 對話紀錄", type="messages", height=500)
            user_input = gr.Textbox(
                placeholder="請輸入您的問題，或點選下列「常見問題」...",
                show_label=False,
                lines=1,
                max_lines=3,
                elem_id="chat-row"
            )
            send_btn = gr.Button("送出", variant="primary", elem_id="send-btn")

            def handle_input(message, history):
                if not message.strip():
                    return history, gr.update(value="")
                reply = chat_fn(message, history)
                history = history or []
                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])

        with gr.Column(scale=1):
            gr.Markdown("### 🔍 常見問題")
            examples = [
                "密碼忘記了怎麼辦？",
                "下單憑證怎麼申請？",
                "法人開證劵戶要準備什麼？",
                "期貨交易保證金是什麼？",
                "美股交易時間？",
                "美股可以定期定額嗎？",
            ]
            for q in examples:
                gr.Button(q).click(
                    fn=lambda q=q: handle_input(q, []),
                    inputs=[],
                    outputs=[chatbot, user_input],
                )

            def clear_all():
                memory.clear()
                return [], gr.update(value="")
            gr.Markdown("---")
            gr.Button("🧹 整理畫面").click(clear_all, outputs=[chatbot, user_input])

    gr.HTML("<div id='footer' style='text-align:center;color:#aaa;'>© Fintech Assistant — 僅業務使用，非官方授權</div>")

demo.launch()