src/streamlit_app.py

import streamlit as st
import google.generativeai as genai
import requests
import os

# 0. 頁面配置與 CSS 注入（隱藏側邊欄捲軸）
st.set_page_config(page_title="AI 新知小助手", page_icon="📚", layout="wide")

st.markdown(
    """
    <style>
    /* 隱藏側邊欄捲軸 */
    [data-testid="stSidebar"] section::-webkit-scrollbar {
        display: none;
    }
    [data-testid="stSidebar"] section {
        -ms-overflow-style: none;
        scrollbar-width: none;
    }
    /* 調整範例按鈕樣式 */
    .stButton button {
        width: auto;
        padding: 5px 15px;
        border-radius: 20px;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# 從環境變數中取得 API Key
api_key = os.environ.get("GEMINI_API_KEY")
if not api_key:
    st.error("請確認已經在 Space 的 Settings 設定了 GEMINI_API_KEY")
    st.stop()

genai.configure(api_key=api_key)

# 1. 基礎設定與連結
BASE_URL = "https://raw.githubusercontent.com/Deep-Learning-101/deep-learning-101.github.io/main/"
LOGO_URL = f"{BASE_URL}images/DeepLearning101-LOGO.png"
HOME_URL = "https://deep-learning-101.github.io"

KNOWLEDGE_MAP = {
    "大型語言模型 (LLM)": {
        "raw_url": f"{BASE_URL}Large-Language-Model.md",
        "page_url": "https://deep-learning-101.github.io/Large-Language-Model",
        "repo_url": "https://github.com/Deep-Learning-101/Natural-Language-Processing-Paper/blob/main/Large-Language-Model.md"
    },
    "自然語言處理 (NLP)": {
        "raw_url": f"{BASE_URL}Natural-Language-Processing.md",
        "page_url": "https://deep-learning-101.github.io/Natural-Language-Processing",
        "repo_url": "https://github.com/Deep-Learning-101/Natural-Language-Processing-Paper"
    },
    "語音處理 (Speech)": {
        "raw_url": f"{BASE_URL}Speech-Processing.md",
        "page_url": "https://deep-learning-101.github.io/Speech-Processing",
        "repo_url": "https://github.com/Deep-Learning-101/Speech-Processing-Paper"
    },
    "電腦視覺 (CV)": {
        "raw_url": f"{BASE_URL}Computer-Vision.md",
        "page_url": "https://deep-learning-101.github.io/Computer-Vision",
        "repo_url": "https://github.com/Deep-Learning-101/Computer-Vision-Paper"
    }
}

# 2. 批量抓取內容
def fetch_all_knowledge():
    combined_knowledge = ""
    with st.spinner("正在同步 GitHub 最新資訊..."):
        for category, info in KNOWLEDGE_MAP.items():
            try:
                response = requests.get(info["raw_url"])
                response.raise_for_status()
                combined_knowledge += f"\n\n## 【領域：{category}】\n"
                combined_knowledge += response.text
            except Exception as e:
                st.warning(f"無法同步 {category} 的資料：{e}")
    return combined_knowledge

# 初始化 Session State
if "knowledge" not in st.session_state:
    st.session_state.knowledge = fetch_all_knowledge()

if "messages" not in st.session_state:
    st.session_state.messages = []

# 用於處理範例按鈕點擊的狀態
if "example_prompt" not in st.session_state:
    st.session_state.example_prompt = None

# 3. 側邊欄設計
with st.sidebar:
    st.markdown(f'<a href="{HOME_URL}" target="_blank"><img src="{LOGO_URL}" width="100%"></a>', unsafe_allow_html=True)
    st.title("⚙️ 知識庫狀態")
    for category, info in KNOWLEDGE_MAP.items():
        with st.expander(category):
            st.markdown(f"🔗 [瀏覽網頁]({info['page_url']})")
            st.markdown(f"📂 [GitHub 原始碼]({info['repo_url']})")
    st.markdown("---")
    if st.button("🔄 手動更新知識庫"):
        st.session_state.knowledge = fetch_all_knowledge()
        st.success("資料已重新抓取！")

# 4. 主介面與範例問句
st.title("📚 AI 演算法與論文社群助手")
st.caption("知識庫涵蓋 LLM、NLP、Speech、CV。歡迎直接提問！")

# 顯示範例問句按鈕
example_cols = st.columns(3)
examples = [
    "🤖 總結 LLM 的最新趨勢",
    "🗣️ 語音處理有哪些新技術？",
    "👁️ CV 領域目前的懶人包重點"
]

for col, ex in zip(example_cols, examples):
    if col.button(ex):
        st.session_state.example_prompt = ex

# 5. 模型回覆邏輯（含額度限制處理）
def get_gemini_response(user_input):
    system_instruction = f"""
    你是一位專業的 AI 技術分析專家。
    以下是從 GitHub 同步的技術資訊：
    ---
    {st.session_state.knowledge}
    ---
    請嚴格基於上述提供的資訊來回答問題。
    """
    try:
        model = genai.GenerativeModel(
            model_name="gemini-flash-lite-latest",
            system_instruction=system_instruction
        )
        chat = model.start_chat(history=[])
        response = chat.send_message(user_input)
        return response.text
    except Exception as e:
        # 捕捉 API 額度滿了 (429) 或其他錯誤
        error_msg = str(e)
        if "429" in error_msg or "quota" in error_msg.lower():
            return "⚠️ **系統提示：API 使用額度已達上限**\n\n由於目前使用人數較多，Google AI Studio 的免費額度已暫時耗盡。請稍等幾分鐘後再試，或聯絡管理員更新 API Key。"
        else:
            return f"❌ **發生預期外錯誤**\n\n訊息：{error_msg}"

# 6. 對話邏輯
# 判斷是否有範例按鈕被點擊，或是使用者自行輸入
prompt = st.chat_input("想瞭解哪方面的技術？")

if st.session_state.example_prompt:
    prompt = st.session_state.example_prompt
    st.session_state.example_prompt = None  # 用完即清空

if prompt:
    st.session_state.messages.append({"role": "user", "content": prompt})
    
    # 顯示所有歷史訊息
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # 產生新回覆
    with st.chat_message("assistant"):
        response_text = get_gemini_response(prompt)
        st.markdown(response_text)
    
    st.session_state.messages.append({"role": "assistant", "content": response_text})
    st.rerun()  # 強制重新整理以保持對話流暢
else:
    # 僅在沒有新輸入時顯示歷史訊息
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])