ChingTech OS：AI 圖片生成與記憶功能

async def generate_image_with_huggingface(prompt: str) -> tuple[str | None, str | None]:
    """使用 Hugging Face FLUX.1-schnell 生成圖片"""
    token = get_hf_token()
    if not token:
        return None, "未設定 HUGGINGFACE_API_TOKEN"

    from huggingface_hub import InferenceClient

    client = InferenceClient(token=token, timeout=HUGGINGFACE_TIMEOUT)

    image = client.text_to_image(
        prompt,
        model="black-forest-labs/FLUX.1-schnell",
        guidance_scale=0.0,
        num_inference_steps=4,
    )

    # 儲存到 NAS 的 ai-images 目錄
    filename = f"flux_{uuid.uuid4().hex[:8]}.png"
    image_path = _nas_ai_images_dir / filename
    image.save(image_path)

    return f"ai-images/{filename}", None

# 檢查 nanobanana 是否有錯誤
nanobanana_error = extract_nanobanana_error(response.tool_calls)
nanobanana_timeout = check_nanobanana_timeout(response.tool_calls)

if nanobanana_error or nanobanana_timeout:
    # 提取原始 prompt，嘗試 fallback
    original_prompt = extract_nanobanana_prompt(response.tool_calls)

    if original_prompt:
        fallback_path, service_used, fallback_error = await generate_image_with_fallback(
            original_prompt, error_reason
        )

        if fallback_path:
            notification = get_fallback_notification(service_used)
            # 加入「使用備用服務」通知

# linebot_ai.py - build_system_prompt()
memories = []
if line_group_id:
    memories = await get_active_group_memories(line_group_id)
elif line_user_uuid:
    memories = await get_active_user_memories(line_user_uuid)

if memories:
    memory_lines = [f"{i+1}. {m['content']}" for i, m in enumerate(memories)]
    memory_block = """

【自訂記憶】
以下是此對話的自訂記憶，請在回應時遵循這些規則：
""" + "\n".join(memory_lines) + """

請自然地遵循上述規則，不需要特別提及或確認。"""
    base_prompt += memory_block

@mcp.tool()
async def add_memory(
    content: str,
    title: str | None = None,
    line_group_id: str | None = None,
    line_user_id: str | None = None,
) -> str:
    # 自動產生標題（取 content 前 20 字）
    if not title:
        title = content[:20] + ("..." if len(content) > 20 else "")

    if line_group_id:
        # 群組記憶
        ...
    elif line_user_id:
        # 個人記憶
        ...

if quoted_message_id:
    # 1. 嘗試查詢圖片
    image_info = await get_image_info_by_line_message_id(quoted_message_id)
    if image_info:
        quoted_image_path = await ensure_temp_image(
            quoted_message_id, image_info["nas_path"]
        )

    # 2. 嘗試查詢檔案
    elif file_info := await get_file_info_by_line_message_id(quoted_message_id):
        quoted_file_path = await ensure_temp_file(...)

    # 3. 嘗試查詢文字訊息
    elif msg_info := await get_message_content_by_line_message_id(quoted_message_id):
        quoted_text_content = {
            "content": msg_info["content"],
            "display_name": msg_info.get("display_name", ""),
            "is_from_bot": msg_info.get("is_from_bot", False),
        }

if quoted_image_path:
    user_message = f"[回覆圖片: {quoted_image_path}]\n{user_message}"

def should_trigger_ai(message_content, is_group, is_reply_to_bot=False):
    if not is_group:
        return True        # 個人對話：全部觸發

    if is_reply_to_bot:
        return True        # 回覆 Bot 訊息：觸發

    # 檢查是否被 @ 提及
    for name in settings.line_bot_trigger_names:
        if f"@{name.lower()}" in content_lower:
            return True

    return False

async def fetch_image(keyword: str, source: str = "pexels") -> Optional[bytes]:
    if source == "huggingface":
        return await generate_huggingface_image(keyword)
    elif source == "nanobanana":
        return await generate_nanobanana_image(keyword)
    else:
        return await fetch_pexels_image(keyword)