# cockpit_chat.dspy - Product-grade LLM conversation with context
# POST: action=send_message, iteration_id, message_text, model_id, file_paths
# GET:  action=list_messages, iteration_id, task_id

import aiohttp

action = (params_kw or {}).get('action', 'list_messages')
dbname = get_module_dbname('pipeline-sdlc')

DEFAULT_SYSTEM_PROMPT = """你是一个专业的软件开发 Agent，名为「开发产线驾驶舱」。你的职责是帮助用户完成软件开发生命周期的各个环节：需求分析、设计、编码、测试、部署。

对话规则：
1. 简洁专业，用中文回复
2. 当用户描述需求时，先理解并复述确认，然后给出分析和建议
3. 如果用户提到了项目/迭代，主动关联上下文
4. 可以建议启动开发产线来推进工作
5. 对于代码相关问题，给出具体的代码示例
6. 记住对话历史，保持上下文连贯

当前你可以帮助用户完成：
- 创建和管理项目、迭代
- 数据表设计、CRUD 设计、API 设计
- 代码生成、规范检查、自动修复
- 测试用例生成、功能测试、Bug 管理
- 环境部署和验证"""


async def _load_agent_settings(sor, uid):
    """Load user's agent settings, return defaults if not set."""
    recs = await sor.sqlExe(
        "SELECT default_llm_id, system_prompt, temperature, max_context_messages FROM sd_agent_settings WHERE user_id=${uid}$",
        {"uid": uid}
    )
    if recs:
        r = recs[0]
        return {
            'llm_id': getattr(r, 'default_llm_id', None),
            'system_prompt': getattr(r, 'system_prompt', None) or DEFAULT_SYSTEM_PROMPT,
            'temperature': float(getattr(r, 'temperature', 0.7) or 0.7),
            'max_context': int(getattr(r, 'max_context_messages', 30) or 30),
        }
    return {
        'llm_id': None,
        'system_prompt': DEFAULT_SYSTEM_PROMPT,
        'temperature': 0.7,
        'max_context': 30,
    }


async def _select_model(sor, preferred_llm_id, has_files):
    """Select best model: prefer user choice, then multimodal if files, else first active text."""
    # If user has preferred model, use it
    if preferred_llm_id:
        recs = await sor.sqlExe(
            "SELECT id, name, provider, model_id, api_base, api_key, capabilities FROM llm WHERE id=${lid}$ AND status='active'",
            {"lid": preferred_llm_id}
        )
        if recs:
            return recs[0]

    # Auto-select based on file presence
    if has_files:
        recs = await sor.sqlExe(
            "SELECT id, name, provider, model_id, api_base, api_key, capabilities FROM llm WHERE status='active' AND capabilities LIKE '%multimodal%' LIMIT 1",
            {}
        )
        if recs:
            return recs[0]

    # Fallback: first active model
    recs = await sor.sqlExe(
        "SELECT id, name, provider, model_id, api_base, api_key, capabilities FROM llm WHERE status='active' LIMIT 1",
        {}
    )
    if recs:
        return recs[0]
    return None


async def _build_context(sor, iteration_id, task_id, max_msgs, system_prompt):
    """Build LLM messages array with Hermes-style context."""
    messages = [{"role": "system", "content": system_prompt}]

    # Project/iteration context
    if iteration_id:
        iters = await sor.sqlExe(
            "SELECT i.iteration_name, i.iteration_type, i.status, i.scope, p.name as project_name, p.description, p.tech_stack "
            "FROM sd_iterations i LEFT JOIN sd_projects p ON i.project_id=p.id WHERE i.id=${iid}$",
            {"iid": iteration_id}
        )
        if iters:
            it = iters[0]
            ctx_parts = ["## 当前上下文"]
            ctx_parts.append(f"项目: {getattr(it, 'project_name', '未知')}")
            ctx_parts.append(f"迭代: {getattr(it, 'iteration_name', '未知')}")
            ctx_parts.append(f"类型: {getattr(it, 'iteration_type', '')}")
            ctx_parts.append(f"状态: {getattr(it, 'status', '')}")
            desc = getattr(it, 'description', '')
            if desc:
                ctx_parts.append(f"项目描述: {desc[:500]}")
            stack = getattr(it, 'tech_stack', '')
            if stack:
                ctx_parts.append(f"技术栈: {stack[:300]}")
            scope = getattr(it, 'scope', '')
            if scope:
                ctx_parts.append(f"迭代范围: {scope[:500]}")
            messages.append({"role": "system", "content": "\n".join(ctx_parts)})

    # Task context
    if task_id:
        tasks = await sor.sqlExe(
            "SELECT id, status, pipeline_id FROM pipeline_tasks WHERE id=${tid}$",
            {"tid": task_id}
        )
        if tasks:
            t = tasks[0]
            messages.append({"role": "system", "content": f"关联 Pipeline 任务: {t.id}, 状态: {getattr(t, 'status', 'unknown')}"})

    # Conversation history
    where = []
    params = {}
    if task_id:
        where.append("task_id=${tid}$")
        params["tid"] = task_id
    if iteration_id:
        where.append("iteration_id=${iid}$")
        params["iid"] = iteration_id

    if where:
        sql = f"SELECT role, content FROM sd_conversations WHERE {' OR '.join(where)} ORDER BY created_at DESC LIMIT ${max_msgs}$"
        params["max_msgs"] = max_msgs
        history = await sor.sqlExe(sql, params)
        # Reverse to chronological order
        for h in reversed(history):
            role = getattr(h, 'role', 'user')
            content = getattr(h, 'content', '')
            if role in ('user', 'agent'):
                messages.append({"role": "user" if role == "user" else "assistant", "content": content})

    return messages


async def _call_llm(model_info, messages, temperature):
    """Call LLM API directly using model config from llm table."""
    api_base = model_info.api_base.rstrip('/')
    api_key = model_info.api_key
    model_id = model_info.model_id

    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    }
    payload = {
        "model": model_id,
        "messages": messages,
        "temperature": temperature,
    }

    timeout = aiohttp.ClientTimeout(total=120)
    async with aiohttp.ClientSession(timeout=timeout) as session:
        async with session.post(f"{api_base}/chat/completions", headers=headers, json=payload) as resp:
            if resp.status != 200:
                text = await resp.text()
                raise ValueError(f"LLM API error {resp.status}: {text[:300]}")
            data = await resp.json()
            content = data["choices"][0]["message"]["content"]
            # Truncate overly long responses
            if len(content) > 8000:
                content = content[:8000] + "\n\n...(内容过长已截断)"
            return content


# ==================== ACTION HANDLERS ====================

if action == 'send_message':
    iteration_id = (params_kw or {}).get('iteration_id', '')
    task_id = (params_kw or {}).get('task_id', '')
    message_text = (params_kw or {}).get('message_text', '').strip()
    model_id = (params_kw or {}).get('model_id', '')
    file_paths_raw = (params_kw or {}).get('file_paths', '[]')

    if not message_text:
        return json.dumps({"error": "message_text is required"}, ensure_ascii=False)

    # Parse file paths
    has_files = False
    try:
        fps = json.loads(file_paths_raw)
        has_files = len(fps) > 0
    except Exception:
        pass

    uid = await get_user()

    async with DBPools().sqlorContext(dbname) as sor:
        # 1. Save user message
        msg_id = getID()
        await sor.C('sd_conversations', {
            'id': msg_id,
            'iteration_id': iteration_id or '',
            'task_id': task_id or '',
            'step_name': '',
            'role': 'user',
            'content': message_text,
            'attachments': file_paths_raw,
            'msg_type': 'text',
            'org_id': '0',
            'created_by': uid
        })

        # 2. Load agent settings
        settings = await _load_agent_settings(sor, uid)

        # 3. Select model (preferred → auto by file type → first active)
        selected_llm_id = model_id or settings['llm_id']
        model_info = await _select_model(sor, selected_llm_id, has_files)
        if not model_info:
            return json.dumps({"error": "No active LLM model configured"}, ensure_ascii=False)

        # 4. Build context
        messages = await _build_context(
            sor, iteration_id, task_id,
            settings['max_context'], settings['system_prompt']
        )
        # Append current user message
        messages.append({"role": "user", "content": message_text})

        # 5. Call LLM
        try:
            agent_reply = await _call_llm(model_info, messages, settings['temperature'])
        except Exception as e:
            agent_reply = f"抱歉，模型调用失败: {str(e)[:200]}"

        # 6. Save agent response
        agent_msg_id = getID()
        await sor.C('sd_conversations', {
            'id': agent_msg_id,
            'iteration_id': iteration_id or '',
            'task_id': task_id or '',
            'step_name': '',
            'role': 'agent',
            'content': agent_reply,
            'attachments': '[]',
            'msg_type': 'text',
            'org_id': '0',
            'created_by': 'system'
        })

    return json.dumps({
        "success": True,
        "message_id": msg_id,
        "agent_reply": agent_reply,
        "model_used": model_info.name,
    }, ensure_ascii=False)


else:
    # list_messages - return conversation as Bricks widget JSON
    iteration_id = (params_kw or {}).get('iteration_id', '')
    task_id = (params_kw or {}).get('task_id', '')

    msgs = []
    if iteration_id or task_id:
        async with DBPools().sqlorContext(dbname) as sor:
            where = []
            params = {}
            if task_id:
                where.append("task_id=${tid}$")
                params["tid"] = task_id
            if iteration_id:
                where.append("iteration_id=${iid}$")
                params["iid"] = iteration_id

            sql = f"SELECT role, content, msg_type, created_at FROM sd_conversations WHERE {' OR '.join(where)} ORDER BY created_at ASC LIMIT 50"
            msgs = await sor.sqlExe(sql, params)

    msg_widgets = []
    for m in msgs:
        role = m.role if hasattr(m, 'role') else ''
        content = m.content if hasattr(m, 'content') else ''

        if role == 'agent':
            bg = '#e8f0fe'
            align = 'flex-start'
            label = 'Agent'
            label_color = '#3b82f6'
        elif role == 'user':
            bg = '#dbeafe'
            align = 'flex-end'
            label = '\u4f60'
            label_color = '#2563eb'
        else:
            bg = '#f1f5f9'
            align = 'center'
            label = '\u7cfb\u7edf'
            label_color = '#94a3b8'

        msg_widgets.append({
            "widgettype": "VBox",
            "options": {
                "width": "85%",
                "alignSelf": align,
                "bgcolor": bg,
                "borderRadius": "12px",
                "padding": "12px 16px",
                "marginBottom": "10px",
                "gap": "4px"
            },
            "subwidgets": [
                {"widgettype": "Text", "options": {
                    "text": label, "cfontsize": 0.75,
                    "color": label_color, "fontWeight": "bold"
                }},
                {"widgettype": "Text", "options": {
                    "text": content, "cfontsize": 0.95,
                    "color": "#1e293b", "whiteSpace": "pre-wrap"
                }}
            ]
        })

    if not msg_widgets:
        msg_widgets.append({
            "widgettype": "Text",
            "options": {
                "text": "\u6682\u65e0\u5bf9\u8bdd\u8bb0\u5f55\u3002\u9009\u62e9\u4e00\u4e2a\u8fed\u4ee3\u540e\uff0c\u5728\u4e0b\u65b9\u8f93\u5165\u6846\u4e2d\u5f00\u59cb\u5bf9\u8bdd\u3002",
                "cfontsize": 0.9, "color": "#94a3b8", "padding": "20px"
            }
        })

    return {
        "widgettype": "VBox",
        "options": {"width": "100%", "padding": "4px"},
        "subwidgets": msg_widgets
    }