import httpx
import json
import logging
import html
from config import ANTCHAT_BASE_URL, ANTCHAT_API_KEY, CHAT_MODEL_SPECS

logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s [%(levelname)s] %(message)s"
)
logger = logging.getLogger(__name__)

def get_model_response(model_id, history, system_prompt, temperature, escape_html=True):
    """
    与 AntChat API 交互以获取模型响应。
    """
    print(f"[get_model_response] History: {history}")
    # The model_id passed in is now the ground truth, potentially overridden by local.py
    api_model_id = CHAT_MODEL_SPECS[model_id]["model_id"]

    # 如果 local.py 存在 get_local_model_id_map 函数，则使用它来覆盖模型 ID
    try:
        from local import get_local_model_id_map
        local_map = get_local_model_id_map()
        if model_id in local_map:
            api_model_id = local_map[model_id]
            logger.info(f"使用本地模型 ID 映射: {model_id} -> {api_model_id}")
    except ImportError:
        logger.info("local.py 未找到，使用默认模型 ID 映射。")
    except Exception as e:
        logger.error(f"获取本地模型 ID 映射时出错: {e}")
    
    headers = {
        "Authorization": f"Bearer {ANTCHAT_API_KEY}",
        "Content-Type": "application/json",
    }
    
    # 构建消息历史
    messages = [{"role": "system", "content": system_prompt}]
    for user_msg, assistant_msg in history:
        # 关键修复：只处理包含用户消息的轮次，以过滤掉UI的初始欢迎语
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
            # 只有在用户消息之后，才可能追加对应的助手消息
            if assistant_msg:
                messages.append({"role": "assistant", "content": assistant_msg})

    print(f"[get_model_response] Messages: {messages}")

    json_data = {
        "model": api_model_id,
        "messages": messages,
        "stream": True,
        "temperature": temperature,
    }

    logger.debug(f"请求 URL: {ANTCHAT_BASE_URL}/chat/completions")
    logger.debug(f"请求头: {headers}")
    logger.debug(f"请求体: {json.dumps(json_data, ensure_ascii=False)}")
    try:
        with httpx.stream(
            "POST",
            f"{ANTCHAT_BASE_URL}/chat/completions",
            headers=headers,
            json=json_data,
            timeout=120,
        ) as response:
            logger.debug(f"响应状态码: {response.status_code}")
            response.raise_for_status()
            for chunk in response.iter_lines():
                if chunk.startswith("data:"):
                    chunk = chunk[5:]
                    if chunk.strip() == "[DONE]":
                        break
                    try:
                        data = json.loads(chunk)
                        if "choices" in data and data["choices"]:
                            delta = data["choices"][0].get("delta", {})
                            content_chunk = delta.get("content")
                            if content_chunk:
                                yield html.escape(content_chunk) if escape_html else content_chunk
                            
                            elif "tool_calls" in delta:
                                tool_calls = delta.get("tool_calls", [])
                                if tool_calls:
                                    func_chunk = tool_calls[0].get("function", {})
                                    args_chunk = func_chunk.get("arguments")
                                    if args_chunk:
                                        yield html.escape(args_chunk) if escape_html else args_chunk
                    except json.JSONDecodeError as e:
                        logger.error(f"JSON 解析错误: {e}, 数据: {chunk}")
    except Exception as e:
        logger.error(f"请求异常: {e}")

def perform_web_search(query):
    # 调用 Tavily 或 Serper API
    #...
    return "搜索结果摘要"

def generate_code_for_tab(system_prompt, user_prompt, code_type, model_choice):
    """
    为代码生成标签页调用 Ring 模型。
    """
    logger.info(f"为 '{code_type}' 类型生成代码，Prompt: '{user_prompt}', Model: '{model_choice}'")
    
    # 从 UI 的选项中解析出模型名称
    if "Ling-1T" in model_choice:
        model_name = "ling-1t"
    elif "Ring-flash-2.0" in model_choice:
        model_name = "ring-flash-2.0"
    else:
        # 默认或备用模型
        model_name = "inclusionai/ling-1t"
        logger.warning(f"未知的模型选项 '{model_choice}', 回退到默认模型 'inclusionai/ling-1t'")

    history = [[user_prompt, None]]
    temperature = 0.7
    # For code, we don't want to escape HTML entities
    yield from get_model_response(model_name, history, system_prompt, temperature, escape_html=False)