import json
import urllib.request
import urllib.error
from typing import List, Dict, Any, Tuple
from datetime import datetime

from .db_tools import (
    db_get_active_system_prompt,
    db_add_system_prompt,
    db_get_tasks,
    db_update_task_status,
    db_delete_task,
    db_add_task,
    db_get_rules
)
from .schemas import TOOLS_SCHEMA

OLLAMA_URL = "http://192.168.11.3:11434/api/chat"
MODEL_NAME = "qwen2.5:14b"

def format_rules_output(rules: List[Dict[str, Any]]) -> str:
    if not rules:
        return "База знаний пока пуста."
    lines = [f"📚 База знаний и правила арбитража ({len(rules)}):\n"]
    for idx, r in enumerate(rules, 1):
        rule_text = r.get("rule_text", "").strip()
        lines.append(f"{idx}. {rule_text}\n")
    return "\n".join(lines).strip()

def process_chat_message(user_id: int, user_message: str, chat_history: List[Dict[str, Any]] = None) -> Tuple[str, List[Dict[str, Any]]]:
    if chat_history is None:
        chat_history = []

    current_now = datetime.now().strftime("%Y-%m-%d %H:%M")
    
    # Запрос к локальной модели Qwen (полный цикл Function Calling)
    dynamic_prompt_text = db_get_active_system_prompt()
    system_prompt = {
        "role": "system",
        "content": f"Текущая дата и время сервера: {current_now}.\n\n{dynamic_prompt_text}"
    }

    messages = [system_prompt] + chat_history + [{"role": "user", "content": user_message}]

    payload = {
        "model": MODEL_NAME,
        "messages": messages,
        "tools": TOOLS_SCHEMA,
        "stream": False,
        "options": {"num_predict": 2048, "num_ctx": 8192, "temperature": 0.1}
    }

    try:
        req = urllib.request.Request(
            OLLAMA_URL,
            data=json.dumps(payload).encode("utf-8"),
            headers={"Content-Type": "application/json"}
        )
        with urllib.request.urlopen(req) as response:
            res_data = json.loads(response.read().decode("utf-8"))
            msg = res_data.get("message", {})
            tool_calls = msg.get("tool_calls", [])

            if tool_calls:
                messages.append(msg)
                
                for tool in tool_calls:
                    fn_name = tool["function"]["name"]
                    fn_args = tool["function"].get("arguments", {})
                    tool_result_content = ""

                    if fn_name == "db_get_tasks":
                        tasks = db_get_tasks(user_id)
                        tool_result_content = json.dumps(tasks, ensure_ascii=False)

                    elif fn_name in ["db_get_system_prompt", "db_get_system_prompts"]:
                        tool_result_content = db_get_active_system_prompt()

                    elif fn_name == "db_add_system_prompt":
                        res = db_add_system_prompt(
                            name=fn_args.get("name", "main_agent"), 
                            prompt_text=fn_args.get("prompt_text")
                        )
                        tool_result_content = json.dumps(res, ensure_ascii=False)

                    elif fn_name == "db_get_rules":
                        tool_result_content = json.dumps(db_get_rules(), ensure_ascii=False)

                    elif fn_name == "db_add_task":
                        res = db_add_task(user_id=user_id, module=fn_args.get("module", "general"), title=fn_args.get("title"), priority=fn_args.get("priority", "MEDIUM"), due_date=fn_args.get("due_date"))
                        tool_result_content = json.dumps(res, ensure_ascii=False)

                    elif fn_name == "db_update_task_status":
                        res = db_update_task_status(user_id=user_id, task_id=str(fn_args.get("task_id")), status=fn_args.get("status", "COMPLETED"), due_date=fn_args.get("due_date"))
                        tool_result_content = json.dumps(res, ensure_ascii=False)

                    elif fn_name == "db_delete_task":
                        res = db_delete_task(user_id=user_id, task_id=str(fn_args.get("task_id", "")).upper())
                        tool_result_content = json.dumps(res, ensure_ascii=False)

                    messages.append({
                        "role": "tool",
                        "content": tool_result_content
                    })

                # Вторичный запрос модели для формирования итогового ответа оператору
                second_payload = {
                    "model": MODEL_NAME,
                    "messages": messages,
                    "stream": False,
                    "options": {"num_predict": 2048, "num_ctx": 8192, "temperature": 0.1}
                }
                sec_req = urllib.request.Request(
                    OLLAMA_URL,
                    data=json.dumps(second_payload).encode("utf-8"),
                    headers={"Content-Type": "application/json"}
                )
                with urllib.request.urlopen(sec_req) as sec_response:
                    sec_res_data = json.loads(sec_response.read().decode("utf-8"))
                    final_content = sec_res_data.get("message", {}).get("content", "").strip().replace("**", "")
                    return final_content, chat_history + [{"role": "user", "content": user_message}, {"role": "assistant", "content": final_content}]

            content_str = msg.get("content", "").strip().replace("**", "")
            return content_str or "Запрос обработан.", chat_history + [{"role": "user", "content": user_message}, {"role": "assistant", "content": content_str}]

    except urllib.error.URLError as e:
        return f"Ошибка связи с Ollama ({OLLAMA_URL}): {e}", chat_history