#  Copyright 2026 zhaoxi826
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

from pretor.worker_individual.base_individual import BaseIndividual, WorkerIndividualDeps
from pretor.utils.logger import get_logger
import os
import json
from pydantic_ai import Tool
import importlib.util

logger = get_logger('skill_individual')

class SkillIndividual(BaseIndividual):
    """
    专家子个体：拥有专业 skill 的 agent。
    """

    def __init__(self, agent_config: dict):
        super().__init__(agent_config)

    async def _load_skill_tools(self):
        """动态加载已绑定的 skill 工具。"""
        tools = []
        bound_skill = self.agent_config.get("bound_skill", "")
        # bound_skill can be string or dict {"skill_name": ["file1", "file2"]}
        skill_mapper = {}
        if isinstance(bound_skill, str) and bound_skill:
            try:
                skill_mapper = json.loads(bound_skill)
            except json.JSONDecodeError:
                pass
        elif isinstance(bound_skill, dict):
            skill_mapper = bound_skill

        skill_base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "plugin", "skill"))

        for skill_name, _ in skill_mapper.items():
            skill_path = os.path.join(skill_base_dir, skill_name)
            metadata_path = os.path.join(skill_path, "metadata.json")
            if not os.path.exists(metadata_path):
                continue

            try:
                with open(metadata_path, 'r', encoding='utf-8') as f:
                    metadata = json.load(f)
            except Exception as e:
                logger.error(f"Failed to load metadata for skill {skill_name}: {e}")
                continue

            if "functions" in metadata:
                for func_info in metadata["functions"]:
                    # Ensure path is absolute
                    script_path = func_info.get("file_path", "")
                    if not os.path.isabs(script_path):
                        script_path = os.path.join(skill_path, script_path)

                    if not os.path.exists(script_path):
                        logger.warning(f"Skill script not found: {script_path}")
                        continue

                    func_name = func_info.get("name")
                    try:
                        # Dynamically load the python module
                        spec = importlib.util.spec_from_file_location(func_name, script_path)
                        module = importlib.util.module_from_spec(spec)
                        spec.loader.exec_module(module)

                        func = getattr(module, func_name)
                        if callable(func):
                            # Convert to PydanticAI Tool
                            tool = Tool(func, name=func_name, description=func_info.get("docstring", ""))
                            tools.append(tool)
                            logger.info(f"Loaded skill tool: {func_name} from {skill_name}")
                    except Exception as e:
                        logger.error(f"Failed to load function {func_name} from {script_path}: {e}")

        return tools

    async def run(self, task_event: dict) -> dict:
        if self.agent is None:
            system_prompt = self.agent_config.get("prompt",
                                                  "你是一个拥有专业技能的专家级AI助手，请利用你的专业知识完成给定的任务。")
            await self._init_agent("skill_individual", system_prompt)

        deps = WorkerIndividualDeps(task_event=task_event)
        self.agent.retries = 3

        tools = await self._load_skill_tools()

        try:
            result = await self.agent.run(
                f"请执行以下任务：\n{task_event}",
                deps=deps,
                tools=tools if tools else None
            )
            return {"output": result.data.output}
        except Exception as e:
            logger.exception(f"SkillIndividual {self.agent_id} 执行失败: {e}")
            raise