from typing import Dict, List, Optional
from pydantic import BaseModel, Field
from openai import OpenAI

class SkillIntent(BaseModel):
    """技能意图结构定义"""
    intent_type: str = Field(..., description="意图类型：monitoring/automation/analysis/transformation")
    target_domain: str = Field(..., description="目标领域：seo/ecommerce/office/data")
    action_description: str = Field(..., description="动作描述：用户自然语言原文")
    parameters: Dict[str, any] = Field(..., description="提取的参数键值对")
    required_tools: List[str] = Field(..., description="所需工具列表：['http_client', 'database', 'scheduler']")
    estimated_complexity: str = Field(..., description="预估复杂度：low/medium/high")

class NL2SkillParser:
    def __init__(self, api_key: str):
        self.client = OpenAI(api_key=api_key)
        self.system_prompt = """
你是一个技能意图解析专家。用户会用自然语言描述他们想要的自动化技能。
你的任务是：
1. 识别意图类型（monitoring/automation/analysis/transformation）
2. 确定目标领域（seo/ecommerce/office/data）
3. 提取关键参数（如：监控频率、目标 URL、告警阈值等）
4. 列出所需工具（HTTP 客户端、数据库、定时器等）
5. 评估复杂度（low/medium/high）

请严格按照 JSON Schema 输出，不要添加任何额外说明。
"""

    def parse(self, user_input: str) -> SkillIntent:
        """解析用户输入，生成技能意图"""
        response = self.client.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[
                {"role": "system", "content": self.system_prompt},
                {"role": "user", "content": user_input}
            ],
            response_format={"type": "json_schema", "json_schema": {"name": "SkillIntent", "schema": SkillIntent.model_json_schema()}}
        )
        
        intent_data = json.loads(response.choices[0].message.content)
        return SkillIntent(**intent_data)

# 使用示例
parser = NL2SkillParser(api_key="sk-...")
intent = parser.parse("帮我监控亚马逊上竞品 iPhone 15 的价格，每小时检查一次，如果降价超过 5% 就发邮件通知我")

# 输出:
# SkillIntent(
#   intent_type="monitoring",
#   target_domain="ecommerce",
#   action_description="帮我监控亚马逊上竞品 iPhone 15 的价格...",
#   parameters={
#     "target_url": "amazon.com/iphone-15",
#     "product_name": "iPhone 15",
#     "check_frequency": "hourly",
#     "price_drop_threshold": 0.05,
#     "notification_method": "email"
#   },
#   required_tools=["http_client", "scheduler", "email_sender"],
#   estimated_complexity="medium"
# )

from jinja2 import Template
from pathlib import Path

class SkillGenerator:
    def __init__(self, template_dir: str = "templates"):
        self.template_dir = Path(template_dir)
        self.templates = self._load_templates()
    
    def _load_templates(self) -> Dict[str, Template]:
        """加载预定义模板"""
        templates = {}
        for file in self.template_dir.glob("*.jinja2"):
            templates[file.stem] = Template(file.read_text())
        return templates
    
    def generate(self, intent: SkillIntent) -> Dict[str, str]:
        """根据意图生成技能代码和配置"""
        template_key = f"{intent.intent_type}_{intent.target_domain}"
        
        if template_key not in self.templates:
            # 降级到通用模板
            template_key = "generic"
        
        template = self.templates[template_key]
        
        # 渲染 Python 代码
        python_code = template.render(
            intent=intent,
            parameters=intent.parameters,
            tools=intent.required_tools
        )
        
        # 生成 YAML 配置
        yaml_config = self._generate_yaml_config(intent)
        
        # 生成 README 文档
        readme = self._generate_readme(intent)
        
        return {
            "main.py": python_code,
            "skill.yaml": yaml_config,
            "README.md": readme
        }
    
    def _generate_yaml_config(self, intent: SkillIntent) -> str:
        """生成 YAML 配置文件"""
        return f"""
name: {intent.target_domain}_skill_{hash(intent.action_description)}
version: 1.0.0
description: {intent.action_description[:100]}
author: auto-generated
category: {intent.target_domain}
tags: [{intent.target_domain}, {intent.intent_type}]
parameters:
{chr(10).join([f'  {k}: {{value}}' for k in intent.parameters.keys()])}
tools_required:
{chr(10).join([f'  - {tool}' for tool in intent.required_tools])}
pricing:
  pay_per_call: 0.05
  subscription: 29.99
"""

# 使用示例
generator = SkillGenerator()
files = generator.generate(intent)

# 输出 files['main.py'] 内容示例:
# """
# 自动生成的电商价格监控技能
# """
# import requests
# import smtplib
# from datetime import datetime
# 
# def check_price(url: str) -> float:
#     response = requests.get(url)
#     # 解析 HTML 提取价格...
#     return price
# 
# def send_email_alert(subject: str, body: str):
# 
# def main():
#     current_price = check_price("amazon.com/iphone-15")
#     if price_drop > 0.05:
#         send_email_alert("价格下降提醒！", f"iPhone 15 降价{price_drop*100}%")
# """