import os
import json
from typing import Dict, Any, Optional, List
from ddms_compliance_suite.test_framework_core import BaseAPITestCase, TestSeverity, ValidationResult, APIRequestContext, APIResponseContext

class LLMComplianceCheckTestCase(BaseAPITestCase):
    id = "TC-LLM-COMPLIANCE-001"
    name = "LLM合规性综合检查"
    description = "读取固定的合规性标准列表，将API所有关键信息（url、headers、params、query、body、示例响应等）发送给大模型，让其判断是否通过并给出理由。"
    severity = TestSeverity.MEDIUM
    tags = ["llm", "compliance", "auto-eval"]
    execution_order = 99

    def __init__(self, endpoint_spec: Dict[str, Any], global_api_spec: Dict[str, Any], json_schema_validator: Optional[Any] = None, llm_service: Optional[Any] = None):
        super().__init__(endpoint_spec, global_api_spec, json_schema_validator=json_schema_validator, llm_service=llm_service)
        # 读取合规性标准
        criteria_path = os.path.join(os.path.dirname(__file__), "compliance_criteria.json")
        with open(criteria_path, "r", encoding="utf-8") as f:
            self.compliance_criteria = json.load(f)
        self.logger.info(f"已加载合规性标准: {self.compliance_criteria}")

    def validate_response(self, response_context: APIResponseContext, request_context: APIRequestContext) -> List[ValidationResult]:
        results = []

        # 如果合规性标准列表为空，则跳过测试
        if not self.compliance_criteria:
            return [ValidationResult(
                passed=True,
                message="合规性标准列表为空，跳过LLM合规性检查。",
                details={"reason": "compliance_criteria.json is empty or contains an empty list."}
            )]

        # 收集API所有关键信息，包括实例数据和Schema定义
        api_info = {
            # API元数据
            "path_template": self.endpoint_spec.get("path"),
            "method": request_context.method,
            # "operationId": self.endpoint_spec.get("operationId"),
            "title": self.endpoint_spec.get("summary") or self.endpoint_spec.get("title"),
            "description": self.endpoint_spec.get("description") or self.endpoint_spec.get("desc"),
            "tags": self.endpoint_spec.get("tags"),
            
            # API Schema 定义 (从 endpoint_spec 获取)
            "schema_parameters": self.endpoint_spec.get("parameters"),
            "schema_request_body": self.endpoint_spec.get("requestBody"),
            "schema_responses": self.endpoint_spec.get("responses"),

            # API 调用实例数据 (从 request_context 和 response_context 获取)
            "instance_url": request_context.url,
            "instance_request_headers": dict(request_context.headers) if hasattr(request_context, "headers") else {},
            "instance_query_params": getattr(request_context, "query_params", {}),
            "instance_path_params": getattr(request_context, "path_params", {}),
            "instance_request_body": getattr(request_context, "body", None),
            "instance_response_status": response_context.status_code,
            "instance_response_headers": dict(response_context.headers) if hasattr(response_context, "headers") else {},
            "instance_response_body": response_context.text_content if hasattr(response_context, "text_content") else None
        }
        # 日志打印所有API信息
        self.logger.info("LLM合规性检查-API信息收集: " + json.dumps(api_info, ensure_ascii=False, indent=2))
        self.logger.info("LLM合规性检查-标准: " + json.dumps(self.compliance_criteria, ensure_ascii=False, indent=2))

        if not self.llm_service:
            results.append(ValidationResult(
                passed=True,
                message="LLM服务不可用，跳过本用例。",
                details={"reason": "llm_service is None"}
            ))
            return results

        # 构建prompt
        prompt = f"""
你是一位API合规性专家。请根据以下合规性标准，对给定的API调用信息进行逐条评估。每条标准请给出是否通过（true/false）和理由。

合规性标准:
{json.dumps(self.compliance_criteria, ensure_ascii=False, indent=2)}

API信息:
{json.dumps(api_info, ensure_ascii=False, indent=2)}

请以如下JSON格式输出：
[
  {{"criterion": "标准内容", "passed": true/false, "reason": "理由"}},
  ...
]
"""
        messages = [
            {"role": "system", "content": "你是一位API合规性专家，输出必须是严格的JSON数组。"},
            {"role": "user", "content": prompt}
        ]
        self.logger.info("发送给LLM的prompt: " + prompt)
        llm_response_str = self.llm_service._execute_chat_completion_request(
            messages=messages,
            max_tokens=2048,
            temperature=0.2
        )
        if not llm_response_str:
            results.append(ValidationResult(
                passed=False,
                message="未能从LLM获取响应。",
                details={"prompt": prompt}
            ))
            return results
        self.logger.info(f"LLM原始响应: {llm_response_str}")
        try:
            cleaned = llm_response_str.strip()
            if cleaned.startswith("```json"):
                cleaned = cleaned[7:]
            if cleaned.endswith("```"):
                cleaned = cleaned[:-3]
            llm_result = json.loads(cleaned)
            if not isinstance(llm_result, list):
                raise ValueError("LLM返回的不是JSON数组")
            for item in llm_result:
                criterion = item.get("criterion", "未知标准")
                passed = item.get("passed", False)
                reason = item.get("reason", "无理由")
                results.append(ValidationResult(
                    passed=passed,
                    message=f"[{criterion}] {'通过' if passed else '不通过'}: {reason}",
                    details={"criterion": criterion, "llm_reason": reason}
                ))
        except Exception as e:
            results.append(ValidationResult(
                passed=False,
                message=f"LLM响应解析失败: {e}",
                details={"raw_llm_response": llm_response_str}
            ))
        return results