import json
from openai import OpenAI
from  kg_build_v2.Prompt import Prompt_anli
from  kg_build_v2.extract_html import extract_fault_sections,extract_by_unit,extract_cases,extract_content_slices
import re
from modelsAPI.model_api import OpenaiAPI

def read_markdown_file(file_path):
    """
    读取 Markdown 文件并返回其全部内容作为字符串。

    参数:
        file_path (str): Markdown 文件的路径（支持相对或绝对路径）

    返回:
        str 或 None: 成功时返回文件内容；出错时返回 None
    """
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            content = f.read()
        return content
    except FileNotFoundError:
        print(f"错误：文件未找到 - {file_path}")
    except PermissionError:
        print(f"错误：没有权限读取文件 - {file_path}")
    except UnicodeDecodeError:
        print(f"错误：文件编码不是 UTF-8，无法读取 - {file_path}")
    except Exception as e:
        print(f"读取文件时发生未知错误: {e}")
    return None


def safe_json_loads(s: str):
    try:
        return json.loads(s)
    except json.JSONDecodeError as e:
        # 尝试修复常见问题
        s = s.replace("\\n", "\\\n").replace("\r", "\\r").replace("\t", "\\t")
        # 或者直接清理非法字符
        import re
        s = re.sub(r'[\x00-\x1f\x7f]', '', s)  # 移除控制字符
        try:
            return json.loads(s)
        except:
            raise e
        
# def openai_chat(query:str, model="Qwen3-32B"):
        
#         """调用OpenAI API"""
#         try:
#             client = OpenAI(
#                 api_key= "none",
#                 base_url= "http://192.168.0.46:59800/v1"
#             )

#             response = client.chat.completions.create(
#                 model=model,
#                 messages=[
#                     {"role": "user", "content": query},
#                 ],
#                 temperature=0.1,
#                 stream=False,
#                 response_format = {"type": "json_object"},
#                 extra_body={"chat_template_kwargs": {"enable_thinking": False}}
#             )
#             return response.choices[0].message.content
#         except Exception as e:
#             print(f"调用OpenAI API时出错: {e}")
#             return None  

def extract_wxanli(chunks):
    """
    批量处理一个文档的多个切片（chunks），每个切片调用大模型结构化，并返回全部结果。
    
    :param chunks: List[str] - 同一文档的多个文本切片
    :return: List[Dict] - 结构化结果列表
    """
    all_results = []
    all_entities = []
    all_relationships = []

    for idx, block in enumerate(chunks, 1):
        print(f"=== 维修案例 Block {idx} ===")
        print(block)
        print("\\n" + "="*60 + "\\n")

        final_prompt = Prompt_anli.replace("text",block)
        
        raw_response =  OpenaiAPI.openai_chat(final_prompt,timeout=180)
        
        # 尝试解析 JSON
        try:
            cleaned = raw_response.strip()
            if cleaned.startswith("```json"):
                cleaned = cleaned[7:].lstrip()
            if cleaned.endswith("```"):
                cleaned = cleaned[:-3].rstrip()
            
            # ✅ 修复：移除 ensure_ascii 和 indent
            json_result = safe_json_loads(cleaned)
            entities = json_result.get("entities", [])
            relationships = json_result.get("relationships", [])
            for entity in entities:
                # 确保 entity 有 "properties" 键，且其值为字典
                if "properties" in entity and isinstance(entity["properties"], dict):
                    entity["properties"]["切片"] = block
            # 打印美化版
            all_entities.extend(entities)
            all_relationships.extend(relationships)
            # 将当前结果追加到总列表
            # all_results.append({
            #     "entities": entities,          # 已添加切片属性的实体列表
            #     "relationships": relationships,  # 关系列表
            #     })
            # 可选：打印美化版（使用 dumps）

            success = True
        except Exception as e:
            print(f"  ⚠️ 第 {idx} 切片 JSON 解析失败: {e}")
            print(f"  原始响应: {repr(raw_response)}")
            continue  # 跳过保存失败项

    
    result = {
        "entities": all_entities,
        "relationships": all_relationships
    }
    return result




def get_wxanli_entity_rela(markdown_content):
    keywords = ["一、故障现象", "故障名称：", "单位："]
    chunks = extract_content_slices(markdown_content,keywords)

    valid_chunks = [chunk for chunk in chunks if chunk and len(chunk.strip()) > 10]
    if valid_chunks:
        # 调用处理函数
        results = extract_wxanli(valid_chunks)
        
        # 这里可以将 results 保存到文件，例如按文件名保存为 JSON

    else:
        results = []

    
    return results






# if __name__ == "__main__":
#     input_directory = r"F:\zklnlp\HJ\hj_kg_code\kgrag\table_extract\files-1\图解目录_shenghna_output11_full_content.md"
    
#     # 调用目录处理函数
#     mdcontent = read_markdown_file(input_directory)
    
#     if mdcontent is None:
#         print("无法读取文件，程序退出")
#         exit(1)
    
#     res = get_wxanli_entity_rela(markdown_content=mdcontent)
#     print(res)