package com.supervision.demo;

import cn.hutool.core.collection.CollUtil;
import cn.hutool.core.util.StrUtil;
import cn.hutool.json.JSONUtil;
import com.supervision.common.domain.R;
import com.supervision.neo4j.controller.Neo4jController;
import com.supervision.neo4j.domain.CaseNode;
import com.supervision.demo.controller.ExampleChatController;
import com.supervision.police.domain.ModelRecordType;
import com.supervision.police.domain.NoteRecordSplit;
import com.supervision.police.service.ModelRecordTypeService;
import com.supervision.police.service.NoteRecordSplitService;
import com.supervision.thread.RecordSplitTypeThread;
import lombok.extern.slf4j.Slf4j;
import org.junit.jupiter.api.Test;
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatClient;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.util.StopWatch;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;


@Slf4j
@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)
public class FuHsiApplicationTests {

    @Autowired
    private OllamaChatClient ollamaChatClient;

    @Autowired
    private ExampleChatController exampleChatController;

    @Autowired
    private Neo4jController neo4jController;

    @Test
    public void contextLoads() {

    }

    @Test
    public void test() {
//        exampleChatController.test("1803663875373694977", "你现在是一个笔录分析人员，请用四个字描述一下下述内容属于哪种类型的对话？");
    }

    @Test
    public void savePersion() {
        CaseNode caseNode = new CaseNode();
        caseNode.setName("自然人");
        R<?> save = neo4jController.save(caseNode);
        System.out.printf(save.toString());
    }

    private final OllamaChatClient chatClient;
    @Autowired
    public FuHsiApplicationTests(OllamaChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @Autowired
    private NoteRecordSplitService noteRecordSplitService;
    @Autowired
    private ModelRecordTypeService modelRecordTypeService;


    @Test
    public void classificationTest() {

        String recordId = "1824320931821637633";

        final String NEW_TEMPLATE = """
                分类任务: 将对话笔录文本进行分类。
                目标: 将给定的对话分配到预定义的类别中
                预定义类别为：{typeContext}。
                说明: 提供一段对话笔记录文本，分类器应该识别出对话的主题，并将其归类到上述类别中的一个或多个。
                ---
                示例输入:
                办案警官问：你和上述的这些受害人签订协议之后是否实际履行合同？
                行为人XXX回答：我和他们签订合同就是为了骗他们相信我，我就是伪造的一些假合同，等我把他们的钱骗到手之后我就不会履行合同。
                预期输出: {"result":[{"type":"合同和协议","explain":"行为人XXX提到签订合同"},{"type":"虚假信息和伪造","explain":"行为人XXX提到合同是假合同"}]}
                ---
                任务要求:
                1. 分类器应当准确地识别对话的主题,分类来自于预定义的类别。
                2. 分类器应该实事求是按照对话进行分类,不要有过多的推测。
                2. 如果一段对话笔记录包含多个主题，请选择最相关的类别,最多可选择三个分类。
                3. 如果不涉及任何分类则回复{"result":[]}
                ---
                以下为问答对内容:
                {question}
                {answer}
                ---
                返回格式为json,字段名要严格一致:{"result":[{"type":"分类1","explain":"分类原因"},{"type":"分类2","explain":"分类原因"}]}
                """;

        final String TYPE_CONTEXT_TEMPLATE = "{分类type:{type},区别点(分类释义):{typeExt}}";

        List<ModelRecordType> allTypeList = modelRecordTypeService.lambdaQuery().list();

        String type = "";

        // 根据recordId查询所有的分割后的笔录
        List<NoteRecordSplit> noteRecordSplitList = noteRecordSplitService.lambdaQuery().eq(NoteRecordSplit::getNoteRecordId, recordId).list();
        for (NoteRecordSplit noteRecordSplit : noteRecordSplitList) {
            try {
                StopWatch stopWatch = new StopWatch();
                // 首先拼接分类模板
                List<String> typeContextList = new ArrayList<>();
                for (ModelRecordType modelRecordType : allTypeList) {
                    String format = StrUtil.format(TYPE_CONTEXT_TEMPLATE, Map.of("type", modelRecordType.getRecordType(), "typeExt", modelRecordType.getRecordTypeExt()));
                    typeContextList.add(format);
                }
                // 开始对笔录进行分类
                Map<String, String> paramMap = new HashMap<>();
                paramMap.put("typeContext", CollUtil.join(typeContextList, ";"));
                paramMap.put("question", noteRecordSplit.getQuestion());
                paramMap.put("answer", noteRecordSplit.getAnswer());
                Prompt prompt = new Prompt(new UserMessage(StrUtil.format(NEW_TEMPLATE, paramMap)));
                stopWatch.start();
                log.info("开始分析:");
                ChatResponse call = chatClient.call(prompt);
                stopWatch.stop();
                log.info("耗时:{}", stopWatch.getTotalTimeSeconds());
                String content = call.getResult().getOutput().getContent();
                log.info("问：{}, 答：{}", noteRecordSplit.getQuestion(), noteRecordSplit.getAnswer());
                log.info("分析的结果是:{}", content);
                RecordSplitTypeThread.TypeResultDTO result = JSONUtil.toBean(content, RecordSplitTypeThread.TypeResultDTO.class);
                List<RecordSplitTypeThread.TypeNodeDTO> typeList = result.getResult();
                if (CollUtil.isNotEmpty(typeList)) {
                    // 将type进行拼接,并以分号进行分割
                    type = CollUtil.join(typeList.stream().map(RecordSplitTypeThread.TypeNodeDTO::getType).collect(Collectors.toSet()), ";");
                } else {
                    // 如果没有提取到,就是无
                    type = "无";
                }

            } catch (Exception e) {
                log.error("分类任务执行失败:{}", e.getMessage(), e);
                type = "无";
            }
            log.info("question:{},answer:{},分析的结果是:{}", noteRecordSplit.getQuestion(),noteRecordSplit.getAnswer(),type);
        }


    }
}