package com.klp.service.impl;

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.klp.common.config.ImageRecognitionConfig;
import com.klp.domain.bo.ImageRecognitionBo;
import com.klp.domain.vo.AttributeVo;
import com.klp.domain.vo.ImageRecognitionVo;
import com.klp.service.IImageRecognitionService;
import com.klp.utils.ImageProcessingUtils;
import lombok.RequiredArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.http.*;
import org.springframework.stereotype.Service;
import org.springframework.web.client.RestTemplate;

import java.util.*;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

/**
 * 图片识别服务实现类
 *
 * @author klp
 * @date 2025-01-27
 */
@Slf4j
@RequiredArgsConstructor
@Service
public class ImageRecognitionServiceImpl implements IImageRecognitionService {

    private final ImageRecognitionConfig config;
    private final ImageProcessingUtils imageProcessingUtils;

    @Qualifier("salesScriptRestTemplate")
    private final RestTemplate restTemplate;

    private final ObjectMapper objectMapper = new ObjectMapper();
    private final ExecutorService executorService = Executors.newFixedThreadPool(5);

    @Override
    public ImageRecognitionVo recognizeImage(ImageRecognitionBo bo) {
        long startTime = System.currentTimeMillis();
        ImageRecognitionVo result = new ImageRecognitionVo();

        try {
            // 验证图片URL
            if (!imageProcessingUtils.isValidImageUrl(bo.getImageUrl())) {
                throw new RuntimeException("无效的图片URL");
            }

            // 根据识别类型调用不同的识别方法
            switch (bo.getRecognitionType()) {
                case "bom":
                    result = recognizeBom(bo);
                    break;
                case "text":
                    result = recognizeText(bo);
                    break;
                default:
                    result = recognizeGeneral(bo);
                    break;
            }

            result.setStatus("success");
            result.setProcessingTime(System.currentTimeMillis() - startTime);

        } catch (Exception e) {
            log.error("图片识别失败", e);
            result.setStatus("failed");
            result.setErrorMessage(e.getMessage());
            result.setProcessingTime(System.currentTimeMillis() - startTime);
        }

        return result;
    }


    public ImageRecognitionVo recognizeBom(ImageRecognitionBo bo) {
        String prompt = buildBomPrompt(bo);
        String aiResponse = callAiApi(bo.getImageUrl(), prompt, bo.getEnableVoting(), bo.getVotingRounds());

        ImageRecognitionVo result = new ImageRecognitionVo();
        result.setImageUrl(bo.getImageUrl());
        result.setRecognitionType("bom");

        // 直接解析属性数组
        List<AttributeVo> attributes = parseAttributesResponse(aiResponse);
        result.setAttributes(attributes);

        // 构建结构化结果
        Map<String, Object> structuredResult = new HashMap<>();
        structuredResult.put("attributes", attributes);
        structuredResult.put("summary", "材料质保单识别结果");
        structuredResult.put("totalItems", attributes.size());



        return result;
    }

    public ImageRecognitionVo recognizeText(ImageRecognitionBo bo) {
        String prompt = buildTextPrompt(bo);
        String aiResponse = callAiApi(bo.getImageUrl(), prompt, bo.getEnableVoting(), bo.getVotingRounds());

        ImageRecognitionVo result = new ImageRecognitionVo();
        result.setImageUrl(bo.getImageUrl());
        result.setRecognitionType("text");

        return result;
    }


    /**
     * 通用识别方法
     */
    private ImageRecognitionVo recognizeGeneral(ImageRecognitionBo bo) {
        String prompt = buildGeneralPrompt(bo);
        String aiResponse = callAiApi(bo.getImageUrl(), prompt, bo.getEnableVoting(), bo.getVotingRounds());

        ImageRecognitionVo result = new ImageRecognitionVo();
        result.setImageUrl(bo.getImageUrl());
        result.setRecognitionType("general");
        return result;
    }

    /**
     * 调用AI API
     */
    private String callAiApi(String imageUrl, String prompt, Boolean enableVoting, Integer votingRounds) {
        // 转换图片为Data URI
        String dataUri = imageProcessingUtils.imageUrlToDataUri(
            imageUrl, config.getMaxImageDimension(), config.getImageQuality());

        // 构建请求体
        Map<String, Object> requestBody = new HashMap<>();
        requestBody.put("model", config.getModelName());

        List<Map<String, Object>> contents = new ArrayList<>();

        // 添加图片内容
        Map<String, Object> imageContent = new HashMap<>();
        imageContent.put("type", "image_url");
        Map<String, Object> imageUrlObj = new HashMap<>();
        imageUrlObj.put("url", dataUri);
        imageUrlObj.put("detail", "low");
        imageContent.put("image_url", imageUrlObj);
        contents.add(imageContent);

        // 添加文本内容
        Map<String, Object> textContent = new HashMap<>();
        textContent.put("type", "text");
        textContent.put("text", prompt);
        contents.add(textContent);

        Map<String, Object> message = new HashMap<>();
        message.put("role", "user");
        message.put("content", contents);

        requestBody.put("messages", Arrays.asList(message));
        requestBody.put("enable_thinking", true);
        requestBody.put("temperature", config.getTemperature());
        requestBody.put("top_p", 0.7);
        requestBody.put("min_p", 0.05);
        requestBody.put("frequency_penalty", 0.2);
        requestBody.put("max_token", config.getMaxTokens());
        requestBody.put("stream", false);
        requestBody.put("stop", new ArrayList<>());
        Map<String, String> responseFormat = new HashMap<>();
        responseFormat.put("type", "text");
        requestBody.put("response_format", responseFormat);

        // 多轮投票处理
        if (Boolean.TRUE.equals(enableVoting) && votingRounds > 1) {
            return callAiApiWithVoting(requestBody, votingRounds);
        } else {
            return callAiApiSingle(requestBody);
        }
    }

    /**
     * 单次调用AI API
     */
    private String callAiApiSingle(Map<String, Object> requestBody) {
        for (int i = 0; i < config.getMaxRetries(); i++) {
            try {
                HttpHeaders headers = new HttpHeaders();
                headers.setContentType(MediaType.APPLICATION_JSON);
                headers.setBearerAuth(config.getApiKey());

                HttpEntity<Map<String, Object>> entity = new HttpEntity<>(requestBody, headers);
                ResponseEntity<Map> response = restTemplate.postForEntity(
                    config.getApiUrl(), entity, Map.class);

                if (response.getStatusCode() == HttpStatus.OK && response.getBody() != null) {
                    Map<String, Object> body = response.getBody();
                    List<Map<String, Object>> choices = (List<Map<String, Object>>) body.get("choices");
                    if (choices != null && !choices.isEmpty()) {
                        Map<String, Object> choice = choices.get(0);
                        Map<String, Object> message = (Map<String, Object>) choice.get("message");
                        return (String) message.get("content");
                    }
                }
            } catch (Exception e) {
                log.error("AI API调用失败，重试 {}: {}", i + 1, e.getMessage());
                if (i == config.getMaxRetries() - 1) {
                    throw new RuntimeException("AI API调用失败", e);
                }
            }
        }
        throw new RuntimeException("AI API调用失败，已达到最大重试次数");
    }

    /**
     * 多轮投票调用AI API
     */
    private String callAiApiWithVoting(Map<String, Object> requestBody, int rounds) {
        List<CompletableFuture<String>> futures = new ArrayList<>();

        for (int i = 0; i < rounds; i++) {
            CompletableFuture<String> future = CompletableFuture.supplyAsync(() ->
                callAiApiSingle(requestBody), executorService);
            futures.add(future);
        }

        List<String> results = new ArrayList<>();
        for (CompletableFuture<String> future : futures) {
            try {
                results.add(future.get());
            } catch (Exception e) {
                log.error("投票轮次失败: {}", e.getMessage());
            }
        }

        if (results.isEmpty()) {
            throw new RuntimeException("所有投票轮次都失败了");
        }

        // 简单投票：返回第一个成功的结果
        return results.get(0);
    }

    /**
     * 构建BOM识别提示词
     */
    private String buildBomPrompt(ImageRecognitionBo bo) {
        StringBuilder prompt = new StringBuilder();
        prompt.append("这是一张材料质保单的图片。请从表格、文字排布中提取有用的信息，返回其中所有可以被识别为字段名 + 字段值的键值对。\n\n");
        prompt.append("要求如下：\n\n");
        prompt.append("1. 忽略标题、编号、日期等通用信息，不要作为键值对输出；\n");
        prompt.append("2. 仅提取\"字段名 + 字段值\"形式的内容，且字段名可以是如\"钢卷号\"、\"规格\"、\"净重\"、\"材质\"、\"下工序\"、\"生产班组\"、\"生产日期\"等；\n");
        prompt.append("3. 字段不固定，根据图像内容自行判断，但要尽量提取所有；\n");
        prompt.append("4. 返回格式统一为 JSON 数组，格式如下：\n");
        prompt.append("[\n");
        prompt.append("  { \"attrKey\": \"字段名\", \"attrValue\": \"字段值\" },\n");
        prompt.append("  ...\n");
        prompt.append("]\n");
        prompt.append("5. 如有值缺失或为空的字段，仍保留字段，value 留空字符串；\n");
        prompt.append("6. 严格按照图像中文字布局顺序返回；\n");
        prompt.append("7. 只输出 JSON 结果，不需要解释或说明；\n\n");

        if (bo.getProductId() != null) {
            prompt.append("【产品信息】\n");
            prompt.append("产品ID: ").append(bo.getProductId()).append("\n\n");
        }

        if (bo.getCustomPrompt() != null && !bo.getCustomPrompt().isEmpty()) {
            prompt.append("【自定义要求】\n");
            prompt.append(bo.getCustomPrompt()).append("\n\n");
        }

        return prompt.toString();
    }

    /**
     * 构建文字识别提示词
     */
    private String buildTextPrompt(ImageRecognitionBo bo) {
        StringBuilder prompt = new StringBuilder();
        prompt.append("请识别图片中的所有文字内容，包括但不限于：\n\n");
        prompt.append("【识别要求】\n");
        prompt.append("1. 识别图片中的所有可见文字\n");
        prompt.append("2. 保持文字的原始格式和顺序\n");
        prompt.append("3. 识别表格、列表等结构化内容\n");
        prompt.append("4. 识别数字、符号等特殊字符\n");
        prompt.append("5. 保持段落和换行格式\n\n");

        if (bo.getCustomPrompt() != null && !bo.getCustomPrompt().isEmpty()) {
            prompt.append("【自定义要求】\n");
            prompt.append(bo.getCustomPrompt()).append("\n\n");
        }

        prompt.append("【输出格式】\n");
        prompt.append("请直接输出识别到的文字内容，保持原有格式。");

        return prompt.toString();
    }

    /**
     * 构建通用识别提示词
     */
    private String buildGeneralPrompt(ImageRecognitionBo bo) {
        StringBuilder prompt = new StringBuilder();
        prompt.append("请分析这张图片的内容，并提供详细描述：\n\n");
        prompt.append("【分析要求】\n");
        prompt.append("1. 描述图片的主要内容和主题\n");
        prompt.append("2. 识别图片中的文字信息\n");
        prompt.append("3. 分析图片的结构和布局\n");
        prompt.append("4. 提取关键信息和数据\n");
        prompt.append("5. 识别图片中的表格、图表等结构化内容\n\n");

        if (bo.getCustomPrompt() != null && !bo.getCustomPrompt().isEmpty()) {
            prompt.append("【自定义要求】\n");
            prompt.append(bo.getCustomPrompt()).append("\n\n");
        }

        prompt.append("【输出格式】\n");
        prompt.append("请提供详细的分析结果，包括文字内容、结构分析等。");

        return prompt.toString();
    }

    /**
     * 解析属性数组响应
     */
    private List<AttributeVo> parseAttributesResponse(String response) {
        try {
            // 尝试直接解析JSON数组
            List<Map<String, Object>> attrList = objectMapper.readValue(response,
                objectMapper.getTypeFactory().constructCollectionType(List.class, Map.class));

            List<AttributeVo> attributes = new ArrayList<>();
            for (Map<String, Object> attr : attrList) {
                AttributeVo attribute = new AttributeVo();
                attribute.setAttrKey((String) attr.get("attrKey"));
                attribute.setAttrValue((String) attr.get("attrValue"));
                attributes.add(attribute);
            }
            return attributes;

        } catch (JsonProcessingException e) {
            // 如果直接解析失败，尝试提取JSON数组部分
            Pattern jsonArrayPattern = Pattern.compile("\\[[\\s\\S]*\\]");
            Matcher matcher = jsonArrayPattern.matcher(response);
            if (matcher.find()) {
                try {
                    List<Map<String, Object>> attrList = objectMapper.readValue(matcher.group(),
                        objectMapper.getTypeFactory().constructCollectionType(List.class, Map.class));

                    List<AttributeVo> attributes = new ArrayList<>();
                    for (Map<String, Object> attr : attrList) {
                        AttributeVo attribute = new AttributeVo();
                        attribute.setAttrKey((String) attr.get("attrKey"));
                        attribute.setAttrValue((String) attr.get("attrValue"));
                        attributes.add(attribute);
                    }
                    return attributes;

                } catch (JsonProcessingException ex) {
                    log.warn("无法解析属性响应为JSON数组: {}", response);
                    return new ArrayList<>();
                }
            }
            log.warn("无法解析属性响应: {}", response);
            return new ArrayList<>();
        }
    }


}