refactor(gpt): 重构 GPT 相关代码并优化功能
- 新增 AbsGPTManager 抽象类，定义 GPT 管理器的通用接口 - 重命名 BaiduGPTManager 类，使其位于 com.yutou.qqbot.gpt 包中 - 更新相关引用和依赖 - 优化部分代码结构，提高可维护性
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@@ -3,10 +3,9 @@ package com.yutou.qqbot.utlis;
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import com.yutou.napcat.QQDatabase;
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import com.yutou.napcat.model.GroupBean;
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import com.yutou.qqbot.Annotations.UseModel;
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import com.yutou.qqbot.QQBotManager;
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import com.yutou.qqbot.QQNumberManager;
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import com.yutou.qqbot.gpt.BaiduGPTManager;
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import com.yutou.qqbot.models.Model;
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import lombok.val;
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import org.springframework.boot.ApplicationArguments;
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import org.springframework.boot.ApplicationRunner;
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import org.springframework.stereotype.Component;
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@@ -1,219 +0,0 @@
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package com.yutou.qqbot.utlis;
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import com.baidubce.qianfan.Qianfan;
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import com.baidubce.qianfan.model.chat.ChatResponse;
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import com.baidubce.qianfan.model.image.Image2TextResponse;
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import com.baidubce.qianfan.model.image.Text2ImageResponse;
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import com.yutou.qqbot.data.baidu.Message;
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import lombok.val;
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import java.io.ByteArrayInputStream;
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import java.io.File;
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import java.nio.file.Files;
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import java.nio.file.StandardCopyOption;
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import java.util.ArrayList;
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import java.util.Base64;
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import java.util.Collections;
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import java.util.List;
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import java.util.concurrent.ConcurrentHashMap;
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import java.util.concurrent.atomic.AtomicBoolean;
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import java.util.concurrent.atomic.AtomicInteger;
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public class BaiduGPTManager {
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private static final AtomicInteger MAX_MESSAGE = new AtomicInteger(20);
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private static final String AppID = ConfigTools.load(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_APPID, String.class);
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private static final String ApiKey = ConfigTools.load(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_API_KEY, String.class);
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//ConfigTools.load操作可以确保获取到相关参数，所以无需关心
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private static final String AccessKey = ConfigTools.load(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_ACCESS_KEY, String.class);
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private static final String SecretKey = ConfigTools.load(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_SECRET_KEY, String.class);
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private final ConcurrentHashMap<String, List<Message>> msgMap;
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private final static String modelFor40 = "ERNIE-4.0-8K";
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private final static String modelFor35 = "ERNIE-3.5-8K";
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private String model = modelFor35;
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// 新增锁映射表
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private final ConcurrentHashMap<String, AtomicBoolean> userLocks = new ConcurrentHashMap<>();
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private final Qianfan qianfan;
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private BaiduGPTManager() {
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msgMap = new ConcurrentHashMap<>();
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qianfan = new Qianfan(AccessKey, SecretKey);
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String savedVersion = ConfigTools.load(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_VERSION, String.class);
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if (StringUtils.isEmpty(savedVersion) || (!"3.5".equals(savedVersion) && !"4.0".equals(savedVersion))) {
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savedVersion = "3.5";
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ConfigTools.save(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_VERSION, savedVersion);
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}
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model = "3.5".equals(savedVersion) ? modelFor35 : modelFor40;
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}
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private static volatile BaiduGPTManager manager;
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public static BaiduGPTManager getManager() {
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if (manager == null) {
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synchronized (BaiduGPTManager.class) {
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if (manager == null) {
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manager = new BaiduGPTManager();
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}
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}
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}
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return manager;
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}
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public int setMaxMessageCount(int count) {
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MAX_MESSAGE.set(count);
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return count;
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}
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public synchronized void setModelFor40() {
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model = modelFor40;
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ConfigTools.save(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_VERSION, "4.0");
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}
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public synchronized void setModelFor35() {
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model = modelFor35;
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ConfigTools.save(ConfigTools.CONFIG, ConfigTools.BAIDU_GPT_VERSION, "3.5");
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}
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/**
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* 这里确实是需要清空所有数据
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*/
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public synchronized void clear() { // 添加同步
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msgMap.clear();
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for (AtomicBoolean value : userLocks.values()) {
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value.set(false);
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}
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userLocks.forEachValue(1, atomicBoolean -> atomicBoolean.set(false));
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userLocks.clear();
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}
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// 这个是官方的示例代码，表示连续对话
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private static void exampleChat() {
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Qianfan qianfan = new Qianfan();
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ChatResponse response = qianfan.chatCompletion()
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// 设置需要使用的模型，与endpoint同时只能设置一种
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.model("ERNIE-Bot")
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// 通过传入历史对话记录来实现多轮对话
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.addMessage("user", "你好！你叫什么名字？")
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.addMessage("assistant", "你好！我是文心一言，英文名是ERNIE Bot。")
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// 传入本轮对话的用户输入
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.addMessage("user", "刚刚我的问题是什么？")
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.execute();
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System.out.println("输出内容：" + response.getResult());
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}
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public Message sendMessage(String user, String message) {
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// 获取或创建用户锁
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AtomicBoolean lock = userLocks.computeIfAbsent(user, k -> new AtomicBoolean(false));
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// 尝试加锁（如果已被锁定则立即返回提示）
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if (!lock.compareAndSet(false, true)) {
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return Message.create("您有请求正在处理中，请稍后再试", true);
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}
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try {
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List<Message> list = msgMap.computeIfAbsent(user, k -> Collections.synchronizedList(new ArrayList<>()));
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// 限制历史消息的最大数量
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synchronized (list) {
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if (list.size() >= MAX_MESSAGE.get()) {
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int removeCount = list.size() - MAX_MESSAGE.get() + 1; // 腾出空间给新消息
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list.subList(0, removeCount).clear();
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}
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list.add(Message.create(message));
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}
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val builder = qianfan.chatCompletion()
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.model(model);
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for (Message msg : list) {
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builder.addMessage(msg.getRole(), msg.getContent());
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}
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ChatResponse chatResponse = builder.execute();
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Message response = Message.create(chatResponse.getResult(), true);
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synchronized (list) {
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list.add(response);
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if (list.size() > MAX_MESSAGE.get()) {
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int overflow = list.size() - MAX_MESSAGE.get();
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list.subList(0, overflow).clear();
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}
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}
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// msgMap.put(user, list);
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return response;
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} catch (Exception e) {
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Log.e(e, message);
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return Message.create("请求失败,请重试", true);
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} finally {
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lock.set(false);
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userLocks.remove(user, lock);
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}
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}
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/**
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* 将文本转换为图像文件
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* 该方法使用预训练的AI模型将给定的文本转换为图像，并将其保存为文件
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*
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* @param user 用户标识符，用于为生成的图像文件命名
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* @param text 要转换为图像的文本
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* @return 返回生成的图像文件对象，如果转换过程中发生错误，则返回null
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*/
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public File textToImage(String user, String text) {
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// 使用QianFan的text2Image方法将文本转换为图像数据
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Text2ImageResponse response = qianfan.text2Image()
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.prompt(text)
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.execute();
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// 获取转换后的图像数据，以Base64编码的图像字符串形式
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val b64Image = response.getData().get(0).getB64Image();
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// 将Base64编码的图像数据转换为图像文件
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// 创建一个临时目录下的图像文件，文件名包含用户标识符和当前时间戳，以确保唯一性
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val imageFile = new File("tmp" + File.separator + user + "_" + System.currentTimeMillis() + ".png");
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try (val inputStream = new ByteArrayInputStream(Base64.getDecoder().decode(b64Image))) {
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// 将解码后的图像数据复制到图像文件中，替换现有文件
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Files.copy(inputStream, imageFile.toPath(), StandardCopyOption.REPLACE_EXISTING);
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return imageFile;
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} catch (Exception e) {
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// 如果在图像文件生成过程中发生错误，记录错误信息
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Log.e(e);
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}
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// 如果发生错误，返回null
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return null;
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}
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/**
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* 将图片转换为文本描述
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*
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* @param user 使用该功能的用户标识
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* @param file 要转换的图片文件
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* @return 转换后的文本描述，如果转换失败则返回null
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*/
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public String imageToText(String user, File file) {
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// 将file文件转换成base64的代码
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try {
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// 读取文件内容并转换为Base64编码
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val base64 = Base64.getEncoder().encodeToString(Files.readAllBytes(file.toPath()));
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// 调用图像转文本的API
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Image2TextResponse response = qianfan.image2Text()
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.image(base64)
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.prompt("请描述这张图片中的主要内容和细节，以及它们之间的关系\n")
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.execute();
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String translationPrompt = "将以下英文内容严格翻译为简体中文，不要解释、不要添加额外内容，保留专业术语和名称（如Star Wars保持英文）：\n" + response.getResult();
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// 获取API返回的结果
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return sendMessage("bot",translationPrompt).getContent();
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} catch (Exception e) {
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// 异常处理：记录错误日志
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Log.e(e);
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}
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// 如果发生异常，返回null
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return null;
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}
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public String getGPTVersion() {
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return (model.equals(modelFor35) ? "3.5" : "4.0");
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}
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public static void main(String[] args) throws Exception {
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// BaiduGPTManager.getManager().textToImage("user","画一个猫娘，用二次元动画画风，她是粉色头发，坐在地上");
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// BaiduGPTManager.getManager().imageToText("user",new File("test.png"));
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// Message message = BaiduGPTManager.getManager().sendMessage("user", "现在假设小猪等于1,小猴等于2");
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// System.out.println(message.getContent());
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// message = BaiduGPTManager.getManager().sendMessage("user", "那么小猪加上小猴等于多少？");
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// System.out.println(message.getContent());
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System.out.println(BaiduGPTManager.getManager().sendMessage("user", "分析这个网页链接的页面内容,而非链接本身:https://www.bilibili.com/video/BV1TTf5YrESz/").getContent());
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}
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}
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