java和azure实现gpt画图接口编写, 给出示例代码
时间: 2024-04-06 18:29:02 浏览: 25
好的,我可以提供一个Java和Azure实现GPT画图接口的示例代码。在这个示例中,我们将使用Azure的Functions服务来实现接口,并使用Java编写函数代码。
首先,您需要创建一个Azure Functions项目并添加以下依赖项:
```
<dependency>
<groupId>com.microsoft.azure.functions</groupId>
<artifactId>azure-functions-java-library</artifactId>
<version>1.4.0</version>
</dependency>
<dependency>
<groupId>com.microsoft.azure</groupId>
<artifactId>azure-storage</artifactId>
<version>8.6.4</version>
</dependency>
```
接下来,您可以编写一个函数来实现GPT画图接口。以下是一个示例代码:
```
import com.microsoft.azure.functions.annotation.*;
import com.microsoft.azure.functions.*;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.net.URISyntaxException;
import java.security.InvalidKeyException;
import java.util.*;
import java.util.List;
import javax.imageio.ImageIO;
import com.microsoft.azure.storage.*;
import com.microsoft.azure.storage.blob.*;
import ai.djl.Model;
import ai.djl.ModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.Classifications;
import ai.djl.modality.Input;
import ai.djl.modality.Output;
import ai.djl.ndarray.*;
import ai.djl.translate.*;
import ai.djl.training.util.ProgressBar;
public class GPTDraw {
@FunctionName("gpt-draw")
public HttpResponseMessage run(
@HttpTrigger(name = "req", methods = {HttpMethod.GET, HttpMethod.POST}, authLevel = AuthorizationLevel.ANONYMOUS) HttpRequestMessage<Optional<String>> request,
final ExecutionContext context) throws InvalidKeyException, URISyntaxException, StorageException, IOException, ModelException {
// 获取请求参数
String text = request.getBody().orElse("");
String style = request.getHeaders().getOrDefault("X-Style", "default");
// 加载模型
String modelPath = "model/gpt_" + style;
Model model = Model.newInstance("GPT");
model.setBlock(new GPTBlock());
model.load(model.getNDManager(), modelPath);
// 推理
Translator<NDList, NDList> translator = new GPTTranslator();
Predictor<NDList, NDList> predictor = model.newPredictor(translator);
NDList output = predictor.predict(new NDList(NDArray.empty(0)));
output = predictor.predict(new NDList(NDArray.array(text)));
String result = output.singletonOrThrow().toString();
// 生成图片
int width = 600;
int height = 300;
BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
Graphics2D g = image.createGraphics();
g.setColor(Color.WHITE);
g.fillRect(0, 0, width, height);
g.setColor(Color.BLACK);
g.setFont(new Font("Arial", Font.PLAIN, 24));
List<String> lines = splitText(result, 30);
int lineHeight = 30;
int y = (height - lines.size() * lineHeight) / 2;
for (String line : lines) {
g.drawString(line, 10, y);
y += lineHeight;
}
// 上传图片到Azure Blob存储
String connectionString = System.getenv("AzureWebJobsStorage");
CloudStorageAccount storageAccount = CloudStorageAccount.parse(connectionString);
CloudBlobClient blobClient = storageAccount.createCloudBlobClient();
CloudBlobContainer container = blobClient.getContainerReference("images");
container.createIfNotExists(BlobContainerPublicAccessType.CONTAINER, new BlobRequestOptions(), new OperationContext());
CloudBlockBlob blob = container.getBlockBlobReference(UUID.randomUUID().toString() + ".png");
ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
ImageIO.write(image, "png", outputStream);
outputStream.flush();
byte[] data = outputStream.toByteArray();
blob.uploadFromByteArray(data, 0, data.length);
// 构造响应
HttpResponseMessage response = request.createResponseBuilder(HttpStatus.OK)
.header("Content-Type", "text/plain")
.body(blob.getUri().toString())
.build();
return response;
}
private List<String> splitText(String text, int maxLength) {
List<String> lines = new ArrayList<>();
int length = 0;
StringBuilder sb = new StringBuilder();
for (String word : text.split(" ")) {
if (length + word.length() + 1 > maxLength) {
lines.add(sb.toString());
sb = new StringBuilder();
length = 0;
}
sb.append(word).append(" ");
length += word.length() + 1;
}
if (sb.length() > 0) {
lines.add(sb.toString());
}
return lines;
}
}
```
在上面的代码中,我们首先获取请求参数(文本和样式),然后加载GPT模型并进行推理。接下来,我们使用Java的AWT库生成一个图片,并将其上传到Azure Blob存储中。最后,我们构造一个响应并返回它。
希望这可以回答您的问题!