There are plenty of ways to integrate an AI model in your code. But sometimes, when you start, it is really hard to pick one. That’s why, I decided to share my experience in going through this path. In this article we will: Get familiar with the Hugging Face platform Explore the basics of Inference API on the Hugging Face Create Spring Boot project Develop a simple Java client, which will help us to establish connection with the Inference API Pick a model Develop a small piece of the REST API And finally, test all that with some REST Client. Quite a lot for a start, isn’t it? 😀 What is Hugging Face The only way to develop such a sophisticated field, as Artificial Intelligence - is to share all the knowledge and resources through building the biggest community possible. That is the key concept, on which this platform is based. And it is also a reason why this platform is so open and friendly to anyone who wants to share, explore and discover the possibilities of the AI-world. It has an enormous amount of models, waiting for you to try them. You can read more about its possibilities in the Hugging Face Hub Documentation. Now, let’s take a closer look at the Inference API. What is Inference API When you need to prototype your application or to experiment with capabilities of the chosen model - you can do it without complicated infrastructure or setup. The Inference API is offering access to most of the models, which are available on the Hugging Face. Essentially, all you need is the url and an api-key. Of course, as it’s free, the Inference API is having some limitations. At the moment of writing this article the number of requests per hour for one signed-up user amounts to 50. But it’s obviously enough for a quick start. You can learn much more about Inference API here. If you need much more requests, it is helpful to get familiar with the Inference Endpoints. But you will need a credit card for that. It is also useful to know about Spring AI, which is being developed at the moment. But for this article it is not needed. Are you ready to start? 😀 Create Spring Boot Project This can be done through SpringInitializr or in IntelliJ, it’s up to your choice. Create a new project Add Spring Web dependency Add Lombok With SpringInitializr Visit SpringInitializr and create the project: With IntelliJ Or open IntelliJ and go to File ➞ New ➞ Project and pick Spring Initializr. Choose your desired settings: And add Spring Web and Lombok. Add more dependencies In the build.gradle file we need to add a few more dependencies for our client. dependencies { implementation 'com.squareup.okhttp3:okhttp:4.12.0' implementation 'com.fasterxml.jackson.core:jackson-databind:2.17.2' } Writing a simple client for Inference API For beginning, we will try to develop the most simple client. I also picked a more easy-to-grasp group of models, which is based on generating text from a prompt. If you want to enhance the client, you can find much more details in the API specification. Create a client Create a new package client and a HuggingfaceModelClient in it. This client will send requests to the Hugging Face platform. We will use a Builder pattern for our client. Let’s add some Lombok annotations to reduce the code and several necessary fields: @Getter @Builder @AllArgsConstructor public class HuggingFaceModelClient { private static final String API_URL = "https://api-inference.huggingface.co/models/"; private final String modelName; private final String accessToken; private final int maxLength; private final Double temperature; private final int maxRetries; private final long retryDelay; @Builder.Default private final OkHttpClient client = new OkHttpClient(); } Let me explain the fields of our client… API_URL is the common part for all model urls at Hugging Face, modelName is the rest of the API_URL for the exact model (e.g. google-t5/t5-small), accessToken is a token, which can be created here , maxLength and temperature are model parameters, maxRetries is a number of attempts to send a request, retryDelay is a time frame between requests, @Builder.Default is a field, excluded from Builder, generated by Lombok, OkHttpClient will help us with sending requests to Hugging Face API. Now, let’s create the most important method of this client - call(): public String call(String inputs) throws IOException { return ""; } Inside this method we will first check if our inputs are not empty: if (inputs.isEmpty()) { throw new IllegalArgumentException("Input string cannot be empty"); } Then, let’s shape the payload: //Create JSON payload using Jackson ObjectMapper objectMapper = new ObjectMapper(); Map<String, Object> payload = new HashMap<>(); payload.put("inputs", inputs); Map<String, Object> parameters = new HashMap<>(); parameters.put("max_length",maxLength); if (temperature != null) { parameters.put("temperature", temperature); } payload.put("parameters", parameters); String requestParams = objectMapper.writeValueAsString(payload); And the complete URL: String url = API_URL + modelName; And finally, everything, what is about sending the request: RequestBody requestBody = RequestBody.create(requestParams, MediaType.parse("application/json")); Request request = new Request.Builder() .url(url) .addHeader("Authorization","Bearer " + accessToken) .post(requestBody) .build(); int retries = 0; while (true) { try (Response response = client.newCall(request).execute()) { if(response.isSuccessful()) { return response.body().string(); } else if (retries< maxRetries) { retries++; try { Thread.sleep(retryDelay); } catch (InterruptedException e) { Thread.currentThread().interrupt(); } } else { throw new IOException("Unexpected response code: " + response.code()); } } } Time to have a short coffee break. ☕ Let’s dive into the AI model’s World! Writing a simple Controller Before we start, let’s clarify a few important moments. Choose a model For the start, go to the Models page. The number of models may seem overwhelming, but you can filter them by tasks. As a developer, you should first pay attention to types of inputs and outputs of the model. I’d like to list several types of tasks I find easier to start with: Text generation Text classification Text-to-image As we are using Inference API, there is no need to bother about size of a model. It is only important if you are going to use a chosen model in production later. In this project we are going to use google-t5/t5-small. It is a text generation model. Let’s take a closer look at the google-t5/t5-small: You can find a model name almost at the top left corner of the page. And you can play with it through the Inference API Widget on the left side of page: Pay attention to the word Cold. That means that the model is not loaded at the moment. But if you start sending requests to the Inference API - it will soon become Warm. When the model is cold, you can get 500 Internal Server Error. Create an Access Token For that you need first to create an account on Hugging Face. Then go to Access Tokens and create a new token. We will need it for authenticating our application to Hugging Face services. For our purposes it is enough to create a read token. It is recommended to have one token per one app. Name it with a word, connected to your app by meaning. You can learn more about the roles of tokens and their creation here. Do not forget to copy a token and save it in a safe place! Write Controller In a controller package create ChatController. Add necessary annotations: @RestController @RequestMapping("/ai") public class ChatController { private final String ACCESS_TOKEN = "<your-access-token-here>"; } Access Token usage While it is totally ok for a hello world app, in a real development you must hide your access token to prevent it from leaking. One of ways to do that is to set an environmental parameter and then pass it as a command line parameter when launching the application. Next, we need to inject HuggingfaceModelClient to our ChatController in a following way: private HuggingFaceModelClient client; @Autowired public ChatController() { this.client = HuggingFaceModelClient.builder() .modelName("google-t5/t5-small") .accessToken(ACCESS_TOKEN ) .maxLength(100) .maxRetries(5) .retryDelay(1000) .build(); } And implement GET method: @GetMapping("/generate") public Map generate(@RequestParam(value = "message", defaultValue = "I love coffee") String message) throws IOException { return Map.of("generation", client.call(message)); } We are almost close to the end of our project. 😀 Tests I think that it’s easier to use a rest client in our case, but it is always useful to have a Junit test. Add a test It’s time to add some simple test to our project: @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT) class AiDemoApplicationTests { @Autowired TestRestTemplate restTemplate; @Test public void shouldReceiveResponseWithOKStatusCode() { ResponseEntity<String> response = restTemplate .getForEntity("/ai/generate?message=I%20love%20coffee",String.class); assertThat(response.getStatusCode()).isEqualTo(HttpStatus.OK); } } Test it with a REST client I used ARC(Advanced Rest Client), but you can use any other. Let’s construct a simple url like this one: http://localhost:8080/ai/generate?message=Everyone%20loves%20coffee Run the application and your favorite rest client. Create a GET request to your app REST API and send it. Then we will get something like that: As you see, we got a response from the model: Jeder liebt Kaffee ! 😀 Congratulations! 🎉 Few words about errors If you get 400 BadRequest error - probably something is wrong with the request, review your code 🧐 If you get 500 InternalServer - check if your model is still cold and send few more requests to wake it up 🙂 Of course, there is a better way to handle 500 InternalServer, if you are ready to spend some time and modify our HuggingFaceModelClient with adding one more parameter in it: x-wait-for-model. You can find more information about it here. Source code All discussed code can be found on GitHub. Conclusion In this article we passed a long path while integrating a REST API on Spring Boot with Hugging Face. We’ve learned about: some basics of the Hugging Face platform, the process of choosing a model for your application, creation of an Access Token. writing a client for interacting with the Inference API on Hugging Face, writing a simple controller and even a test. That was a really big job 👏 There are plenty of ways to integrate an AI model in your code. But sometimes, when you start, it is really hard to pick one. That’s why, I decided to share my experience in going through this path. In this article we will: Get familiar with the Hugging Face platform Explore the basics of Inference API on the Hugging Face Create Spring Boot project Develop a simple Java client, which will help us to establish connection with the Inference API Pick a model Develop a small piece of the REST API And finally, test all that with some REST Client. Get familiar with the Hugging Face platform Explore the basics of Inference API on the Hugging Face Create Spring Boot project Develop a simple Java client, which will help us to establish connection with the Inference API Pick a model Develop a small piece of the REST API And finally, test all that with some REST Client. Quite a lot for a start, isn’t it? 😀 What is Hugging Face What is Hugging Face The only way to develop such a sophisticated field, as Artificial Intelligence - is to share all the knowledge and resources through building the biggest community possible. That is the key concept, on which this platform is based. And it is also a reason why this platform is so open and friendly to anyone who wants to share, explore and discover the possibilities of the AI-world. It has an enormous amount of models, waiting for you to try them. You can read more about its possibilities in the Hugging Face Hub Documentation . Hub Documentation Now, let’s take a closer look at the Inference API. What is Inference API When you need to prototype your application or to experiment with capabilities of the chosen model - you can do it without complicated infrastructure or setup. The Inference API is offering access to most of the models, which are available on the Hugging Face. Essentially, all you need is the url and an api-key. Of course, as it’s free, the Inference API is having some limitations. At the moment of writing this article the number of requests per hour for one signed-up user amounts to 50. But it’s obviously enough for a quick start. You can learn much more about Inference API here . Inference API here Inference API here If you need much more requests, it is helpful to get familiar with the Inference Endpoints . But you will need a credit card for that. It is also useful to know about Spring AI , which is being developed at the moment. But for this article it is not needed. Inference Endpoints Inference Endpoints Spring AI Are you ready to start? 😀 Create Spring Boot Project This can be done through SpringInitializr or in IntelliJ, it’s up to your choice. SpringInitializr Create a new project Add Spring Web dependency Add Lombok Create a new project Add Spring Web dependency Add Lombok With SpringInitializr Visit SpringInitializr and create the project: SpringInitializr With IntelliJ With IntelliJ Or open IntelliJ and go to File ➞ New ➞ Project and pick Spring Initializr . Choose your desired settings: File ➞ New ➞ Project Spring Initializr And add Spring Web and Lombok . Spring Web Lombok Add more dependencies Add more dependencies In the build.gradle file we need to add a few more dependencies for our client. build.gradle dependencies { implementation 'com.squareup.okhttp3:okhttp:4.12.0' implementation 'com.fasterxml.jackson.core:jackson-databind:2.17.2' } dependencies { implementation 'com.squareup.okhttp3:okhttp:4.12.0' implementation 'com.fasterxml.jackson.core:jackson-databind:2.17.2' } Writing a simple client for Inference API Writing a simple client for Inference API For beginning, we will try to develop the most simple client. I also picked a more easy-to-grasp group of models, which is based on generating text from a prompt. If you want to enhance the client, you can find much more details in the API specification . API specification Create a client Create a new package client and a HuggingfaceModelClient in it. This client will send requests to the Hugging Face platform. We will use a Builder pattern for our client. Let’s add some Lombok annotations to reduce the code and several necessary fields: client HuggingfaceModelClient Lombok @Getter @Builder @AllArgsConstructor public class HuggingFaceModelClient { private static final String API_URL = "https://api-inference.huggingface.co/models/"; private final String modelName; private final String accessToken; private final int maxLength; private final Double temperature; private final int maxRetries; private final long retryDelay; @Builder.Default private final OkHttpClient client = new OkHttpClient(); } @Getter @Builder @AllArgsConstructor public class HuggingFaceModelClient { private static final String API_URL = "https://api-inference.huggingface.co/models/"; private final String modelName; private final String accessToken; private final int maxLength; private final Double temperature; private final int maxRetries; private final long retryDelay; @Builder.Default private final OkHttpClient client = new OkHttpClient(); } Let me explain the fields of our client… API_URL is the common part for all model urls at Hugging Face, modelName is the rest of the API_URL for the exact model (e.g. google-t5/t5-small), accessToken is a token, which can be created here , maxLength and temperature are model parameters, maxRetries is a number of attempts to send a request, retryDelay is a time frame between requests, @Builder.Default is a field, excluded from Builder, generated by Lombok, OkHttpClient will help us with sending requests to Hugging Face API. API_URL is the common part for all model urls at Hugging Face, API_URL modelName is the rest of the API_URL for the exact model (e.g. google-t5/t5-small ), modelName google-t5/t5-small accessToken is a token, which can be created here , accessToken here maxLength and temperature are model parameters, maxLength temperature maxRetries is a number of attempts to send a request, maxRetries retryDelay is a time frame between requests, retryDelay @Builder.Default is a field, excluded from Builder, generated by Lombok, @Builder.Default OkHttpClient will help us with sending requests to Hugging Face API. OkHttpClient Now, let’s create the most important method of this client - call() : call() public String call(String inputs) throws IOException { return ""; } public String call(String inputs) throws IOException { return ""; } Inside this method we will first check if our inputs are not empty: if (inputs.isEmpty()) { throw new IllegalArgumentException("Input string cannot be empty"); } if (inputs.isEmpty()) { throw new IllegalArgumentException("Input string cannot be empty"); } Then, let’s shape the payload: //Create JSON payload using Jackson ObjectMapper objectMapper = new ObjectMapper(); Map<String, Object> payload = new HashMap<>(); payload.put("inputs", inputs); Map<String, Object> parameters = new HashMap<>(); parameters.put("max_length",maxLength); if (temperature != null) { parameters.put("temperature", temperature); } payload.put("parameters", parameters); String requestParams = objectMapper.writeValueAsString(payload); //Create JSON payload using Jackson ObjectMapper objectMapper = new ObjectMapper(); Map<String, Object> payload = new HashMap<>(); payload.put("inputs", inputs); Map<String, Object> parameters = new HashMap<>(); parameters.put("max_length",maxLength); if (temperature != null) { parameters.put("temperature", temperature); } payload.put("parameters", parameters); String requestParams = objectMapper.writeValueAsString(payload); And the complete URL: String url = API_URL + modelName; String url = API_URL + modelName; And finally, everything, what is about sending the request: RequestBody requestBody = RequestBody.create(requestParams, MediaType.parse("application/json")); Request request = new Request.Builder() .url(url) .addHeader("Authorization","Bearer " + accessToken) .post(requestBody) .build(); int retries = 0; while (true) { try (Response response = client.newCall(request).execute()) { if(response.isSuccessful()) { return response.body().string(); } else if (retries< maxRetries) { retries++; try { Thread.sleep(retryDelay); } catch (InterruptedException e) { Thread.currentThread().interrupt(); } } else { throw new IOException("Unexpected response code: " + response.code()); } } } RequestBody requestBody = RequestBody.create(requestParams, MediaType.parse("application/json")); Request request = new Request.Builder() .url(url) .addHeader("Authorization","Bearer " + accessToken) .post(requestBody) .build(); int retries = 0; while (true) { try (Response response = client.newCall(request).execute()) { if(response.isSuccessful()) { return response.body().string(); } else if (retries< maxRetries) { retries++; try { Thread.sleep(retryDelay); } catch (InterruptedException e) { Thread.currentThread().interrupt(); } } else { throw new IOException("Unexpected response code: " + response.code()); } } } Time to have a short coffee break. ☕ Let’s dive into the AI model’s World! Writing a simple Controller Writing a simple Controller Before we start, let’s clarify a few important moments. Choose a model Choose a model For the start, go to the Models page . The number of models may seem overwhelming, but you can filter them by tasks. As a developer, you should first pay attention to types of inputs and outputs of the model. I’d like to list several types of tasks I find easier to start with: Models page Text generation Text classification Text-to-image Text generation Text classification Text-to-image As we are using Inference API, there is no need to bother about size of a model. It is only important if you are going to use a chosen model in production later. In this project we are going to use google-t5/t5-small . It is a text generation model. Let’s take a closer look at the google-t5/t5-small : google-t5/t5-small google-t5/t5-small You can find a model name almost at the top left corner of the page. And you can play with it through the Inference API Widget on the left side of page: Pay attention to the word Cold . That means that the model is not loaded at the moment. But if you start sending requests to the Inference API - it will soon become Warm . When the model is cold, you can get 500 Internal Server Error . Cold Warm 500 Internal Server Error Create an Access Token For that you need first to create an account on Hugging Face. Then go to Access Tokens and create a new token. We will need it for authenticating our application to Hugging Face services. Access Tokens For our purposes it is enough to create a read token. It is recommended to have one token per one app. Name it with a word, connected to your app by meaning. You can learn more about the roles of tokens and their creation here . read here Do not forget to copy a token and save it in a safe place! Do not forget Do not forget Write Controller In a controller package create ChatController . Add necessary annotations: controller ChatController @RestController @RequestMapping("/ai") public class ChatController { private final String ACCESS_TOKEN = "<your-access-token-here>"; } @RestController @RequestMapping("/ai") public class ChatController { private final String ACCESS_TOKEN = "<your-access-token-here>"; } Access Token usage While it is totally ok for a hello world app, in a real development you must hide your access token to prevent it from leaking. One of ways to do that is to set an environmental parameter and then pass it as a command line parameter when launching the application. Access Token usage Access Token usage While it is totally ok for a hello world app, in a real development you must hide your access token to prevent it from leaking. One of ways to do that is to set an environmental parameter and then pass it as a command line parameter when launching the application. Next, we need to inject HuggingfaceModelClient to our ChatController in a following way: HuggingfaceModelClient ChatController private HuggingFaceModelClient client; @Autowired public ChatController() { this.client = HuggingFaceModelClient.builder() .modelName("google-t5/t5-small") .accessToken(ACCESS_TOKEN ) .maxLength(100) .maxRetries(5) .retryDelay(1000) .build(); } private HuggingFaceModelClient client; @Autowired public ChatController() { this.client = HuggingFaceModelClient.builder() .modelName("google-t5/t5-small") .accessToken(ACCESS_TOKEN ) .maxLength(100) .maxRetries(5) .retryDelay(1000) .build(); } And implement GET method: @GetMapping("/generate") public Map generate(@RequestParam(value = "message", defaultValue = "I love coffee") String message) throws IOException { return Map.of("generation", client.call(message)); } @GetMapping("/generate") public Map generate(@RequestParam(value = "message", defaultValue = "I love coffee") String message) throws IOException { return Map.of("generation", client.call(message)); } We are almost close to the end of our project. 😀 Tests Tests I think that it’s easier to use a rest client in our case, but it is always useful to have a Junit test. Add a test Add a test It’s time to add some simple test to our project: @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT) class AiDemoApplicationTests { @Autowired TestRestTemplate restTemplate; @Test public void shouldReceiveResponseWithOKStatusCode() { ResponseEntity<String> response = restTemplate .getForEntity("/ai/generate?message=I%20love%20coffee",String.class); assertThat(response.getStatusCode()).isEqualTo(HttpStatus.OK); } } @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT) class AiDemoApplicationTests { @Autowired TestRestTemplate restTemplate; @Test public void shouldReceiveResponseWithOKStatusCode() { ResponseEntity<String> response = restTemplate .getForEntity("/ai/generate?message=I%20love%20coffee",String.class); assertThat(response.getStatusCode()).isEqualTo(HttpStatus.OK); } } Test it with a REST client Test it with a REST client I used ARC(Advanced Rest Client), but you can use any other. Let’s construct a simple url like this one: http://localhost:8080/ai/generate?message=Everyone%20loves%20coffee http://localhost:8080/ai/generate?message=Everyone%20loves%20coffee Run the application and your favorite rest client. Create a GET request to your app REST API and send it. Then we will get something like that: As you see, we got a response from the model: Jeder liebt Kaffee ! 😀 Jeder liebt Kaffee ! Congratulations! 🎉 Few words about errors If you get 400 BadRequest error - probably something is wrong with the request, review your code 🧐 If you get 500 InternalServer - check if your model is still cold and send few more requests to wake it up 🙂 Of course, there is a better way to handle 500 InternalServer, if you are ready to spend some time and modify our HuggingFaceModelClient with adding one more parameter in it: x-wait-for-model. You can find more information about it here. Few words about errors Few words about errors If you get 400 BadRequest error - probably something is wrong with the request, review your code 🧐 400 BadRequest If you get 500 InternalServer - check if your model is still cold and send few more requests to wake it up 🙂 500 InternalServer Of course, there is a better way to handle 500 InternalServer , if you are ready to spend some time and modify our HuggingFaceModelClient with adding one more parameter in it: x-wait-for-model . You can find more information about it here . 500 InternalServer HuggingFaceModelClient x-wait-for-model here Source code Source code All discussed code can be found on GitHub . GitHub Conclusion Conclusion In this article we passed a long path while integrating a REST API on Spring Boot with Hugging Face. We’ve learned about: some basics of the Hugging Face platform, the process of choosing a model for your application, creation of an Access Token. writing a client for interacting with the Inference API on Hugging Face, writing a simple controller and even a test. some basics of the Hugging Face platform, the process of choosing a model for your application, creation of an Access Token. writing a client for interacting with the Inference API on Hugging Face, writing a simple controller and even a test. That was a really big job 👏
