• Request API Documentation
  • Create a Chat [POST]
    • Request
      • Request Body
      • Example
      • C# Sample
    • Response
      • Response Body
      • Example
  • Get Chat Conversation [GET]
    • Request
      • URL Parameters
      • Example
      • C# Sample
    • Response
      • Response Body
      • Example
  • Send Text Message [POST]
    • Request
      • URL Parameters
      • Request Body
      • Example
      • C# Sample
    • Response
      • Response Body
      • Example
  • Send Audio Message [POST]
    • Request
      • URL Parameters
      • Request Body
      • Example
      • C# Sample
    • Response
      • Response Body
      • Example
  • Reset Chat Conversation [POST]
    • Request
      • URL Parameters
      • Example
      • C# Sample
    • Response
      • Response Body
      • Example
  • code repositories
    • App Architecture
    • Swagger API page
  • Project Deployment Documentation
    • Part 1: Deployment of API
      • Step 1: Pull the Repository
      • Step 2: Install Requirements
      • Step 3: Create Docker Connection
      • Step 4: Run MongoDB Docker Image
      • Step 5: Convert New Model
      • Step 6: Add your OpenAI API key
      • Step 7: Build Docker Image
      • Step 8: Run Docker Image
    • Part 2: Deployment of Application (If you want to deploy it OUTSIDE of huggingface space)
      • Step 1: Pull Hugging Face Space Repository
      • Step 2: Change the public API endpoint
      • Step 3: Run the Application

Request API Documentation

To evaluate model on Bingsu/zeroth-korean, run evaluate.py script, by:

python evaluate.py

myHFworkspace for tiny-model

app.py

Create a Chat [POST]

Create a new chat conversation.

Request

• Endpoint: /create
• Method: POST
• Content-Type: application/json

Request Body

Example

POST /create

{
  "messages": ["Hello", "How are you?"],
  "roles": ["user", "assistant"],
  "audios": ["SGVsbG8=", "SG93IGFyZSB5b3U/"]
}

C# Sample

public void CreateChat()
{
    StartCoroutine(CreateChatCoroutine());
}

private IEnumerator CreateChatCoroutine()
{
    string url = baseURL + "/create";

    Dictionary<string, object> data = new Dictionary<string, object>()
    {
        { "messages", new List<string>() { "Hello", "How are you?" } },
        { "roles", new List<string>() { "user", "assistant" } },
        { "audios", new List<string>() { "SGVsbG8=", "SG93IGFyZSB5b3U/" } }
    };

    string jsonData = JsonUtility.ToJson(data);

    UnityWebRequest request = UnityWebRequest.Post(url, jsonData);
    request.SetRequestHeader("Content-Type", "application/json");

    yield return request.SendWebRequest();

    if (request.result != UnityWebRequest.Result.Success)
    {
        Debug.Log("Create chat request failed: " + request.error);
    }
    else
    {
        string response = request.downloadHandler.text;
        Debug.Log("Create chat request succeeded. Response: " + response);
    }
}

Response

• Status Code: 201 CREATED

Response Body

Example

HTTP/1.1 201 CREATED

{
  "id": "60c98731e3c1aa3eb4b57d9c"
}

Get Chat Conversation [GET]

Retrieve a chat conversation by its ID.

Request

• Endpoint: /<id>
• Method: GET

URL Parameters

Example

GET /60c98731e3c1aa3eb4b57d9c

C# Sample

public void GetChat(string id)
{
    StartCoroutine(GetChatCoroutine(id));
}

private IEnumerator GetChatCoroutine(string id)
{
    string url = baseURL + "/" + id;

    UnityWebRequest request = UnityWebRequest.Get(url);

    yield return request.SendWebRequest();

    if (request.result != UnityWebRequest.Result.Success)
    {
        Debug.Log("Get chat request failed: " + request.error);
    }
    else
    {
        string response = request.downloadHandler.text;
        Debug.Log("Get chat request succeeded. Response: " + response);
    }
}

Response

• Status Code: 200 OK

Response Body

Example

HTTP/1.1 200 OK

[
  {
    "message": "Hello",
    "role": "user",
    "audio": "SGVsbG8="
  },
  {
    "message": "How are you?",
    "role": "assistant",
    "audio": "SG93IGFyZSB5b3U/"
  }
]

Send Text Message [POST]

Send a text message in a chat conversation.

Request

• Endpoint: /send/text/<id>
• Method: POST
• Content-Type: application/json

URL Parameters

Request Body

Example

POST /send/text/60c98731e3c1aa3eb4b57d9c

{
  "message": "What's your name?",
  "role": "user"
}

C# Sample

public void SendTextMessage(string id, string message, string role)
{
    StartCoroutine(SendTextMessageCoroutine(id, message, role));
}

private IEnumerator SendTextMessageCoroutine(string id, string message, string role)
{
    string url = baseURL + "/send/text/" + id;

    Dictionary<string, string> data = new Dictionary<string, string>()
    {
        { "message", message },
        { "role", role }
    };

    string jsonData = JsonUtility.ToJson(data);

    UnityWebRequest request = UnityWebRequest.Post(url, jsonData);
    request.SetRequestHeader("Content-Type", "application/json");

    yield return request.SendWebRequest();

    if (request.result != UnityWebRequest.Result.Success)
    {
        Debug.Log("Send text message request failed: " + request.error);
    }
    else
    {
        string response = request.downloadHandler.text;
        Debug.Log("Send text message request succeeded. Response: " + response);
    }
}

Response

• Status Code: 200 OK

Response Body

Example

HTTP/

1.1 200 OK

{
  "text": "My name is Chatbot.",
  "audio": "SGVsbG8gd29ybGQh"
}

Send Audio Message [POST]

Send an audio message in a chat conversation.

Request

• Endpoint: /send/audio/<id>
• Method: POST
• Content-Type: multipart/form-data

URL Parameters

Request Body

Example

POST /send/audio/60c98731e3c1aa3eb4b57d9c

Content-Type: multipart/form-data

--boundary
Content-Disposition: form-data; name="audio"; filename="audio.wav"
Content-Type: audio/wav

<Binary audio data>
--boundary--

C# Sample

public void SendAudioMessage(string id, byte[] audioData)
{
    StartCoroutine(SendAudioMessageCoroutine(id, audioData));
}

private IEnumerator SendAudioMessageCoroutine(string id, byte[] audioData)
{
    string url = baseURL + "/send/audio/" + id;

    UnityWebRequest request = new UnityWebRequest(url, "POST");
    request.SetRequestHeader("Content-Type", "multipart/form-data");

    UploadHandlerRaw uploadHandler = new UploadHandlerRaw(audioData);
    request.uploadHandler = uploadHandler;

    yield return request.SendWebRequest();

    if (request.result != UnityWebRequest.Result.Success)
    {
        Debug.Log("Send audio message request failed: " + request.error);
    }
    else
    {
        string response = request.downloadHandler.text;
        Debug.Log("Send audio message request succeeded. Response: " + response);
    }
}

Response

• Status Code: 200 OK

Response Body

Example

HTTP/1.1 200 OK

{
  "text": "I received your audio message.",
  "audio": "SGVsbG8gd29ybGQh"
}

Reset Chat Conversation [POST]

Reset a chat conversation, removing all messages and audio.

Request

• Endpoint: /reset/<id>
• Method: POST

URL Parameters

Example

POST /reset/60c98731e3c1aa3eb4b57d9c

C# Sample

public void ResetChat(string id)
{
    StartCoroutine(ResetChatCoroutine(id));
}

private IEnumerator ResetChatCoroutine(string id)
{
    string url = baseURL + "/reset/" + id;

    UnityWebRequest request = UnityWebRequest.Post(url, "");

    yield return request.SendWebRequest();

    if (request.result != UnityWebRequest.Result.Success)
    {
        Debug.Log("Reset chat request failed: " + request.error);
    }
    else
    {
        string response = request.downloadHandler.text;
        Debug.Log("Reset chat request succeeded. Response: " + response);
    }
}

Response

• Status Code: 200 OK

Response Body

Example

HTTP/1.1 200 OK

{
  "messages": [],
  "roles": [],
  "audio": []
}

code repositories

koreanVoiceQ&A

App Architecture

Swagger API page

eng2-swagger

Project Deployment Documentation

This document provides step-by-step instructions for deploying the API and application for the project. The deployment process consists of two parts: deploying the API and deploying the application.

Part 1: Deployment of API

Step 1: Pull the Repository

To begin with, you need to pull the repository from GitHub. Run the following command in your terminal:

git clone https://github.com/rifkybujana/Korean-Voice-QnA

Step 2: Install Requirements

Navigate to the cloned repository and install the required dependencies. Use the following command:

cd Korean-Voice-QnA
pip install -r requirements-min.txt

Step 3: Create Docker Connection

Next, create a Docker connection that will be used for running the MongoDB Docker image. Make sure you have Docker installed and running on your system.

docker network create mongo-network

Step 4: Run MongoDB Docker Image

With the Docker connection established, you can now run the MongoDB Docker image. Execute the following command:

docker run -d --network mongo-network --name mongodb -p 27017:27017 -d mongo

Step 5: Convert New Model

To convert a new model, you need to use the “ct2-transformers-converter” tool. Run the following command:

ct2-transformers-converter --model openai/whisper-large-v2 --output_dir model --copy_files tokenizer.json --quantization float16

Step 6: Add your OpenAI API key

Copy .env.example to .env by running:

cp .env.example .env

Edit the OPENAI_API_KEY environment variables inside the new file .env to your open ai API key.

NOTE: You can use vim or any other terminal text editor for this

Step 7: Build Docker Image

Build the Docker image for the API using the provided Dockerfile. Execute the following command:

docker build -t api-image .

Step 8: Run Docker Image

Finally, run the Docker image with the same connection as the MongoDB container. Use the following command:

docker run -it --rm --gpus all --name api-container -p 5000:5000 --network mongo-network api-image

The API is now deployed and accessible at http://localhost:5000 and http://:5000.

Part 2: Deployment of Application (If you want to deploy it OUTSIDE of huggingface space)

Step 1: Pull Hugging Face Space Repository

To deploy the application, you must pull the Hugging Face Space repository. Run the following command:

git clone https://huggingface.co/spaces/aiegoo/whisper-chatbot-ko

Step 2: Change the public API endpoint

Open app.py, and change the constant PUBLIC_API_ENDPOINT to your API URL address (make sure it’s already running).

Step 3: Run the Application

Navigate to the cloned repository and run the app.py file. Execute the following command:

cd whisper-chatbot-ko
python app.py

The application is now running and accessible.

NOTE: if you run it on a Linux server, run ufw allow 7860 to allow the port to be accessible NOTE: you need to enable SSL for the app to access the user’s microphone NOTE: to make the app run in the server’s background, use screen by running screen on your terminal, then just run the app.py

The following wiki, pages and posts are tagged with ai

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