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Emotion AI

This is a Deep Leaning API for classifying emotions from text input and audios.

😞 😨 😄 😮 😍 😠 😐 🤮

This api will be able to serve different kind of models to perform emotions predictions based on the following user input.

texts
audios

Starting the server

To start the server first you need to install all the packages used by running the following command:

pip install -r requirements.txt
# make sure your current directory is "server"

After that you can start the server by running the following commands:

change the directory from server to api:

cd api

run the app.py

python app.py

The server will start at a default PORT of 3001 which you can configure in the api/app.py on the Config class:

class AppConfig:
    PORT = 3001
    DEBUG = False

If everything went well you will be able to make api request to the server.

EmotionAI

Consist of two parallel models that are trained with different model architectures to save different task. The AI api will do the following:

Given a text be able to predict the emotions in the text
Given an audio be able to predict the emotions in the audio

Oral emotions

The 8 oral emotions that we will be predicting are as follows:

neutral
calm
happy
sad
angry
fearful
disgust
surprised

Sending an audio file to the server at http://127.0.0.1:3001/api/classify/audio using the POST method we will be able to get the data that looks as follows as the json response from the server:

{
  "predictions": {
    "emotion": { "class": "sad", "label": 3, "probability": 0.22 },
    "emotion_intensity": { "class": "normal", "label": 0, "probability": 0.85 },
    "gender": { "class": "male", "label": 0, "probability": 1.0 }
  },
  "success": true
}

Classifying emotions in audios

Using cURL

To classify the audio using cURL make sure that you open the command prompt where the audio files are located for example in my case the audios are located in the audios folder so i open the command prompt in the audios folder or else i will provide the absolute path when making a cURL request for example

curl -X POST -F audio=@03-01-01-01-01-01-01.wav http://127.0.0.1:3001/api/classify/audio

If everything went well we will get the following response from the server:

{
  "predictions": {
    "emotion": { "class": "sad", "label": 3, "probability": 0.22 },
    "emotion_intensity": { "class": "normal", "label": 0, "probability": 0.85 },
    "gender": { "class": "male", "label": 0, "probability": 1.0 }
  },
  "success": true
}

Using Postman client

To make this request with postman we do it as follows:

Change the request method to POST at http://127.0.0.1:3001/api/classify/audio
Click on form-data
Select type to be file on the KEY attribute
For the KEY type audio and select the audio you want to predict under value Click send
If everything went well you will get the following response depending on the audio you have selected:

{
  "predictions": {
    "emotion": { "class": "sad", "label": 3, "probability": 0.22 },
    "emotion_intensity": { "class": "normal", "label": 0, "probability": 0.85 },
    "gender": { "class": "male", "label": 0, "probability": 1.0 }
  },
  "success": true
}

Using JavaScript fetch api.

First you need to get the input from html
Create a formData object
make a POST requests

const input = document.getElementById("input").files[0];
let formData = new FormData();
formData.append("audio", input);
fetch("http://127.0.0.1:3001/api/classify/audio", {
  method: "POST",
  body: formData,
})
  .then((res) => res.json())
  .then((data) => console.log(data));

If everything went well you will be able to get expected response.

{
  "predictions": {
    "emotion": { "class": "sad", "label": 3, "probability": 0.22 },
    "emotion_intensity": { "class": "normal", "label": 0, "probability": 0.85 },
    "gender": { "class": "male", "label": 0, "probability": 1.0 }
  },
  "success": true
}

Textual Emotions

There are 6 different emotions that we can detect in a sentence or a text which are:

😞 -> sadness
😨 -> fear
😄 -> joy
😮 -> surprise
😍 -> love
😠 -> anger

Given a sentence to the right endpoint http://127.0.0.1:3001/api/classify/text with expected request body which look as follows:

{
  "text": "some text here"
}

The endpoint will call the textual emotion classifier and be able to detect emotions in the text and yield the response that looks like:

{
  "predictions": {
    "class_label": "sadness",
    "emoji": "😞",
    "label": 1,
    "predictions": [
      {
        "class_label": "joy",
        "emoji": "😄",
        "label": 0,
        "probability": 0.0
      },
      {
        "class_label": "sadness",
        "emoji": "😞",
        "label": 1,
        "probability": 1.0
      },
      {
        "class_label": "anger",
        "emoji": "😠",
        "label": 2,
        "probability": 0.0
      },
      {
        "class_label": "fear",
        "emoji": "😨",
        "label": 3,
        "probability": 0.0
      },
      {
        "class_label": "love",
        "emoji": "😍",
        "label": 4,
        "probability": 0.0
      },
      {
        "class_label": "surprise",
        "emoji": "😮",
        "label": 5,
        "probability": 0.0
      }
    ],
    "probability": 1.0,
    "sentence": "im updating my blog because i feel shitty."
  },
  "success": true
}

Classifying emotions on text using cURL

To classify the emotion in the text using cURL we send the POST request as follows:

curl -X POST http://127.0.0.1:3001/api/classify/text -H "Content-Type: application/json" -d "{\"text\":\"i feel like my irritable sensitive combination skin has finally met it s match.\"}"

If everything went well we will be able to se the json response that looks as follow:

{
  "class_label": "anger",
  "emoji": "\ud83d\ude20",
  "label": 2,
  "predictions": [
    {
      "class_label": "joy",
      "emoji": "\ud83d\ude04",
      "label": 0,
      "probability": 0.0
    },
    {
      "class_label": "sadness",
      "emoji": "\ud83d\ude1e",
      "label": 1,
      "probability": 0.0
    },
    {
      "class_label": "anger",
      "emoji": "\ud83d\ude20",
      "label": 2,
      "probability": 1.0
    },
    {
      "class_label": "fear",
      "emoji": "\ud83d\ude28",
      "label": 3,
      "probability": 0.0
    },
    {
      "class_label": "love",
      "emoji": "\ud83d\ude0d",
      "label": 4,
      "probability": 0.0
    },
    {
      "class_label": "surprise",
      "emoji": "\ud83d\ude2e",
      "label": 5,
      "probability": 0.0
    }
  ],
  "probability": 1.0,
  "sentence": "i feel like my irritable sensitive combination skin has finally met it s match."
}

Classifying emotions on text using POSTMAN client

To classify the emotions on text using postman client we do it as follows:

Send a POST request at http://127.0.0.1:3001/api/classify/text
Under request body we select json
We add the json object that looks as follows:

{
  "text": "i feel like my irritable sensitive combination skin has finally met it s match."
}

Click send and you will be able to see the predictions of the following nature:

{
  "class_label": "anger",
  "emoji": "\ud83d\ude20",
  "label": 2,
  "predictions": [
    {
      "class_label": "joy",
      "emoji": "\ud83d\ude04",
      "label": 0,
      "probability": 0.0
    },
    {
      "class_label": "sadness",
      "emoji": "\ud83d\ude1e",
      "label": 1,
      "probability": 0.0
    },
    {
      "class_label": "anger",
      "emoji": "\ud83d\ude20",
      "label": 2,
      "probability": 1.0
    },
    {
      "class_label": "fear",
      "emoji": "\ud83d\ude28",
      "label": 3,
      "probability": 0.0
    },
    {
      "class_label": "love",
      "emoji": "\ud83d\ude0d",
      "label": 4,
      "probability": 0.0
    },
    {
      "class_label": "surprise",
      "emoji": "\ud83d\ude2e",
      "label": 5,
      "probability": 0.0
    }
  ],
  "probability": 1.0,
  "sentence": "i feel like my irritable sensitive combination skin has finally met it s match."
}

Using the javascript fetch API

To classify emotions on text using the javascript fetch api, one can run the following script:

fetch("http://127.0.0.1:3001/api/classify/text", {
  method: "POST",
  headers: new Headers({ "content-type": "application/json" }),
  body: JSON.stringify({
    text: "i feel like my irritable sensitive combination skin has finally met it s match.",
  }),
})
  .then((res) => res.json())
  .then((data) => console.log(data));

If everything goes well you will be able to get the following response from the server:

{
  "class_label": "anger",
  "emoji": "\ud83d\ude20",
  "label": 2,
  "predictions": [
    {
      "class_label": "joy",
      "emoji": "\ud83d\ude04",
      "label": 0,
      "probability": 0.0
    },
    {
      "class_label": "sadness",
      "emoji": "\ud83d\ude1e",
      "label": 1,
      "probability": 0.0
    },
    {
      "class_label": "anger",
      "emoji": "\ud83d\ude20",
      "label": 2,
      "probability": 1.0
    },
    {
      "class_label": "fear",
      "emoji": "\ud83d\ude28",
      "label": 3,
      "probability": 0.0
    },
    {
      "class_label": "love",
      "emoji": "\ud83d\ude0d",
      "label": 4,
      "probability": 0.0
    },
    {
      "class_label": "surprise",
      "emoji": "\ud83d\ude2e",
      "label": 5,
      "probability": 0.0
    }
  ],
  "probability": 1.0,
  "sentence": "i feel like my irritable sensitive combination skin has finally met it s match."
}

Note that when you are sending the request to the server using the javascript fetch API you don't need to worry about CORS this is a public API.

Notebooks

If you want to see how the models were trained you can open the respective notebooks:

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
audios		audios
js		js
notebooks		notebooks
server		server
.gitignore		.gitignore
1.jpg		1.jpg
2.png		2.png
3.jpg		3.jpg
4.png		4.png
LICENSE		LICENSE
README.md		README.md
requests.rest		requests.rest

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Emotion AI

Starting the server

EmotionAI

Oral emotions

Classifying emotions in audios

Textual Emotions

Notebooks

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CrispenGari/emotionAI

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Emotion AI

Starting the server

EmotionAI

Oral emotions

Classifying emotions in audios

Textual Emotions

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