Next Step

Step 7: Adding Multi-Label Classification

Sometimes text could belong to multiple categories. Let's update our classifier to support multi-label classification:

For JavaScript

app.post('/classify-multi', async (req, res) => {
  try {
    const { text } = req.body;
    
    if (!text) {
      return res.status(400).json({ error: 'Text is required' });
    }
    
    const result = await client.nlp.classify({
      text,
      categories: [
        'Positive Feedback',
        'Negative Feedback',
        'Feature Request',
        'Bug Report',
        'Question'
      ],
      advanced: {
        contextAwareness: true,
        sensitivityLevel: 0.7,
        minConfidence: 0.3,
        multiLabel: true
      }
    });
    
    return res.json(result);
  } catch (error) {
    console.error('Classification error:', error);
    return res.status(500).json({ error: 'Classification failed' });
  }
});

For Python

@app.route('/classify-multi', methods=['POST'])
def classify_multi():
    try:
        data = request.get_json()
        text = data.get('text')
        
        if not text:
            return jsonify({'error': 'Text is required'}), 400
        
        result = client.nlp.classify(
            text=text,
            categories=[
                'Positive Feedback',
                'Negative Feedback',
                'Feature Request',
                'Bug Report',
                'Question'
            ],
            advanced={
                'context_awareness': True,
                'sensitivity_level': 0.7,
                'min_confidence': 0.3,
                'multi_label': True
            }
        )
        
        return jsonify(result)
    except Exception as e:
        print(f"Classification error: {e}")
        return jsonify({'error': 'Classification failed'}), 500

Step 8: Batch Processing

For processing multiple texts at once:

For JavaScript

app.post('/classify-batch', async (req, res) => {
  try {
    const { texts } = req.body;
    
    if (!texts || !Array.isArray(texts) || texts.length === 0) {
      return res.status(400).json({ error: 'Texts array is required' });
    }
    
    const results = await client.nlp.classifyBatch({
      texts,
      categories: [
        'Positive Feedback',
        'Negative Feedback',
        'Feature Request',
        'Bug Report',
        'Question'
      ]
    });
    
    return res.json(results);
  } catch (error) {
    console.error('Batch classification error:', error);
    return res.status(500).json({ error: 'Batch classification failed' });
  }
});

For Python

@app.route('/classify-batch', methods=['POST'])
def classify_batch():
    try:
        data = request.get_json()
        texts = data.get('texts')
        
        if not texts or not isinstance(texts, list) or len(texts) == 0:
            return jsonify({'error': 'Texts array is required'}), 400
        
        results = client.nlp.classify_batch(
            texts=texts,
            categories=[
                'Positive Feedback',
                'Negative Feedback',
                'Feature Request',
                'Bug Report',
                'Question'
            ]
        )
        
        return jsonify(results)
    except Exception as e:
        print(f"Batch classification error: {e}")
        return jsonify({'error': 'Batch classification failed'}), 500

Conclusion

Congratulations! You've built a text classification system using Invictus AI. This system can automatically categorize customer feedback, which can help your team prioritize and respond to feedback more efficiently.

Next Steps

Explore Custom Models to train a classifier on your own data
Learn how to implement Sentiment Analysis alongside classification
Check out our API Reference for more advanced options# Text Classification Tutorial

Introduction

This tutorial will guide you through building a text classification system using Invictus AI. By the end, you'll have a working application that can automatically categorize text into predefined categories.

Prerequisites

An Invictus AI account with an API key
Basic understanding of JavaScript or Python
Installed the Invictus AI SDK (see Installation)

Step 1: Set Up Your Project

First, let's create a new project and install the required dependencies.

For JavaScript

mkdir invictus-text-classifier
cd invictus-text-classifier
npm init -y
npm install invictus-ai express

Create a new file called app.js:

const express = require('express');
const InvictusAI = require('invictus-ai');

const app = express();
app.use(express.json());

const client = new InvictusAI.Client('YOUR_API_KEY');

// We'll add our classifier code here

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
  console.log(`Server running on port ${PORT}`);
});

For Python

mkdir invictus-text-classifier
cd invictus-text-classifier
pip install invictus-ai flask

Create a new file called app.py:

from flask import Flask, request, jsonify
from invictus_ai import Client

app = Flask(__name__)
client = Client('YOUR_API_KEY')

# We'll add our classifier code here

if __name__ == '__main__':
    app.run(debug=True, port=3000)

Step 2: Define Your Categories

For this tutorial, we'll build a classifier that categorizes customer feedback into the following categories:

Positive Feedback
Negative Feedback
Feature Request
Bug Report
Question

Step 3: Create the Classification Endpoint

For JavaScript

Add the following code to your app.js file:

// Classification endpoint
app.post('/classify', async (req, res) => {
  try {
    const { text } = req.body;
    
    if (!text) {
      return res.status(400).json({ error: 'Text is required' });
    }
    
    const result = await client.nlp.classify({
      text,
      categories: [
        'Positive Feedback',
        'Negative Feedback',
        'Feature Request',
        'Bug Report',
        'Question'
      ]
    });
    
    return res.json(result);
  } catch (error) {
    console.error('Classification error:', error);
    return res.status(500).json({ error: 'Classification failed' });
  }
});

For Python

Add the following code to your app.py file:

@app.route('/classify', methods=['POST'])
def classify():
    try:
        data = request.get_json()
        text = data.get('text')
        
        if not text:
            return jsonify({'error': 'Text is required'}), 400
        
        result = client.nlp.classify(
            text=text,
            categories=[
                'Positive Feedback',
                'Negative Feedback',
                'Feature Request',
                'Bug Report',
                'Question'
            ]
        )
        
        return jsonify(result)
    except Exception as e:
        print(f"Classification error: {e}")
        return jsonify({'error': 'Classification failed'}), 500

Step 4: Test Your Classifier

Start your server:

For JavaScript

node app.js

For Python

python app.py

Now, let's test our classifier with some sample texts:

# For Positive Feedback
curl -X POST \
  http://localhost:3000/classify \
  -H 'Content-Type: application/json' \
  -d '{"text": "I love your product! It has made my life so much easier."}'

# For Feature Request
curl -X POST \
  http://localhost:3000/classify \
  -H 'Content-Type: application/json' \
  -d '{"text": "It would be great if you could add dark mode to the app."}'

# For Bug Report
curl -X POST \
  http://localhost:3000/classify \
  -H 'Content-Type: application/json' \
  -d '{"text": "The app crashes whenever I try to upload an image."}'

Step 5: Building a Simple Front-End (Optional)

Let's create a simple HTML form to test our classifier.

Create a new file called index.html:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Invictus Text Classifier</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            max-width: 800px;
            margin: 0 auto;
            padding: 20px;
        }
        textarea {
            width: 100%;
            height: 150px;
            margin-bottom: 10px;
        }
        #result {
            margin-top: 20px;
            padding: 10px;
            border: 1px solid #ccc;
            border-radius: 5px;
            background-color: #f9f9f9;
        }
    </style>
</head>
<body>
    <h1>Invictus Text Classifier</h1>
    <p>Enter text below to classify it:</p>
    <textarea id="text" placeholder="Enter text here..."></textarea>
    <button onclick="classifyText()">Classify</button>
    <div id="result"></div>

    <script>
        async function classifyText() {
            const text = document.getElementById('text').value;
            const resultDiv = document.getElementById('result');
            
            if (!text) {
                resultDiv.innerHTML = '<p>Please enter some text.</p>';
                return;
            }
            
            resultDiv.innerHTML = '<p>Classifying...</p>';
            
            try {
                const response = await fetch('/classify', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json'
                    },
                    body: JSON.stringify({ text })
                });
                
                const result = await response.json();
                
                if (response.ok) {
                    resultDiv.innerHTML = `
                        <h3>Classification Result:</h3>
                        <p><strong>Category:</strong> ${result.category}</p>
                        <p><strong>Confidence:</strong> ${(result.confidence * 100).toFixed(2)}%</p>
                    `;
                } else {
                    resultDiv.innerHTML = `<p>Error: ${result.error}</p>`;
                }
            } catch (error) {
                resultDiv.innerHTML = `<p>Error: ${error.message}</p>`;
            }
        }
    </script>
</body>
</html>

For JavaScript

Update your app.js to serve the HTML file:

const path = require('path');

// Add this before your routes
app.use(express.static(path.join(__dirname)));

// Add this after your routes
app.get('/', (req, res) => {
  res.sendFile(path.join(__dirname, 'index.html'));
});

For Python

Update your app.py to serve the HTML file:

@app.route('/')
def index():
    return app.send_static_file('index.html')

# Make sure to create a 'static' folder and put your index.html there

Step 6: Enhancing the Classifier

For more accurate classification, you can use Invictus AI's advanced configuration options:

const result = await client.nlp.classify({
  text,
  categories: [
    'Positive Feedback',
    'Negative Feedback',
    'Feature Request',
    'Bug Report',
    'Question'
  ],
  advanced: {
    contextAwareness: true,
    sensitivityLevel: 0.7,
    minConfidence: 0.4,
    multiLabel: false
  }
});

PreviousAPI Reference Overview NextAuthentication

hashtagStep 7: Adding Multi-Label Classification

hashtagFor JavaScript

hashtagFor Python

hashtagStep 8: Batch Processing

hashtagFor JavaScript

hashtagFor Python

hashtagConclusion

hashtagNext Steps

hashtagIntroduction

hashtagPrerequisites

hashtagStep 1: Set Up Your Project

hashtagFor JavaScript

hashtagFor Python

hashtagStep 2: Define Your Categories

hashtagStep 3: Create the Classification Endpoint

hashtagFor JavaScript

hashtagFor Python

hashtagStep 4: Test Your Classifier

hashtagFor JavaScript

hashtagFor Python

hashtagStep 5: Building a Simple Front-End (Optional)

hashtagFor JavaScript

hashtagFor Python

hashtagStep 6: Enhancing the Classifier

Step 7: Adding Multi-Label Classification

For JavaScript

For Python

Step 8: Batch Processing

For JavaScript

For Python

Conclusion

Next Steps

Introduction

Prerequisites

Step 1: Set Up Your Project

For JavaScript

For Python

Step 2: Define Your Categories

Step 3: Create the Classification Endpoint

For JavaScript

For Python

Step 4: Test Your Classifier

For JavaScript

For Python

Step 5: Building a Simple Front-End (Optional)

For JavaScript

For Python

Step 6: Enhancing the Classifier