How to use AI in Flutter Application

Using AI in a Flutter application involves integrating machine learning (ML) models or AI services to perform tasks like image recognition, text generation, speech-to-text, recommendation systems, or chatbots.

Here’s a step-by-step guide on how to use AI in a Flutter app:

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1. Decide the Use Case

Some common AI features in Flutter apps:

• Chatbots (e.g., OpenAI ChatGPT, Dialogflow)
• Image recognition (e.g., TensorFlow Lite)
• Text classification / sentiment analysis
• Speech recognition / text-to-speech
• Recommendation systems

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🔹 2. Choose the AI Approach

You have two main options:

1. Cloud-based AI services (e.g., OpenAI, Google ML Kit, Firebase ML, Dialogflow)
2. On-device ML models (e.g., TensorFlow Lite, tflite_flutter)

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3. Add Dependencies

A. Cloud-based AI Example

Using OpenAI GPT model:

dependencies:
  http: ^0.13.6
  flutter_dotenv: ^5.0.2

Store your API key securely using .env file.

Sample API Call to OpenAI:

import 'package:http/http.dart' as http;
import 'dart:convert';
import 'package:flutter_dotenv/flutter_dotenv.dart';

Future<String> getAIResponse(String prompt) async {
  final response = await http.post(
    Uri.parse("https://api.openai.com/v1/chat/completions"),
    headers: {
      "Content-Type": "application/json",
      "Authorization": "Bearer ${dotenv.env['OPENAI_API_KEY']}",
    },
    body: jsonEncode({
      "model": "gpt-3.5-turbo",
      "messages": [{"role": "user", "content": prompt}],
    }),
  );

  final decoded = jsonDecode(response.body);
  return decoded['choices'][0]['message']['content'];
}

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B. On-device ML Example

Using TensorFlow Lite:

dependencies:
  tflite_flutter: ^0.10.2
  tflite_flutter_helper: ^0.4.0

Steps:

1. Convert your ML model to .tflite format.
2. Place it in assets/.
3. Load and use it in your Dart code.

import 'package:tflite_flutter/tflite_flutter.dart';

Future<void> runModel() async {
  final interpreter = await Interpreter.fromAsset('model.tflite');
  var input = [1.0, 2.0]; // Example input
  var output = List.filled(1, 0).reshape([1]);

  interpreter.run(input, output);
  print('Output: $output');
}

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4. Popular AI Libraries / APIs for Flutter

Use Case	Tool / Package
Chatbots	OpenAI, Dialogflow, Rasa
Text/Speech	speech_to_text, flutter_tts, google_speech
Image recognition	tflite_flutter, google_mlkit_image_labeling
Face detection	google_mlkit_face_detection
Language translation	google_mlkit_translation
Sentiment analysis	Use on-device or API model

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5. Security Tips

• Use environment variables for API keys (e.g., flutter_dotenv)
• Avoid hardcoding keys in the app
• Use backend servers as proxies for secure API interaction if needed

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6. Real-World Example Projects

• AI Chatbot App with OpenAI + Flutter
• Image Classifier using TensorFlow Lite
• Voice Assistant using speech-to-text + AI model
• Document Scanner with text recognition

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Final Notes

• For quick AI integration, cloud APIs are easier.
• For offline capability, use tflite_flutter.
• Always optimize models for mobile using quantization or model compression.