Here is a simple, clear, and complete guide to using AI in a Flutter application, including text AI (ChatGPT/Gemini), image generation, speech, and on-device AI.

---

🚀 How to Use AI in a Flutter Application

Below are the 4 most common ways developers use AI in Flutter:

---

✅ 1. Using AI via Cloud APIs (OpenAI, Gemini, Claude, etc.)

🔥 Best for:

• Chatbots
• Text generation
• Summaries
• AI assistants
• Image generation (DALL·E, Gemini, etc.)

---

Example: Using OpenAI (ChatGPT) API in Flutter

Step 1: Add Dependencies

dependencies:
  http: ^1.2.0
  flutter_dotenv: ^5.1.0

---

Step 2: Create .env file

OPENAI_API_KEY=your_openai_key_here

---

Step 3: Create API Service

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

class OpenAIService {
  final String apiKey = dotenv.env['OPENAI_API_KEY']!;

  Future<String> sendMessage(String message) async {
    final url = Uri.parse("https://api.openai.com/v1/chat/completions");

    final response = await http.post(
      url,
      headers: {
        "Content-Type": "application/json",
        "Authorization": "Bearer $apiKey",
      },
      body: jsonEncode({
        "model": "gpt-4o-mini",
        "messages": [
          {"role": "user", "content": message}
        ]
      }),
    );

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

---

Step 4: Use in UI

OpenAIService ai = OpenAIService();

String response = await ai.sendMessage("Hello, what is Flutter?");
print(response);

---

✅ 2. Using Google’s Gemini API (Free Option Available)

Step 1: Add Dependency

dependencies:
  google_generative_ai: ^0.4.3

Step 2: Use Gemini in Flutter

import 'package:google_generative_ai/google_generative_ai.dart';

final model = GenerativeModel(
  model: "gemini-pro",
  apiKey: "YOUR_API_KEY",
);

Future<void> runAI() async {
  final response = await model.generateContent(
    [Content.text("Explain Flutter in simple words")]
  );

  print(response.text);
}

---

✅ 3. Using On-Device AI (No Internet Required)

You can use on-device AI with:

✔ TensorFlow Lite (TFLite)

✔ Firebase ML Kit

✔ PyTorch Mobile

---

Example: TFLite Image Classification

Step 1: Add Dependencies

dependencies:
  tflite_flutter: ^1.0.1
  image: ^4.1.3

Step 2: Add Model to assets

assets:
  - assets/model.tflite

Step 3: Load and Run Model

import 'package:tflite_flutter/tflite_flutter.dart';

void runModel() async {
  final interpreter = await Interpreter.fromAsset('model.tflite');

  var input = [1.0, 2.0, 3.0];  
  var output = List.filled(1 * 3, 0).reshape([1, 3]);

  interpreter.run(input, output);
  print(output);
}

---

✅ 4. Using AI Features in Firebase ML Kit

ML Kit supports:

• Face detection
• Text recognition
• Barcode scanning
• Voice transcription
• On-device translation

---

Example: Text Recognition

final textRecognizer = TextRecognizer();
final inputImage = InputImage.fromFilePath("assets/image.jpg");

final RecognizedText text = await textRecognizer.processImage(inputImage);

print(text.text);

---

🎯 What Type of AI Do You Want to Add?

Choose your goal:

🧠 Chatbot → Use OpenAI / Gemini API

🔍 Image Recognition → Use TFLite / ML Kit

🖼 Image Generation → OpenAI DALL·E / Gemini Vision

🎤 Voice AI → Speech-to-text + AI API

📍 On-device offline AI → TensorFlow Lite