Introduction

This tutorial walks through building a minimal AI chat interface in Flutter for mobile development. You’ll create a responsive UI, wire it to an AI model endpoint, and manage message state. The goal is a practical, production-minded blueprint you can extend — not a fully featured product. Prerequisites: Flutter SDK installed, familiarity with Dart, and an AI REST endpoint (OpenAI-compatible or custom).

Project Setup

Create a new Flutter app and add two lightweight packages: http for network calls and flutter_dotenv for environment variables (API keys). In pubspec.yaml add:

• http: ^0.13.0

• flutter_dotenv: ^5.0.0

Run flutter pub get and store your API key in a .env file (do not commit). Use dotenv.load() in main() before runApp(). Keep networking code isolated in a service class so you can swap providers or add auth headers later — a recommended practice in mobile development.

Building The UI

The UI for a chat app has three parts: an AppBar, a scrollable message list, and a composer (text field + send button). Use ListView.builder for the message feed and reverse: true so the latest messages appear at the bottom. For messages, create a minimal model describing author, text, and timestamp. Use simple Row + Container widgets to differentiate user vs. AI bubbles.

Key considerations:

• Make the composer persist above the keyboard using SafeArea and Scaffold.resizeToAvoidBottomInset.

• Use Expanded for the ListView to fill remaining vertical space.

• For accessibility, ensure semantics labels on buttons and proper contrast in bubbles.

Example message model:

class ChatMessage {
  final String id;
  final String text;
  final bool fromUser;
  ChatMessage({required this.id, required this.text, required this.fromUser});
}

Integrating The AI Model

Treat the AI as a backend service. Create a ChatService with a sendMessage(text) method that posts the user prompt and returns the model response. Keep the HTTP payload small — only send what's necessary (prompt, model name, max tokens). Handle errors: network timeouts, 429 rate limits, and non-2xx responses.

Example send flow (simplified):

Future<String> sendToModel(String prompt) async {
  final res = await http.post(Uri.parse(apiUrl),
    headers: {'Authorization': 'Bearer $apiKey', 'Content-Type': 'application/json'},
    body: jsonEncode({'prompt': prompt, 'max_tokens': 150}),
  );
  if (res.statusCode != 200) throw Exception('Model error');
  final body = jsonDecode(res.body);
  return body['choices']?[0]?['text'] ?? '';
}

Notes:

• Replace apiUrl and response parsing to match your provider.

• Consider streaming responses for long outputs — implement SSE or chunked decoding if supported.

• Never embed API keys in the client for production. Use a proxy service that hides credentials and enforces rate limits.

Managing Messages And State

Use a simple ChangeNotifier to keep the message list and loading state. The pattern is: add a user message immediately, set a loading/typing indicator, await model response, append AI message, notify listeners. Debounce rapid sends and disable the send button while awaiting a response to prevent parallel calls.

Performance tips for mobile development:

• Use const constructors where possible.

• Reuse widgets and avoid rebuilding the full ListView on every state change by using keys or selective rebuilding.

• Persist recent conversation locally (sqflite or hive) so users can resume sessions offline.

Error handling and UX:

• Show inline error messages for failed sends and allow retry.

• Provide a clear affordance to clear conversation or export chat history.

Security and compliance:

• Route requests through your server to apply content filtering, audit logs, or user quotas.

• Mask or redact PII before sending prompts.

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Conclusion

You now have a concise blueprint for a Flutter AI chat interface suitable for mobile development. The main components: a responsive UI with a message list and composer, a networking layer that communicates with an AI model, and a small state manager to sequence messages and indicate loading. From here, you can add streaming, attachments, authentication, caching, and analytics. Keep networking thin in the client, secure keys on a backend, and prioritize responsiveness for a smooth mobile experience.