Creating a Custom Chatbot on Custom Data Using Flutter and Ollama

Chatbots have become an essential part of modern applications, offering automated customer support, personalized recommendations, and interactive user engagement. While many chatbot solutions rely on predefined APIs, sometimes you need a chatbot tailored to your specific data. In this article, we’ll walk through the process of creating a custom chatbot using Flutter and Ollama, a powerful AI framework.

Why Use Flutter and Ollama?

Flutter provides a seamless cross-platform development experience, allowing you to build a chatbot that works on both Android and iOS with a single codebase. Ollama, on the other hand, allows us to train and fine-tune AI models on our own dataset, ensuring our chatbot provides relevant and accurate responses.

Setting Up the Project

1. Initialize a New Flutter Project

If you haven’t already, create a new Flutter project:

flutter create custom_chatbot
cd custom_chatbot

2. Add Required Dependencies

To integrate a chatbot, you’ll need HTTP for API calls and a state management solution like Riverpod or Provider:

dependencies:
  flutter:
    sdk: flutter
  http: ^0.14.0
  provider: ^6.0.0

Run flutter pub get to install the dependencies.

3. Set Up Ollama

Ollama provides powerful AI models for natural language processing. Follow these steps:

1. Install Ollama: Download here
2. Prepare your dataset (CSV, JSON, or database format) containing the chatbot’s knowledge base.
3. Train the model using Ollama’s CLI:

ollama train --data my_dataset.json --output my_custom_model

4.Deploy the trained model on Ollama’s cloud or local environment.

Building the Chat UI in Flutter

A chatbot UI typically consists of a text input field and a conversation history. Let’s create a simple UI:

1. Create the Chat Screen

import 'package:flutter/material.dart';
import 'package:provider/provider.dart';
import 'chat_provider.dart';

class ChatScreen extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    final chatProvider = Provider.of<ChatProvider>(context);
    TextEditingController controller = TextEditingController();

    return Scaffold(
      appBar: AppBar(title: Text('Custom Chatbot')),
      body: Column(
        children: [
          Expanded(
            child: ListView.builder(
              itemCount: chatProvider.messages.length,
              itemBuilder: (context, index) {
                final message = chatProvider.messages[index];
                return ListTile(
                  title: Text(message.text),
                  subtitle: Text(message.sender),
                );
              },
            ),
          ),
          Padding(
            padding: const EdgeInsets.all(8.0),
            child: Row(
              children: [
                Expanded(
                  child: TextField(
                    controller: controller,
                    decoration: InputDecoration(hintText: 'Ask something...'),
                  ),
                ),
                IconButton(
                  icon: Icon(Icons.send),
                  onPressed: () {
                    chatProvider.sendMessage(controller.text);
                    controller.clear();
                  },
                ),
              ],
            ),
          ),
        ],
      ),
    );
  }
}

2. Manage Chat State with Provider

import 'package:flutter/material.dart';
import 'package:http/http.dart' as http;
import 'dart:convert';

class ChatProvider extends ChangeNotifier {
  List<Message> messages = [];

  void sendMessage(String text) async {
    messages.add(Message(text: text, sender: 'User'));
    notifyListeners();

    final response = await http.post(
      Uri.parse('https://api.ollama.ai/chat'),
      headers: {'Content-Type': 'application/json'},
      body: jsonEncode({'message': text}),
    );

    if (response.statusCode == 200) {
      final responseData = jsonDecode(response.body);
      messages.add(Message(text: responseData['reply'], sender: 'Bot'));
      notifyListeners();
    }
  }
}

class Message {
  final String text;
  final String sender;

  Message({required this.text, required this.sender});
}

Running the Chatbot

Now, run the Flutter app using:

flutter run

Type a message in the chat input field, and the chatbot will respond using the trained Ollama model.

Enhancing the Chatbot

• Improve UI: Use animations for smooth message transitions.
• Add Voice Input: Use the speech_to_text package to enable voice-based interactions.
• Enhance AI Responses: Train the model with more diverse datasets and refine its responses.
• Deploy to Cloud: Host your chatbot model on a cloud service for scalability.

Conclusion

Building a custom chatbot with Flutter and Ollama provides the flexibility to tailor responses to your specific needs. By integrating AI models with a Flutter UI, you can create intelligent, responsive, and highly engaging chatbot experiences.