pocket_brain 1.0.1

pocket_brain: ^1.0.1 copied to clipboard

example/lib/main.dart

import 'dart:io';
import 'package:flutter/material.dart';
import 'package:file_picker/file_picker.dart';
import 'package:dash_chat_2/dash_chat_2.dart';
// Import the PocketBrain library
import 'package:pocket_brain/pocket_brain.dart';

void main() {
  runApp(const MaterialApp(
    home: PocketBrainExample(),
    debugShowCheckedModeBanner: false,
  ));
}

class PocketBrainExample extends StatefulWidget {
  const PocketBrainExample({super.key});

  @override
  State<PocketBrainExample> createState() => _PocketBrainExampleState();
}

class _PocketBrainExampleState extends State<PocketBrainExample> {
  // Instance of the PocketBrain library
  final PocketBrain _brain = PocketBrain();

  // Chat UI Identities
  final ChatUser _user = ChatUser(id: '1', firstName: 'Me');
  final ChatUser _bot = ChatUser(
      id: '2',
      firstName: 'PocketBrain',
      profileImage: "https://robohash.org/brain");

  List<ChatMessage> messages = [];
  bool _isReady = false;

  @override
  void initState() {
    super.initState();
    // 1. Initialize the Brain (Loads AI Model & Database into memory)
    _initBrain();
  }

  Future<void> _initBrain() async {
    // This method prepares the TFLite model and ObjectBox store
    await _brain.init();

    setState(() {
      _isReady = true;
      // Add a welcome message once initialized
      messages.add(ChatMessage(
        user: _bot,
        createdAt: DateTime.now(),
        text:
            "Hello! I am ready to answer questions based on your knowledge base.",
      ));
    });
  }

  // 2. Knowledge Acquisition Feature (Import CSV)
  // or you can directly add your CSV file into your project assets and load it from there.
  Future<void> _pickCsv() async {
    // Pick a CSV file from the device storage
    FilePickerResult? result = await FilePicker.platform.pickFiles(
      type: FileType.custom,
      allowedExtensions: ['csv'],
    );

    if (result != null) {
      File file = File(result.files.single.path!);

      // Import the CSV.
      // NOTE: This function automatically wipes old data and learns the new facts.
      int count = await _brain.importFromCsv(file);

      if (!mounted) return;
      ScaffoldMessenger.of(context).showSnackBar(
        SnackBar(content: Text("Successfully learned $count new facts!")),
      );
    }
  }

  // 3. Q&A Feature (Semantic Search Inference)
  void _onSend(ChatMessage msg) {
    // Immediately show user's message in the UI
    setState(() => messages.insert(0, msg));

    // IMPORTANT
    // Core Logic: Ask the Brain
    // This performs a vector similarity search in the offline database
    String? answer = _brain.ask(msg.text);

    // Prepare the bot's response
    final botReply = ChatMessage(
      user: _bot,
      createdAt: DateTime.now(),
      text: answer ?? "Sorry, I couldn't find an answer in your data.",
    );

    // Simulate a small "thinking" delay for better UX
    Future.delayed(const Duration(milliseconds: 300), () {
      setState(() => messages.insert(0, botReply));
    });
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: const Text("Pocket Brain Demo"),
        actions: [
          IconButton(
            icon: const Icon(Icons.upload_file),
            onPressed: _isReady ? _pickCsv : null,
            tooltip: "Upload Knowledge Base (.csv)",
          )
        ],
      ),
      body: DashChat(
        currentUser: _user,
        onSend: _onSend,
        messages: messages,
        inputOptions: InputOptions(
          // Show different placeholder text based on initialization status
          inputDecoration: InputDecoration(
            hintText: _isReady
                ? "Ask me anything based on your data..."
                : "Loading AI Model and Database...",
          ),
          textCapitalization: TextCapitalization.sentences,
        ),
      ),
    );
  }
}