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I am currently working on a Flutter project where I need to implement a chatbot using the GPT-2 Mini model with ONNX Runtime. I have successfully loaded the GPT-2 Mini model into my Flutter app, but I am facing challenges with performing inference to obtain logical outputs.

Here is the corrected text with the ">" quote at the beginning of each line for Markdown syntax:

import 'dart:io';
import 'dart:math';
import 'dart:typed_data';
import 'package:flutter/services.dart';
import 'package:onnxruntime/onnxruntime.dart';
import 'package:path_provider/path_provider.dart';
import 'dart:convert';
import 'package:flutter/services.dart' show rootBundle;

class Phi3ChatModel {

  late OrtSession _session;
  late OrtSessionOptions _sessionOptions;
  late Map<String, dynamic> _tokenizerConfig;
  late Map<String, dynamic> _generationConfig;
  late Map<String, dynamic> _modelConfig;
  late Map<String, dynamic> _tokenizer;
  late Map<String, dynamic> _vocab;
  late Map<String, dynamic> _specialTokensMap;
  late int _vocabSize;
  late String _bosToken;
  late String _eosToken;
  late String _unkToken;
  late int _maxLength;

  Phi3ChatModel() : _sessionOptions = OrtSessionOptions() {
    OrtEnv.instance.init();
  }

  Future<void> initModel() async {
    try {
      print("Initializing GPT-2 model...");
      _sessionOptions = OrtSessionOptions()
        ..setInterOpNumThreads(1)
        ..setIntraOpNumThreads(1)
        ..setSessionGraphOptimizationLevel(GraphOptimizationLevel.ortEnableAll);

      final appDocDir = await getApplicationDocumentsDirectory();

      // Load ONNX model
      final modelFile = File('${appDocDir.path}/decoder_model.onnx');
      if (!await modelFile.exists()) {
        final data = await rootBundle.load('assets/models/gpt2/decoder_model.onnx');
        await modelFile.writeAsBytes(data.buffer.asUint8List());
      }
      _session = OrtSession.fromFile(modelFile, _sessionOptions);

      // Load configuration files
      _tokenizerConfig = await _loadJsonFile('assets/models/gpt2/tokenizer_config.json');
      _generationConfig = await _loadJsonFile('assets/models/gpt2/generation_config.json');
      _modelConfig = await _loadJsonFile('assets/models/gpt2/config.json');
      _tokenizer = await _loadJsonFile('assets/models/gpt2/tokenizer.json');
      _specialTokensMap = await _loadJsonFile('assets/models/gpt2/special_tokens_map.json');
      _vocab = await _loadJsonFile('assets/models/gpt2/vocab.json');

      // Initialize model parameters
      _vocabSize = _modelConfig['vocab_size'];
      _bosToken = _specialTokensMap['bos_token'];
      _eosToken = _specialTokensMap['eos_token'];
      _unkToken = _specialTokensMap['unk_token'];
      _maxLength = _modelConfig['n_positions'];

      print("GPT-2 model initialized successfully.");
    } catch (e) {
      print("Error initializing GPT-2 model: $e");
      rethrow;
    }
  }

  Future<Map<String, dynamic>> _loadJsonFile(String path) async {
    try {
      print("Loading JSON file $path...");
      String jsonString = await rootBundle.loadString(path);
      print("JSON file $path loaded successfully.");
      return json.decode(jsonString);
    } catch (e) {
      print("Error loading JSON file $path: $e");
      rethrow;
    }
  }

  List<int> encode(String text) {
    print("Encoding text: \"$text\"");
    List<int> tokens = [];
    for (String word in text.split(' ')) {
      if (_tokenizer['model']['vocab'].containsKey(word)) {
        tokens.add(_tokenizer['model']['vocab'][word]);
      } else {
        // TODO: handle unknown words
      }
    }
    print("Encoded text: $tokens");
    return tokens;
  }

  String decode(List<int> tokens) {
    print("Decoding tokens: $tokens");
    String text = '';
    for (int token in tokens) {
      if (_tokenizer['model']['vocab'].containsValue(token)) {
        String word = _tokenizer['model']['vocab'].keys.firstWhere((key) => _tokenizer['model']['vocab'][key] == token);
        text += '$word ';
      } else {
        // TODO: handle unknown tokens
      }
    }
    print("Decoded text: \"$text\"");
    return text.trim();
  }

  void processOutputTokens(Object outputTokensObject, List<int> generatedTokens) {
    if (outputTokensObject is List) {
      if (outputTokensObject[0] is List) {
        for (var item in outputTokensObject) {
          processOutputTokens(item, generatedTokens);
        }
      }
      if (outputTokensObject[0] is double) {
        var probs = _softmax(outputTokensObject as List<double>);
        var sample = _sampleFromProbs(probs);
        generatedTokens.add(sample);
      }
    }
  }

  Future<String> generateText(String prompt, {int maxNewTokens = 50}) async {
    try {
      print("Generating text with prompt: \"$prompt\"...");

      // Encode the prompt
      List<int> inputIds = encode(prompt);
      inputIds.insert(0, _modelConfig['bos_token_id']); // Beginning of sequence token
      inputIds.add(_modelConfig['eos_token_id']);

      // Initialize an empty list to store the generated tokens
      List<int> generatedTokens = [];

      var inputTensor = OrtValueTensor.createTensorWithDataList(
        inputIds,
        [1, inputIds.length],
      );

      // Create attention mask (all ones since there is no padding)
      var attentionMask = OrtValueTensor.createTensorWithDataList(
        Int64List.fromList(List.filled(inputIds.length, 1)),
        [1, inputIds.length],
      );

      var ortInput = {
        'input_ids': inputTensor,
        'attention_mask': attentionMask
      };

      final outputs = _session.run(
        OrtRunOptions(),
        ortInput
      );

      var out1 = outputs[0]?.value as List;

      processOutputTokens(out1, generatedTokens);

      inputTensor.release();
      attentionMask.release();

      // Decode the generated tokens
      String result = decode(generatedTokens);
      print("Generated text: \"$result\"");
      return result;
    } catch (e) {
      print("Error generating text: $e");
      return "Error: Unable to generate text.";
    }
  }


// Helper function to calculate softmax
  List<double> _softmax(List<double> logits) {
    print("Calculating softmax for logits: $logits");
    double maxLogit = logits.reduce((a, b) => a > b ? a : b);
    List<double> expLogits = logits.map((logit) => exp(logit - maxLogit)).toList();
    double sum = expLogits.reduce((a, b) => a + b);
    List<double> result = expLogits.map((expLogit) => expLogit / sum).toList();
    print("Softmax result: $result");
    return result;
  }

// Helper function to sample from probabilities
  int _sampleFromProbs(List<double> probs) {
    print("Sampling from probabilities: $probs");
    Random rand = Random();
    double cumulativeProb = 0.0;
    for (int i = 0; i < probs.length; i++) {
      cumulativeProb += probs[i];
      if (rand.nextDouble() < cumulativeProb) {
        int result = i;
        print("Sampled token: $result");
        return result;
      }
    }
    int result = probs.length - 1; // Fallback to last token
    print("Sampled token: $result");
    return result;
  }

  Future<String> predict(String inputData) async {
    print("----------------------------- predicting start -");
    final inputTensor = OrtValueTensor.createTensorWithDataList([inputData], [1]);
    print("----------------------------- predicting input tensor created -");
    final inputs = {'input': inputTensor};
    final outputs = await _session.runAsync(OrtRunOptions(), inputs);
    print("----------------------------- predicting infered -");
    inputTensor.release();
    print("----------------------------- predicting memory released  -");

    final response = outputs?[0]?.value as List<String>;
    outputs?.forEach((element) => element?.release());

    print("----------------------------- predicting done -");
    return response.first;
  }

  void release() {
    print("Releasing resources...");
    _sessionOptions.release();
    _session.release();
    OrtEnv.instance.release();
    print("Resources released.");
  }
}

import 'package:flutter/material.dart';
import 'package:onnxruntime_example/features/phi3_chat_model.dart';

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

 

@override
	
 _ONNXChatScreenState createState() => _ONNXChatScreenState();
}

class _ONNXChatScreenState extends State<ONNXChatScreen> {
 final TextEditingController _controller = TextEditingController();
 late Phi3ChatModel _chatbotModel;
 List<String> _messages = [];
 bool _isModelInitialized = false;

 

@override
	
 void initState() {
   super.initState();
   _initializeModel();
 }

 Future<void> _initializeModel() async {
   _chatbotModel = Phi3ChatModel();
   try {
     await _chatbotModel.initModel();
     setState(() {
       _isModelInitialized = true;
     });
   } catch (e) {
     print('Erreur lors de l\'initialisation du modèle : $e');
   }
 }

 

@override
	
 void dispose() {
   _chatbotModel.release();
   super.dispose();
 }

 void _sendMessage() async {
   final message = _controller.text;
   if (message.isEmpty) return;

   try {
     final response = await _chatbotModel.generateText(message);
     setState(() {
       _messages.add('You: $message');
       _messages.add('Bot: $response');
       _controller.clear();
     });
   } catch (e) {
     print('Erreur lors de l\'envoi du message : $e');
   }
 }

 

@override
	
 Widget build(BuildContext context) {
   return Scaffold(
     appBar: AppBar(title: const Text('ONNX Chatbot')),
     body: !_isModelInitialized
         ? const Center(child: CircularProgressIndicator())
         : Column(
       children: <Widget>[
         Expanded(
           child: ListView.builder(
             itemCount: _messages.length,
             itemBuilder: (context, index) => ListTile(
               title: Text(_messages[index]),
             ),
           ),
         ),
         Padding(
           padding: const EdgeInsets.all(8.0),
           child: Row(
             children: <Widget>[
               Expanded(
                 child: TextField(controller: _controller),
               ),
               IconButton(
                 icon: const Icon(Icons.send),
                 onPressed: _sendMessage,
               ),
             ],
           ),
         ),
       ],
     ),
   );
 }
}