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AI Integrations and Intelligent Data Processing with Firestore: Converting Flutter

Scaibu
7 min readSep 15, 2024

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In today’s data-driven world, integrating AI and intelligent data processing into mobile applications is becoming increasingly important. Flutter, with its powerful capabilities and seamless integration with Firebase, offers a great platform for such implementations. Here, we’ll walk through converting several advanced AI and data processing examples from JavaScript to Flutter using Dart and Firebase Firestore.

1. Recommendation System Using Collaborative Filtering

A recommendation system helps users discover relevant items based on their preferences and behaviors. Collaborative filtering is one approach to building such a system. Here’s how you can implement it in Flutter:

import 'package:cloud_firestore/cloud_firestore.dart';

Future<List<String>> getRecommendations(String userId, {int numRecommendations = 5}) async {
  final firestore = FirebaseFirestore.instance;

  // Get user's ratings
  final userRatingsDoc = await firestore.collection('userRatings').doc(userId).get();
  final userRatings = Map<String, dynamic>.from(userRatingsDoc.data()!['ratings']);

  // Find similar users
  final allUsersSnapshot = await firestore.collection('userRatings').get();
  final similarUsers = <Map<String, dynamic>>[];

  for (var doc in allUsersSnapshot.docs) {
    if (doc.id != userId) {
      final similarity = calculateSimilarity(userRatings, Map<String, dynamic>.from(doc.data()['ratings']));
      similarUsers.add({'id': doc.id, 'similarity': similarity});
    }
  }

  // Sort similar users by similarity
  similarUsers.sort((a, b) => b['similarity'].compareTo(a['similarity']));

  // Get recommendations
  final recommendations = <String>[];
  for (var user in similarUsers.take(10)) {
    final userRatingsDoc = await firestore.collection('userRatings').doc(user['id']).get();
    final userRatings = Map<String, dynamic>.from(userRatingsDoc.data()!['ratings']);

    for (var itemId in userRatings.keys) {
      if (!userRatings.containsKey(itemId) && !recommendations.contains(itemId)) {
        recommendations.add(itemId);
        if (recommendations.length >= numRecommendations) break;
      }
    }
    if (recommendations.length >= numRecommendations) break;
  }

  return recommendations;
}

double calculateSimilarity(Map<String, dynamic> ratings1, Map<String, dynamic> ratings2) {
  // Implement similarity calculation (e.g., cosine similarity)
  double dotProduct = 0;
  double magnitude1 = 0;
  double magnitude2 = 0;

  ratings1.forEach((itemId, rating) {
    if (ratings2.containsKey(itemId)) {
      dotProduct += rating * ratings2[itemId];
    }
    magnitude1 += rating * rating;
  });

  ratings2.forEach((_, rating) {
    magnitude2 += rating * rating;
  });

  return dotProduct / (sqrt(magnitude1) * sqrt(magnitude2));
}

// Usage
final recommendations = await getRecommendations('user123', numRecommendations: 5);
print(recommendations);

2. Text Classification with TensorFlow Lite and Firestore

Text classification can be performed using TensorFlow Lite models in Flutter. Here’s how to set up a basic text classifier:

import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:cloud_firestore/cloud_firestore.dart';

class TextClassifier {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  TextClassifier() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('model.tflite');
  }

  Future<Map<String, dynamic>> classifyText(String text, String userId) async {
    final processedText = _preprocessText(text);

    final input = [processedText]; // Adjust based on model input
    final output = List.generate(1, (index) => List.filled(10, 0.0)); // Adjust output size
    
    _interpreter.run(input, output);

    final classIndex = output[0].indexOf(output[0].reduce((a, b) => a > b ? a : b));
    final confidence = output[0][classIndex];

    // Get class label
    final classMapDoc = await _firestore.collection('classMapping').doc('textClassification').get();
    final classMap = Map<String, dynamic>.from(classMapDoc.data()!);
    final classLabel = classMap[classIndex.toString()];

    // Store result in Firestore
    await _firestore.collection('userClassifications').doc(userId).set({
      'text': text,
      'classLabel': classLabel,
      'confidence': confidence,
      'timestamp': DateTime.now().millisecondsSinceEpoch,
    }, SetOptions(merge: true));

    return {'classLabel': classLabel, 'confidence': confidence};
  }

  List<double> _preprocessText(String text) {
    // Implement text preprocessing
    // Return a list of doubles suitable for your model
    return [];
  }
}

// Usage
final classifier = TextClassifier();
final result = await classifier.classifyText("This is a sample text", "user123");
print(result);

3. Anomaly Detection in Time Series Data

Detecting anomalies in time series data involves identifying data points that deviate significantly from the norm. Here’s a Dart implementation:

import 'package:cloud_firestore/cloud_firestore.dart';
import 'dart:math';

Future<List<Map<String, dynamic>>> detectAnomalies(String seriesId, {int windowSize = 20, double threshold = 2.0}) async {
  final firestore = FirebaseFirestore.instance;
  final timeSeriesRef = firestore.collection('timeSeries').doc(seriesId).collection('dataPoints');
  
  final querySnapshot = await timeSeriesRef.orderBy('timestamp', descending: true).limit(100).get();
  final data = querySnapshot.docs.map((doc) => doc.data()['value'] as double).toList();
  data.reverse(); // Oldest first

  final anomalies = <Map<String, dynamic>>[];
  for (int i = windowSize; i < data.length; i++) {
    final window = data.sublist(i - windowSize, i);
    final mean = window.reduce((a, b) => a + b) / windowSize;
    final stdDev = sqrt(window.map((val) => (val - mean) * (val - mean)).reduce((a, b) => a + b) / windowSize);

    final zScore = (data[i] - mean) / stdDev;
    if (zScore.abs() > threshold) {
      anomalies.add({'index': i, 'value': data[i], 'zScore': zScore});
    }
  }

  return anomalies;
}

// Usage
final anomalies = await detectAnomalies('temperatureSensor1');
print(anomalies);

4. Natural Language Processing with Firestore

Sentiment analysis is a common NLP task. Here’s how to perform sentiment analysis and store results in Firestore:

import 'package:cloud_firestore/cloud_firestore.dart';
import 'package:natural/natural.dart'; // Hypothetical package

final analyzer = SentimentAnalyzer('afinn');

Future<Map<String, dynamic>> analyzeSentiment(String text, String documentId) async {
  final words = WordTokenizer().tokenize(text);
  final sentiment = analyzer.getSentiment(words);

  final firestore = FirebaseFirestore.instance;

  // Store result in Firestore
  await firestore.collection('sentimentAnalysis').doc(documentId).set({
    'text': text,
    'sentiment': sentiment,
    'timestamp': DateTime.now().millisecondsSinceEpoch,
  });

  return {'sentiment': sentiment};
}

// Usage
final result = await analyzeSentiment("I love this product!", "review123");
print(result);

5. Image Classification with TensorFlow Lite and Firestore

Image classification can be achieved with TensorFlow Lite models. Here’s a simple implementation for classifying images:

import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:tflite_flutter/tflite_flutter_helper.dart';
import 'package:cloud_firestore/cloud_firestore.dart';
import 'dart:io';
import 'dart:typed_data';
import 'package:image/image.dart' as img;

class ImageClassifier {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  ImageClassifier() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('mobilenet_v1_0.25_224.tflite');
  }

  Future<List<Map<String, dynamic>>> classifyImage(Uint8List imageBuffer, String imageId) async {
    if (_interpreter == null) await _loadModel();

    final image = img.decodeImage(imageBuffer)!;
    final resizedImage = img.copyResize(image, width: 224, height: 224);
    final inputImage = TensorImage.fromImage(resizedImage);

    final input = inputImage.buffer.asFloat32List();
    final output = List.generate(1, (index) => List.filled(1001, 0.0)); // Adjust based on model output size

    _interpreter.run(input, output);

    final topClasses = List<Map<String, dynamic>>.generate(3, (index) {
      final classIndex = output[0].indexOf(output[0].reduce((a, b) => a > b ? a : b));
      final className = MODEL_CLASSES[classIndex]; // Assume MODEL_CLASSES is defined
      final probability = output[0][classIndex];
      return {'className': className, 'probability': probability};
    });

    // Store result in Firestore
    await _firestore.collection('imageClassifications').doc(imageId).set({
      'topClasses': topClasses,
      'timestamp': DateTime.now().millisecondsSinceEpoch,
    });

    return topClasses;
  }
}

// Usage
final imageFile = File('path/to/image.jpg');
final imageBuffer = await imageFile.readAsBytes();
final classifier = ImageClassifier();
final result = await classifier.classifyImage(imageBuffer, 'image123');
print(result);

6. Chatbot with Dialogflow and Firestore

Integrating a chatbot can enhance user interaction. Here’s how to use Dialogflow for chatbot functionality:

import 'package:cloud_firestore/cloud_firestore.dart';

Stream<List<String>> getRealTimeRecommendations(String userId) async* {
  final firestore = FirebaseFirestore.instance;

  yield* firestore.collection('userRatings').doc(userId).snapshots().asyncMap((snapshot) async {
    final userRatings = Map<String, dynamic>.from(snapshot.data()!['ratings']);
    final allUsersSnapshot = await firestore.collection('userRatings').get();
    final similarUsers = <Map<String, dynamic>>[];

    for (var doc in allUsersSnapshot.docs) {
      if (doc.id != userId) {
        final similarity = calculateSimilarity(userRatings, Map<String, dynamic>.from(doc.data()['ratings']));
        similarUsers.add({'id': doc.id, 'similarity': similarity});
      }
    }

    similarUsers.sort((a, b) => b['similarity'].compareTo(a['similarity']));
    final recommendations = <String>[];

    for (var user in similarUsers.take(10)) {
      final userRatingsDoc = await firestore.collection('userRatings').doc(user['id']).get();
      final userRatings = Map<String, dynamic>.from(userRatingsDoc.data()!['ratings']);

      for (var itemId in userRatings.keys) {
        if (!userRatings.containsKey(itemId) && !recommendations.contains(itemId)) {
          recommendations.add(itemId);
          if (recommendations.length >= 5) break;
        }
      }
      if (recommendations.length >= 5) break;
    }

    return recommendations;
  });
}

// Usage
final recommendationsStream = getRealTimeRecommendations('user123');
recommendationsStream.listen((recommendations) {
  print(recommendations);
});

7. User Behavior Prediction

Predict user behavior based on historical data stored in Firestore:

import 'package:cloud_firestore/cloud_firestore.dart';
import 'package:tflite_flutter/tflite_flutter.dart';

class BehaviorPredictor {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  BehaviorPredictor() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('behavior_model.tflite');
  }

  Future<String> predictBehavior(String userId) async {
    final userBehaviorDoc = await _firestore.collection('userBehaviors').doc(userId).get();
    final historicalData = List<double>.from(userBehaviorDoc.data()!['data']);
    
    final input = [historicalData];
    final output = List.filled(1, 0.0); // Adjust based on model output size

    _interpreter.run(input, output);

    final prediction = output[0];
    return prediction > 0.5 ? 'High' : 'Low';
  }
}

// Usage
final predictor = BehaviorPredictor();
final prediction = await predictor.predictBehavior('user123');
print(prediction);

8. Personalized Ad Targeting

Target personalized ads based on user interests:

import 'package:cloud_firestore/cloud_firestore.dart';
import 'package:tflite_flutter/tflite_flutter.dart';

class AdTargeting {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  AdTargeting() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('ad_targeting_model.tflite');
  }

  Future<List<String>> getTargetedAds(String userId) async {
    final userInterestsDoc = await _firestore.collection('userInterests').doc(userId).get();
    final interests = List<double>.from(userInterestsDoc.data()!['interests']);
    
    final input = [interests];
    final output = List.generate(1, (index) => List.filled(100, 0.0)); // Adjust based on model output size

    _interpreter.run(input, output);

    final topAdIndices = List<int>.generate(5, (index) => output[0].indexOf(output[0].reduce((a, b) => a > b ? a : b)));
    final ads = topAdIndices.map((index) => 'Ad_${index + 1}').toList();

    return ads;
  }
}

// Usage
final adTargeting = AdTargeting();
final ads = await adTargeting.getTargetedAds('user123');
print(ads);

Voice Command Recognition

Integrate voice command recognition using TensorFlow Lite:

import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:cloud_firestore/cloud_firestore.dart';
import 'package:flutter_sound/flutter_sound.dart';
import 'dart:io';
import 'dart:typed_data';

class VoiceCommandRecognizer {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;
  final FlutterSoundRecorder _recorder = FlutterSoundRecorder();

  VoiceCommandRecognizer() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('voice_command_model.tflite');
  }

  Future<String> recognizeCommand() async {
    await _recorder.startRecorder(toFile: 'temp.wav');
    // Record audio (simplified)
    await Future.delayed(Duration(seconds: 2));
    await _recorder.stopRecorder();

    final audioData = await File('temp.wav').readAsBytes();
    final input = _preprocessAudio(audioData);
    final output = List.filled(1, 0.0); // Adjust based on model output size

    _interpreter.run(input, output);

    final commandIndex = output[0].toInt();
    final command = await _getCommandLabel(commandIndex);

    return command;
  }

  List<double> _preprocessAudio(Uint8List audioData) {
    // Implement audio preprocessing
    return [];
  }

  Future<String> _getCommandLabel(int index) async {
    final labelDoc = await _firestore.collection('voiceCommands').doc(index.toString()).get();
    return labelDoc.data()!['label'];
  }
}

// Usage
final recognizer = VoiceCommandRecognizer();
final command = await recognizer.recognizeCommand();
print(command);

10. Fraud Detection in Transactions

Detect fraudulent transactions based on historical data:

import 'package:cloud_firestore/cloud_firestore.dart';
import 'package:tflite_flutter/tflite_flutter.dart';

class FraudDetector {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  FraudDetector() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('fraud_detection_model.tflite');
  }

  Future<bool> isFraudulent(String transactionId) async {
    final transactionDoc = await _firestore.collection('transactions').doc(transactionId).get();
    final transactionData = Map<String, dynamic>.from(transactionDoc.data()!);
    
    final input = [transactionData.values.toList()];
    final output = List.filled(1, 0.0); // Adjust based on model output size

    _interpreter.run(input, output);

    return output[0] > 0.5;
  }
}

// Usage
final detector = FraudDetector();
final isFraudulent = await detector.isFraudulent('transaction123');
print(isFraudulent);

11. Customer Support Ticket Classification

Classify customer support tickets using a model:

import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:cloud_firestore/cloud_firestore.dart';

class TicketClassifier {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  TicketClassifier() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('ticket_classifier_model.tflite');
  }

  Future<String> classifyTicket(String ticketId) async {
    final ticketDoc = await _firestore.collection('supportTickets').doc(ticketId).get();
    final ticketText = ticketDoc.data()!['text'];
    
    final input = [_preprocessText(ticketText)];
    final output = List.generate(1, (index) => List.filled(10, 0.0)); // Adjust based on model output size

    _interpreter.run(input, output);

    final classIndex = output[0].indexOf(output[0].reduce((a, b) => a > b ? a : b));
    final classLabel = await _getClassLabel(classIndex);

    return classLabel;
  }

  List<double> _preprocessText(String text) {
    // Implement text preprocessing
    return [];
  }

  Future<String> _getClassLabel(int index) async {
    final labelDoc = await _firestore.collection('ticketLabels').doc(index.toString()).get();
    return labelDoc.data()!['label'];
  }
}

// Usage
final classifier = TicketClassifier();
final label = await classifier.classifyTicket('ticket123');
print(label);

12. Predictive Maintenance for Equipment

Predict equipment failure using historical sensor data:

import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:cloud_firestore/cloud_firestore.dart';

class MaintenancePredictor {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  MaintenancePredictor() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('maintenance_model.tflite');
  }

  Future<String> predictFailure(String equipmentId) async {
    final sensorDataDoc = await _firestore.collection('sensorData').doc(equipmentId).get();
    final sensorData = List<double>.from(sensorDataDoc.data()!['data']);
    
    final input = [sensorData];
    final output = List.filled(1, 0.0); // Adjust based on model output size

    _interpreter.run(input, output);

    return output[0] > 0.5 ? 'Failure Likely' : 'Failure Unlikely';
  }
}

// Usage
final predictor = MaintenancePredictor();
final prediction = await predictor.predictFailure('equipment123');
print(prediction);

13. Stock Price Prediction

Predict stock prices using historical data:

import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:cloud_firestore/cloud_firestore.dart';

class StockPredictor {
  late Interpreter _interpreter;
  final FirebaseFirestore _firestore = FirebaseFirestore.instance;

  StockPredictor() {
    _loadModel();
  }

  Future<void> _loadModel() async {
    _interpreter = await Interpreter.fromAsset('stock_model.tflite');
  }

  Future<double> predictPrice(String stockId) async {
    final stockDataDoc = await _firestore.collection('stockData').doc(stockId).get();
    final stockPrices = List<double>.from(stockDataDoc.data()!['prices']);
    
    final input = [stockPrices];
    final output = List.filled(1, 0.0); // Adjust based on model output size

    _interpreter.run(input, output);

    return output[0];
  }
}

// Usage
final predictor = StockPredictor();
final predictedPrice = await predictor.predictPrice('AAPL');
print(predictedPrice);
Flutter

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Scaibu
Scaibu

Written by Scaibu

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