AI-Powered Mobile Development: How Machine Learning is Revolutionizing App Creation in 2025

The mobile development landscape has undergone a dramatic transformation in 2025, with artificial intelligence and machine learning emerging as fundamental pillars of modern app creation. What once required months of manual coding, extensive testing cycles, and iterative design processes can now be accelerated through intelligent automation and predictive capabilities. This revolution extends far beyond simple code completion—AI is reshaping every aspect of mobile development, from initial conception to post-deployment optimization.

Today's mobile developers are leveraging AI-powered tools that can generate entire code modules, predict user behavior, automate testing workflows, and even optimize app performance in real-time. Companies like Netflix have seen 80% increases in user engagement through AI-driven personalization engines, while Spotify's machine learning algorithms have significantly boosted session duration by creating more relevant user experiences. These aren't isolated successes—they represent a fundamental shift in how mobile applications are conceived, built, and maintained.

The implications extend beyond individual productivity gains. Organizations implementing AI-powered mobile development workflows report 40-60% improvements in development velocity, substantial reductions in bug rates, and accelerated time-to-market for new features. However, this transformation also brings new challenges: managing AI tool dependencies, ensuring code quality with automated generation, and addressing privacy concerns in an increasingly intelligent mobile ecosystem.

AI-Driven Development Tools and Code Generation

The foundation of AI-powered mobile development lies in intelligent tooling that augments human creativity with machine precision. GitHub Copilot and Amazon CodeWhisperer have evolved from simple autocomplete tools to sophisticated development partners capable of understanding context, generating complex functions, and suggesting architectural improvements. These tools analyze millions of code repositories to provide suggestions that often exceed what individual developers might conceive independently.

Modern AI-powered testing frameworks have revolutionized quality assurance by automatically generating comprehensive test suites. These systems can create unit tests, integration tests, and UI automation scripts by analyzing code structure and identifying potential edge cases. Tools like Diffblue Cover and Facebook's Sapienz use machine learning to generate tests that achieve higher coverage rates than traditional manual approaches, often uncovering bugs that human testers might miss.

Machine learning models for bug detection have become increasingly sophisticated, capable of identifying potential issues before they reach production. Static analysis AI tools can predict code smells, security vulnerabilities, and performance bottlenecks by analyzing patterns across codebases. Companies implementing these tools report 30-50% reductions in post-deployment bugs and significant improvements in code maintainability.

Natural language to code conversion represents one of the most exciting frontiers in AI-assisted development. Developers can now describe functionality in plain English and receive working code implementations. This capability accelerates prototyping phases and enables non-technical stakeholders to contribute more directly to the development process.

Here's an example of AI-assisted React Native development with TensorFlow.js integration:

import React, { useState, useEffect } from 'react';
import { View, Text, Image, TouchableOpacity, Alert } from 'react-native';
import * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-react-native';

interface ImageClassificationProps {
  imageUri: string;
  onClassification: (result: string) => void;
}

const ImageClassificationComponent: React.FC<ImageClassificationProps> = ({
  imageUri,
  onClassification,
}) => {
  const [model, setModel] = useState<tf.LayersModel | null>(null);
  const [isProcessing, setIsProcessing] = useState(false);
  const [classification, setClassification] = useState<string>('');

  useEffect(() => {
    loadModel();
  }, []);

  const loadModel = async (): Promise<void> => {
    try {
      await tf.ready();
      const loadedModel = await tf.loadLayersModel('path/to/your/model.json');
      setModel(loadedModel);
    } catch (error) {
      console.error('Error loading TensorFlow model:', error);
      Alert.alert('Error', 'Failed to load AI model');
    }
  };

  const classifyImage = async (): Promise<void> => {
    if (!model || !imageUri) {
      Alert.alert('Error', 'Model not loaded or image not available');
      return;
    }

    setIsProcessing(true);
    try {
      const response = await fetch(imageUri);
      const imageData = await response.arrayBuffer();
      
      // Convert image to tensor
      const imageTensor = tf.node.decodeImage(imageData)
        .resizeNearestNeighbor([224, 224])
        .toFloat()
        .div(tf.scalar(255.0))
        .expandDims();

      const predictions = model.predict(imageTensor) as tf.Tensor;
      const predictionData = await predictions.data();
      
      // Get top prediction
      const maxIndex = predictionData.indexOf(Math.max(...predictionData));
      const confidence = predictionData[maxIndex];
      
      const result = `Classification: ${getClassLabel(maxIndex)} (${(confidence * 100).toFixed(2)}%)`;
      setClassification(result);
      onClassification(result);
      
      // Clean up tensors
      imageTensor.dispose();
      predictions.dispose();
    } catch (error) {
      console.error('Classification error:', error);
      Alert.alert('Error', 'Image classification failed');
    } finally {
      setIsProcessing(false);
    }
  };

  const getClassLabel = (index: number): string => {
    const labels = ['Cat', 'Dog', 'Bird', 'Car', 'Person'];
    return labels[index] || 'Unknown';
  };

  return (
    <View style={{ padding: 20 }}>
      <Image source={{ uri: imageUri }} style={{ width: 200, height: 200 }} />
      <TouchableOpacity
        onPress={classifyImage}
        disabled={!model || isProcessing}
        style={{
          backgroundColor: isProcessing ? '#ccc' : '#007AFF',
          padding: 15,
          borderRadius: 8,
          marginTop: 20,
        }}
      >
        <Text style={{ color: 'white', textAlign: 'center' }}>
          {isProcessing ? 'Processing...' : 'Classify Image'}
        </Text>
      </TouchableOpacity>
      {classification && (
        <Text style={{ marginTop: 20, fontSize: 16 }}>{classification}</Text>
      )}
    </View>
  );
};

export default ImageClassificationComponent;

Automated refactoring tools powered by AI can identify technical debt and suggest improvements across entire codebases. These systems analyze code patterns, dependency structures, and performance characteristics to recommend optimizations that maintain functionality while improving maintainability and performance.

Intelligent User Interface and Experience Design

AI-powered interface design has moved beyond static layouts to create truly adaptive user experiences. Dynamic UI adaptation systems analyze user behavior patterns, device capabilities, and contextual factors to optimize interface elements in real-time. These systems can adjust button sizes for users with accessibility needs, reorganize navigation based on usage patterns, and modify color schemes for different lighting conditions.

Personalization engines have become sophisticated enough to create unique experiences for individual users while maintaining consistent brand identity. Netflix's recommendation system, which contributed to their 80% user engagement increase, represents the gold standard for AI-driven personalization. These systems analyze viewing history, time of day, device usage, and even pause patterns to surface relevant content.

Automated A/B testing powered by machine learning has revolutionized conversion optimization. Traditional A/B testing requires manual hypothesis formation and statistical analysis. AI-powered systems can automatically generate test variations, determine optimal sample sizes, detect statistical significance, and even suggest new tests based on results. This automation allows teams to run dozens of concurrent experiments while maintaining statistical rigor.

Voice and natural language interfaces have matured significantly, with speech recognition APIs achieving near-human accuracy. Integration with services like Google Cloud Speech-to-Text and Apple's Speech framework enables developers to create conversational interfaces that understand context, intent, and user preferences. The key is implementing robust error handling and fallback mechanisms for edge cases where recognition fails.

Computer vision capabilities for gesture recognition and augmented reality have opened new interaction paradigms. Modern mobile devices can track hand movements, recognize facial expressions, and overlay digital content onto real-world environments with minimal latency. Successful implementations focus on intuitive gestures that feel natural to users while providing clear visual feedback.

Predictive Analytics and User Behavior Intelligence

Real-time user engagement prediction has become a critical capability for mobile applications. Churn prevention algorithms analyze user behavior patterns, session frequency, feature usage, and engagement metrics to identify users at risk of abandoning the app. Duolingo's adaptive learning algorithms exemplify this approach, personalizing language education paths to maintain user motivation and reduce churn.

Machine learning models for app performance optimization can predict resource requirements, identify bottlenecks before they impact users, and automatically adjust system parameters. These models analyze historical performance data, user load patterns, and system metrics to optimize everything from database queries to network requests.

Predictive caching represents a sophisticated application of machine learning to improve user experience. By analyzing usage patterns, these systems can pre-load content, cache frequently accessed data, and optimize storage allocation. Spotify's music recommendation system demonstrates this concept by pre-loading songs users are likely to play, reducing playback latency and improving perceived performance.

Here's an example of Core ML integration for on-device sentiment analysis:

import Foundation
import CoreML
import NaturalLanguage

class SentimentAnalysisManager {
    private var sentimentModel: MLModel?
    private let textProcessor = NLTokenizer(unit: .word)
    
    init() {
        loadModel()
    }
    
    private func loadModel() {
        guard let modelURL = Bundle.main.url(forResource: "SentimentClassifier", withExtension: "mlmodelc") else {
            print("Error: Could not find SentimentClassifier.mlmodelc in bundle")
            return
        }
        
        do {
            sentimentModel = try MLModel(contentsOf: modelURL)
        } catch {
            print("Error loading Core ML model: \(error.localizedDescription)")
        }
    }
    
    func analyzeSentiment(text: String, completion: @escaping (Result<SentimentResult, SentimentError>) -> Void) {
        guard let model = sentimentModel else {
            completion(.failure(.modelNotLoaded))
            return
        }
        
        guard !text.trimmingCharacters(in: .whitespacesAndNewlines).isEmpty else {
            completion(.failure(.invalidInput))
            return
        }
        
        DispatchQueue.global(qos: .userInitiated).async { [weak self] in
            do {
                // Preprocess text
                guard let processedFeatures = self?.preprocessText(text) else {
                    DispatchQueue.main.async {
                        completion(.failure(.preprocessingFailed))
                    }
                    return
                }
                
                // Create ML input
                let input = try MLDictionaryFeatureProvider(dictionary: processedFeatures)
                
                // Make prediction
                let prediction = try model.prediction(from: input)
                
                // Extract results
                guard let sentimentProbabilities = prediction.featureValue(for: "sentimentProbability")?.dictionaryValue,
                      let positiveProbability = sentimentProbabilities["Positive"]?.doubleValue,
                      let negativeProbability = sentimentProbabilities["Negative"]?.doubleValue else {
                    DispatchQueue.main.async {
                        completion(.failure(.predictionFailed))
                    }
                    return
                }
                
                let result = SentimentResult(
                    sentiment: positiveProbability > negativeProbability ? .positive : .negative,
                    confidence: max(positiveProbability, negativeProbability),
                    positiveScore: positiveProbability,
                    negativeScore: negativeProbability
                )
                
                DispatchQueue.main.async {
                    completion(.success(result))
                }
                
            } catch {
                DispatchQueue.main.async {
                    completion(.failure(.predictionFailed))
                }
            }
        }
    }
    
    private func preprocessText(_ text: String) -> [String: Any]? {
        // Tokenize and clean text
        let cleanedText = text.lowercased()
            .trimmingCharacters(in: .whitespacesAndNewlines)
            .replacingOccurrences(of: "[^a-zA-Z0-9\\s]", with: "", options: .regularExpression)
        
        textProcessor.string = cleanedText
        let tokens = textProcessor.tokens(for: cleanedText.startIndex..<cleanedText.endIndex)
        
        let words = tokens.map { String(cleanedText[$0]) }
        
        // Create feature vector (simplified bag-of-words approach)
        var features: [String: Any] = [:]
        features["text"] = cleanedText
        features["wordCount"] = words.count
        
        return features
    }
    
    func batchAnalyzeSentiments(texts: [String], completion: @escaping ([SentimentResult]) -> Void) {
        let group = DispatchGroup()
        var results: [SentimentResult] = Array(repeating: SentimentResult.neutral, count: texts.count)
        
        for (index, text) in texts.enumerated() {
            group.enter()
            analyzeSentiment(text: text) { result in
                switch result {
                case .success(let sentimentResult):
                    results[index] = sentimentResult
                case .failure:
                    results[index] = SentimentResult.neutral
                }
                group.leave()
            }
        }
        
        group.notify(queue: .main) {
            completion(results)
        }
    }
}

struct SentimentResult {
    let sentiment: SentimentType
    let confidence: Double
    let positiveScore: Double
    let negativeScore: Double
    
    static let neutral = SentimentResult(
        sentiment: .neutral,
        confidence: 0.5,
        positiveScore: 0.5,
        negativeScore: 0.5
    )
}

enum SentimentType {
    case positive
    case negative
    case neutral
}

enum SentimentError: Error {
    case modelNotLoaded
    case invalidInput
    case preprocessingFailed
    case predictionFailed
    
    var localizedDescription: String {
        switch self {
        case .modelNotLoaded:
            return "Sentiment analysis model is not loaded"
        case .invalidInput:
            return "Input text is invalid or empty"
        case .preprocessingFailed:
            return "Text preprocessing failed"
        case .predictionFailed:
            return "Sentiment prediction failed"
        }
    }
}

Anomaly detection for security threats and unusual user behavior has become essential for maintaining app integrity. These systems can identify suspicious login patterns, unusual data access requests, and potential security breaches in real-time. Instagram's AI-powered content moderation system demonstrates how machine learning can automatically detect and respond to policy violations while minimizing false positives.

Revenue optimization through intelligent pricing and recommendation systems leverages user behavior data to maximize monetization while maintaining user satisfaction. Uber's real-time demand prediction and dynamic pricing optimization exemplifies this approach, balancing supply and demand while optimizing both driver earnings and rider satisfaction.

AI-Enhanced Backend Services and Architecture

Serverless functions with machine learning inference capabilities have revolutionized how mobile applications handle AI workloads. Services like AWS Lambda with integrated ML inference, Google Cloud Functions with AI Platform integration, and Azure Functions with Cognitive Services enable developers to deploy sophisticated AI capabilities without managing infrastructure. These services automatically scale based on demand and provide cost-effective solutions for variable workloads.

Intelligent API gateway routing and load balancing systems use machine learning to optimize request distribution based on real-time performance metrics, geographical factors, and historical patterns. These systems can predict server load, route requests to optimal endpoints, and automatically failover to backup systems when issues are detected.

Automated database optimization and query performance tuning have become critical as mobile applications generate increasingly complex data access patterns. AI-powered systems can analyze query performance, suggest index optimizations, and automatically adjust database configurations based on usage patterns. These tools can identify slow queries, recommend schema improvements, and optimize data partitioning strategies.

Smart caching strategies using predictive algorithms analyze user behavior patterns to determine what data should be cached, where it should be stored, and when it should be refreshed. These systems consider factors like geographical location, time of day, user preferences, and seasonal patterns to optimize cache hit rates and reduce latency.

Here's an example of Android ML Kit implementation for text recognition and smart replies:

import android.graphics.Bitmap
import android.util.Log
import com.google.mlkit.nl.smartreply.SmartReply
import com.google.mlkit.nl.smartreply.SmartReplySuggestion
import com.google.mlkit.nl.smartreply.SmartReplySuggestionResult
import com.google.mlkit.nl.smartreply.TextMessage
import com.google.mlkit.vision.common.InputImage
import com.google.mlkit.vision.text.TextRecognition
import com.google.mlkit.vision.text.latin.TextRecognizerOptions
import kotlinx.coroutines.*

class MLKitTextProcessor {
    private val textRecognizer = TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS)
    private val smartReplyGenerator = SmartReply.getClient()
    private val coroutineScope = CoroutineScope(Dispatchers.Main + SupervisorJob())

    sealed class ProcessingResult {
        data class Success(val extractedText: String, val smartReplies: List<String>) : ProcessingResult()
        data class TextOnlySuccess(val extractedText: String) : ProcessingResult()
        data class Error(val exception: Exception) : ProcessingResult()
    }

    fun processImageWithSmartReply(
        bitmap: Bitmap,
        conversationHistory: List<ChatMessage> = emptyList(),
        callback: (ProcessingResult) -> Unit
    ) {
        coroutineScope.launch {
            try {
                val extractedText = extractTextFromImage(bitmap)
                
                if (extractedText.isNotEmpty()) {
                    if (conversationHistory.isNotEmpty()) {
                        val smartReplies = generateSmartReplies(conversationHistory + ChatMessage(extractedText, false))
                        callback(ProcessingResult.Success(extractedText, smartReplies))
                    } else {
                        callback(ProcessingResult.TextOnlySuccess(extractedText))
                    }
                } else {
                    callback(ProcessingResult.Error(Exception("No text found in image")))
                }
            } catch (e: Exception) {
                Log.e("MLKitTextProcessor", "Error processing image", e)
                callback(ProcessingResult.Error(e))
            }
        }
    }

    private suspend fun extractTextFromImage(bitmap: Bitmap): String = withContext(Dispatchers.IO) {
        try {
            val inputImage = InputImage.fromBitmap(bitmap, 0)
            val result = textRecognizer.process(inputImage).await()
            
            val extractedText = result.text.trim()
            Log.d("MLKitTextProcessor", "Extracted text: $extractedText")
            
            extractedText
        } catch (e: Exception) {
            Log.e("MLKitTextProcessor", "Text extraction failed", e)
            throw e
        }
    }

    private suspend fun generateSmartReplies(conversation: List<ChatMessage>): List<String> = withContext(Dispatchers.IO) {
        try {
            if (conversation.isEmpty()) {
                return@withContext emptyList<String>()
            }

            val textMessages = conversation.takeLast(10).map { message ->
                if (message.isFromLocalUser) {
                    TextMessage.createForLocalUser(message.text, System.currentTimeMillis())
                } else {
                    TextMessage.createForRemoteUser(message.text, System.currentTimeMillis(), "user")
                }
            }

            val result = smartReplyGenerator.suggestReplies(textMessages).await()
            
            return@withContext when (result.status) {
                SmartReplySuggestionResult.STATUS_SUCCESS -> {
                    result.suggestions.map { it.text }
                }
                SmartReplySuggestionResult.STATUS_NOT_SUPPORTED_LANGUAGE -> {
                    Log.w("MLKitTextProcessor", "Language not supported for smart replies")
                    emptyList()
                }
                SmartReplySuggestionResult.STATUS_NO_REPLY -> {
                    Log.d("MLKitTextProcessor", "No smart replies available")
                    emptyList()
                }
                else -> {
                    Log.w("MLKitTextProcessor", "Unknown smart reply status: ${result.status}")
                    emptyList()
                }
            }
        } catch (e: Exception) {
            Log.e("MLKitTextProcessor", "Smart reply generation failed", e)
            emptyList()
        }
    }

    fun processTextForSmartReplies(
        inputText: String,
        conversationHistory: List<ChatMessage> = emptyList(),
        callback: (List<String>) -> Unit
    ) {
        coroutineScope.launch {
            try {
                val conversation = conversationHistory + ChatMessage(inputText, false)
                val smartReplies = generateSmartReplies(conversation)
                callback(smartReplies)
            } catch (e: Exception) {
                Log.e("MLKitTextProcessor", "Error generating smart replies", e)
                callback(emptyList())
            }
        }
    }

    fun batchProcessImages(
        bitmaps: List<Bitmap>,
        callback: (List<String>) -> Unit
    ) {
        coroutineScope.launch {
            try {
                val results = bitmaps.map { bitmap ->
                    async(Dispatchers.IO) {
                        extractTextFromImage(bitmap)
                    }
                }.awaitAll()
                
                callback(results)
            } catch (e: Exception) {
                Log.e("MLKitTextProcessor", "Batch processing failed", e)
                callback(emptyList())
            }
        }
    }

    fun cleanup() {
        coroutineScope.cancel()
        textRecognizer.close()
        smartReplyGenerator.close()
    }
}

data class ChatMessage(
    val text: String,
    val isFromLocalUser: Boolean,
    val timestamp: Long = System.currentTimeMillis()
)

// Extension function to await ML Kit tasks
suspend fun <T> com.google.android.gms.tasks.Task<T>.await(): T {
    return suspendCancellableCoroutine { continuation ->
        addOnCompleteListener { task ->
            if (task.exception != null) {
                continuation.resumeWithException(task.exception!!)
            } else {
                continuation.resume(task.result)
            }
        }
    }
}

AI-powered infrastructure scaling and cost optimization systems analyze application usage patterns, predict demand fluctuations, and automatically adjust resources to maintain performance while minimizing costs. These systems can scale database connections, adjust server capacity, and optimize content delivery network configurations based on real-time and predicted demand.

Development Workflow Automation and DevOps

Intelligent CI/CD pipelines have transformed software delivery by incorporating machine learning into every stage of the development lifecycle. These systems can automatically determine when code is ready for deployment, select appropriate testing strategies, and even roll back deployments when anomalies are detected. Advanced pipelines analyze code changes, historical performance data, and risk factors to make intelligent deployment decisions.

AI-driven code review and security vulnerability detection have become essential tools for maintaining code quality at scale. These systems can identify potential security issues, suggest performance optimizations, and ensure compliance with coding standards. Tools like DeepCode and Snyk use machine learning to analyze code patterns and identify vulnerabilities that traditional static analysis tools might miss.

Automated app store optimization and release management leverage machine learning to optimize app store listings, manage staged rollouts, and monitor user feedback. These systems can automatically generate app descriptions, optimize keyword usage, and adjust rollout percentages based on user reception and crash rates.

Performance monitoring with machine learning anomaly detection provides unprecedented visibility into application behavior. These systems establish baseline performance metrics and automatically alert teams when deviations occur. They can identify performance regressions before they impact users and provide detailed insights into root causes.

Intelligent incident response and root cause analysis systems can automatically correlate events across distributed systems, identify likely causes of issues, and even suggest remediation steps. These systems reduce mean time to resolution and help teams learn from incidents to prevent future occurrences.

Cross-Platform Development with AI Assistance

Flutter and React Native development have been significantly enhanced by AI-powered tooling that can generate platform-specific code, optimize performance, and ensure consistent user experiences across devices. These tools analyze design specifications and automatically generate responsive layouts that work seamlessly across different screen sizes and platforms.

Here's an example of Flutter TensorFlow Lite integration for predictive user behavior analytics:

import 'dart:io';
import 'dart:typed_data';
import 'package:flutter/material.dart';
import 'package:tflite_flutter/tflite_flutter.dart';
import 'package:shared_preferences/shared_preferences.dart';

class UserBehaviorPredictor {
  static const String _modelPath = 'assets/models/user_behavior_model.tflite';
  static const String _userDataKey = 'user_behavior_data';
  
  Interpreter? _interpreter;
  bool _isModelLoaded = false;
  List<List<double>> _userBehaviorHistory = [];

  Future<void> loadModel() async {
    try {
      _interpreter = await Interpreter.fromAsset(_modelPath);
      _isModelLoaded = true;
      print('User behavior prediction model loaded successfully');
      await _loadUserBehaviorHistory();
    } catch (e) {
      print('Error loading model: $e');
      _isModelLoaded = false;
    }
  }

  Future<void> _loadUserBehaviorHistory() async {
    try {
      final prefs = await SharedPreferences.getInstance();
      final behaviorData = prefs.getStringList(_userDataKey);
      
      if (behaviorData != null) {
        _userBehaviorHistory = behaviorData.map((data) {
          return data.split(',').map((value) => double.parse(value)).toList();
        }).toList();
      }
    } catch (e) {
      print('Error loading user behavior history: $e');
      _userBehaviorHistory = [];
    }
  }

  Future<void> _saveUserBehaviorHistory() async {
    try {
      final prefs = await SharedPreferences.getInstance();
      final behaviorData = _userBehaviorHistory.map((behavior) {
        return behavior.join(',');
      }).toList();
      
      await prefs.setStringList(_userDataKey, behaviorData);
    } catch (e) {
      print('Error saving user behavior history: $e');
    }
  }

  Future<UserBehaviorPrediction?> predictUserBehavior(UserSession session) async {
    if (!_isModelLoaded || _interpreter == null) {
      print('Model not loaded');
      return null;
    }

    try {
      // Convert user session to input features
      final inputFeatures = _extractFeatures(session);
      
      // Prepare input tensor
      final input = [inputFeatures];
      final inputTensor = input.map((row) => row.map((value) => value).toList()).toList();
      
      // Prepare output tensor
      final output = List.filled(1, List.filled(4, 0.0)).cast<List<double>>();
      
      // Run inference
      _interpreter!.run(inputTensor, output);
      
      // Interpret results
      final predictions = output[0];
      final prediction = _interpretPredictions(predictions);
      
      // Store session data for future predictions
      _userBehaviorHistory.add(inputFeatures);
      if (_userBehaviorHistory.length > 100) {
        _userBehaviorHistory.removeAt(0); // Keep only recent history
      }
      await _saveUserBehaviorHistory();
      
      return prediction;
      
    } catch (e) {
      print('Error during prediction: $e');
      return null;
    }
  }

  List<double> _extractFeatures(UserSession session) {
    return [
      session.sessionDuration.inMinutes.toDouble(),
      session.screenViews.toDouble(),
      session.buttonClicks.toDouble(),
      session.scrollDistance.toDouble(),
      session.timeOfDay.toDouble(),
      session.dayOfWeek.toDouble(),
      session.appVersion.hashCode.toDouble(),
      session.deviceType.index.toDouble(),
      _calculateHistoricalEngagement(),
      _calculateSessionFrequency(),
    ];
  }

  double _calculateHistoricalEngagement() {
    if (_userBehaviorHistory.isEmpty) return 0.0;
    
    // Calculate average engagement from historical data
    final engagementScores = _userBehaviorHistory.map((behavior) {
      return behavior[0] * 0.3 + behavior[1] * 0.2 + behavior[2] * 0.5;
    }).toList();
    
    return engagementScores.reduce((a, b) => a + b) / engagementScores.length;
  }

  double _calculateSessionFrequency() {
    if (_userBehaviorHistory.length < 2) return 0.0;
    
    // Calculate average time between sessions
    return _userBehaviorHistory.length.toDouble() / 7.0; // Sessions per week
  }

  UserBehaviorPrediction _interpretPredictions(List<double> predictions) {
    final churnProbability = predictions[0];
    final engagementScore = predictions[1];
    final nextActionProbability = predictions[2];
    final lifetimeValuePrediction = predictions[3];
    
    return UserBehaviorPrediction(
      churnProbability: churnProbability,
      engagementScore: engagementScore,
      nextActionProbability: nextActionProbability,
      predictedLifetimeValue: lifetimeValuePrediction,
      riskLevel: _calculateRiskLevel(churnProbability),
      recommendations: _generateRecommendations(churnProbability, engagementScore),
    );
  }

  RiskLevel _calculateRiskLevel(double churnProbability) {
    if (churnProbability > 0.7) return RiskLevel.high;
    if (churnProbability > 0.4) return RiskLevel.medium;
    return RiskLevel.low;
  }

  List<String> _generateRecommendations(double churnProbability, double engagementScore) {
    List<String> recommendations = [];
    
    if (churnProbability > 0.6) {
      recommendations.add('Send personalized retention offer');
      recommendations.add('Provide premium feature trial');
    }
    
    if (engagementScore < 0.3) {
      recommendations.add('Show onboarding tutorial');
      recommendations.add('Highlight unused features');
    }
    
    if (recommendations.isEmpty) {
      recommendations.add('Continue current user experience');
    }
    
    return recommendations;
  }

  Future<BatchPredictionResult> predictBatchBehavior(List<UserSession> sessions) async {
    if (!_isModelLoaded || _interpreter == null) {
      return BatchPredictionResult(predictions: [], errors: ['Model not loaded']);
    }

    List<UserBehaviorPrediction> predictions = [];
    List<String> errors = [];

    for (int i = 0; i < sessions.length; i++) {
      try {
        final prediction = await predictUserBehavior(sessions[i]);
        if (prediction != null) {
          predictions.add(prediction);
        } else {
          errors.add('Failed to predict behavior for session $i');
        }
      } catch (e) {
        errors.add('Error predicting session $i: $e');
      }
    }

    return BatchPredictionResult(predictions: predictions, errors: errors);
  }

  void dispose() {
    _interpreter?.close();
    _isModelLoaded = false;
  }
}

class UserSession {
  final Duration sessionDuration;
  final int screenViews;
  final int buttonClicks;
  final double scrollDistance;
  final double timeOfDay; // Hour