content: package com.mlchatbot.ml;
  
  import android.content.Context;
  import android.util.Log;
  import java.io.File;
  import java.io.FileOutputStream;
  import java.io.IOException;
  import java.util.concurrent.locks.ReentrantLock;
  import java.util.ArrayList;
  import java.util.List;
  
  public class ModelTrainer {
      private static final String TAG = "ModelTrainer";
      private static final String MODEL_FILE = "chatbot_model.tflite";
      private static final int MAX_HISTORY_SIZE = 1000;
      
      private final Context context;
      private final ReentrantLock trainingLock;
      private final List<TrainingExample> trainingHistory;
      private float learningRate;
      private int batchSize;
  
      private static class TrainingExample {
          final String input;
          final String output;
          float reward;
  
          TrainingExample(String input, String output) {
              this.input = input;
              this.output = output;
              this.reward = 0.0f;
          }
      }
  
      public ModelTrainer(Context context) {
          this.context = context;
          this.trainingLock = new ReentrantLock();
          this.trainingHistory = new ArrayList<>();
          this.learningRate = 0.001f;
          this.batchSize = 32;
      }
  
      public void updateModel(String input, String output) {
          trainingLock.lock();
          try {
              // Add new example to training history
              TrainingExample example = new TrainingExample(input, output);
              addToTrainingHistory(example);
  
              // Perform local training if we have enough examples
              if (trainingHistory.size() >= batchSize) {
                  performLocalTraining();
              }
          } finally {
              trainingLock.unlock();
          }
      }
  
      private void addToTrainingHistory(TrainingExample example) {
          trainingHistory.add(example);
          if (trainingHistory.size() > MAX_HISTORY_SIZE) {
              trainingHistory.remove(0);
          }
      }
  
      private void performLocalTraining() {
          try {
              // Select batch for training
              List<TrainingExample> batch = selectTrainingBatch();
              
              // Perform reinforcement learning update
              updateModelWeights(batch);
              
              // Clear processed examples
              batch.clear();
              
              Log.i(TAG, "Local training iteration completed successfully");
          } catch (Exception e) {
              Log.e(TAG, "Error during local training", e);
          }
      }
  
      private List<TrainingExample> selectTrainingBatch() {
          List<TrainingExample> batch = new ArrayList<>();
          int historySize = trainingHistory.size();
          
          // Select random examples for the batch
          for (int i = 0; i < Math.min(batchSize, historySize); i++) {
              int randomIndex = (int) (Math.random() * historySize);
              batch.add(trainingHistory.get(randomIndex));
          }
          
          return batch;
      }
  
      private void updateModelWeights(List<TrainingExample> batch) {
          // TODO: Implement actual weight updates using reinforcement learning
          // This would involve:
          // 1. Computing gradients based on rewards
          // 2. Applying updates with the learning rate
          // 3. Updating the model parameters
      }
  
      public void applyFederatedUpdate(byte[] modelUpdate) {
          trainingLock.lock();
          try {
              // Verify update integrity
              if (!verifyUpdateIntegrity(modelUpdate)) {
                  throw new IllegalArgumentException("Invalid model update received");
              }
  
              // Apply the federated update
              applyModelUpdate(modelUpdate);
  
              // Save updated model
              saveModel(modelUpdate);
  
              Log.i(TAG, "Federated update applied successfully");
          } catch (Exception e) {
              Log.e(TAG, "Error applying federated update", e);
              throw new RuntimeException("Failed to apply federated update", e);
          } finally {
              trainingLock.unlock();
          }
      }
  
      private boolean verifyUpdateIntegrity(byte[] modelUpdate) {
          // TODO: Implement update verification
          // Check update format, size, and signature
          return modelUpdate != null && modelUpdate.length > 0;
      }
  
      private void applyModelUpdate(byte[] modelUpdate) {
          // TODO: Implement actual model update application
          // This would involve:
          // 1. Deserializing the update
          // 2. Averaging with current weights
          // 3. Applying the merged weights
      }
  
      private void saveModel(byte[] modelUpdate) throws IOException {
          File modelFile = new File(context.getFilesDir(), MODEL_FILE);
          try (FileOutputStream fos = new FileOutputStream(modelFile)) {
              fos.write(modelUpdate);
              fos.flush();
          }
      }
  
      public void setLearningRate(float learningRate) {
          this.learningRate = learningRate;
      }
  
      public void setBatchSize(int batchSize) {
          this.batchSize = batchSize;
      }
  
      public float getLearningRate() {
          return learningRate;
      }
  
      public int getBatchSize() {
          return batchSize;
      }
  }
  