content: package com.mlchatbot.ml;
  
  import android.content.Context;
  import android.util.Log;
  import org.tensorflow.lite.Interpreter;
  import java.io.File;
  import java.io.FileInputStream;
  import java.io.IOException;
  import java.nio.MappedByteBuffer;
  import java.nio.channels.FileChannel;
  import java.util.concurrent.ExecutorService;
  import java.util.concurrent.Executors;
  
  public class LocalModel {
      private static final String TAG = "LocalModel";
      private static final String MODEL_FILE = "chatbot_model.tflite";
      
      private final Context context;
      private final ExecutorService executor;
      private Interpreter tflite;
      private ModelTrainer trainer;
      
      public interface ResponseCallback {
          void onResponse(String response);
      }
  
      public LocalModel(Context context) {
          this.context = context;
          this.executor = Executors.newSingleThreadExecutor();
          initializeModel();
          this.trainer = new ModelTrainer(context);
      }
  
      private void initializeModel() {
          try {
              MappedByteBuffer modelBuffer = loadModelFile();
              tflite = new Interpreter(modelBuffer);
              Log.i(TAG, "TFLite model loaded successfully");
          } catch (IOException e) {
              Log.e(TAG, "Error loading TFLite model", e);
          }
      }
  
      private MappedByteBuffer loadModelFile() throws IOException {
          String modelPath = new File(context.getFilesDir(), MODEL_FILE).getAbsolutePath();
          try (FileInputStream inputStream = new FileInputStream(modelPath)) {
              FileChannel fileChannel = inputStream.getChannel();
              return fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, fileChannel.size());
          }
      }
  
      public void generateResponse(String userMessage, ResponseCallback callback) {
          executor.execute(() -> {
              try {
                  // Preprocess input
                  float[] inputVector = preprocessInput(userMessage);
                  
                  // Run inference
                  float[] outputVector = new float[1024]; // Adjust size based on your model
                  tflite.run(inputVector, outputVector);
                  
                  // Post-process output
                  String response = postprocessOutput(outputVector);
                  
                  // Update model with interaction
                  trainer.updateModel(userMessage, response);
                  
                  // Deliver response on main thread
                  android.os.Handler mainHandler = new android.os.Handler(context.getMainLooper());
                  mainHandler.post(() -> callback.onResponse(response));
                  
              } catch (Exception e) {
                  Log.e(TAG, "Error generating response", e);
                  android.os.Handler mainHandler = new android.os.Handler(context.getMainLooper());
                  mainHandler.post(() -> callback.onResponse("I'm sorry, I encountered an error. Please try again."));
              }
          });
      }
  
      private float[] preprocessInput(String input) {
          // TODO: Implement text preprocessing
          // Convert text to vector using tokenization and embedding
          return new float[512]; // Placeholder
      }
  
      private String postprocessOutput(float[] output) {
          // TODO: Implement output processing
          // Convert output vector to human-readable text
          return "This is a placeholder response. The actual model will generate meaningful responses.";
      }
  
      public void updateModelWithFederatedLearning(byte[] modelUpdate) {
          executor.execute(() -> {
              try {
                  trainer.applyFederatedUpdate(modelUpdate);
                  Log.i(TAG, "Applied federated learning update successfully");
              } catch (Exception e) {
                  Log.e(TAG, "Error applying federated learning update", e);
              }
          });
      }
  
      public void close() {
          if (tflite != null) {
              tflite.close();
              tflite = null;
          }
          executor.shutdown();
      }
  }
  