@alanhelmick/memorable/src/core/inputProcessor.js

An AI memory system enabling personalized, context-aware interactions through advanced memory management and emotional intelligence

import { promises as fs } from 'fs';
import { logger } from '../utils/logger.js';
import * as tf from '@tensorflow/tfjs-node';
import { NLP } from 'node-nlp';
import NodeWebcam from 'node-webcam';
import { Microphone } from 'node-microphone';
import { promisify } from 'util';

export class InputProcessor {
  constructor() {
    this.nlp = new NLP({ language: 'en' });
    this.webcam = null;
    this.microphone = null;
    this.supportedTypes = ['text', 'vision', 'audio', 'video', 'file'];
  }

  async initialize() {
    try {
      // Initialize NLP
      await this.nlp.train();

      // Initialize webcam if enabled
      if (process.env.ENABLE_CAMERA === 'true') {
        this.webcam = NodeWebcam.create({
          width: 1280,
          height: 720,
          quality: 100,
          delay: 0,
          saveShots: true,
          output: 'jpeg',
          device: false,
          callbackReturn: 'buffer'
        });
        this.webcam.capture = promisify(this.webcam.capture);
      }

      // Initialize microphone if enabled
      if (process.env.ENABLE_MICROPHONE === 'true') {
        this.microphone = new Microphone();
      }

      logger.info('Input Processor initialized');
    } catch (error) {
      logger.error('Failed to initialize Input Processor:', error);
      throw error;
    }
  }

  async processInput(input, type) {
    try {
      if (!this.supportedTypes.includes(type)) {
        throw new Error(`Unsupported input type: ${type}`);
      }

      const processedInput = await this[`process${type.charAt(0).toUpperCase() + type.slice(1)}`](input);
      
      return {
        type,
        processed: processedInput,
        timestamp: Date.now(),
        metadata: this.extractMetadata(input, type)
      };
    } catch (error) {
      logger.error(`Failed to process ${type} input:`, error);
      throw error;
    }
  }

  async processText(text) {
    try {
      // Process text input using NLP
      const analysis = await this.nlp.process(text);
      
      return {
        original: text,
        tokens: analysis.tokens,
        sentiment: analysis.sentiment,
        entities: analysis.entities,
        intent: analysis.intent,
        language: analysis.language,
        embeddings: await this.generateTextEmbeddings(text)
      };
    } catch (error) {
      logger.error('Text processing failed:', error);
      throw error;
    }
  }

  async processVision(input) {
    try {
      let imageBuffer;
      
      if (input instanceof Buffer) {
        imageBuffer = input;
      } else if (typeof input === 'string') {
        // Check if input is a file path or base64
        if (input.startsWith('data:image')) {
          imageBuffer = Buffer.from(input.split(',')[1], 'base64');
        } else {
          imageBuffer = await fs.readFile(input);
        }
      } else if (input === 'capture' && this.webcam) {
        imageBuffer = await this.captureImage();
      } else {
        throw new Error('Invalid vision input format');
      }

      // Process image using TensorFlow.js
      const tensor = tf.node.decodeImage(imageBuffer);
      const processed = await this.processImageTensor(tensor);
      tensor.dispose();

      return processed;
    } catch (error) {
      logger.error('Vision processing failed:', error);
      throw error;
    }
  }

  async processAudio(input) {
    try {
      let audioBuffer;
      
      if (input instanceof Buffer) {
        audioBuffer = input;
      } else if (typeof input === 'string') {
        if (input === 'record' && this.microphone) {
          audioBuffer = await this.recordAudio();
        } else {
          audioBuffer = await fs.readFile(input);
        }
      } else {
        throw new Error('Invalid audio input format');
      }

      // Process audio data
      const features = await this.extractAudioFeatures(audioBuffer);
      
      return {
        features,
        duration: features.duration,
        sampleRate: features.sampleRate,
        embeddings: await this.generateAudioEmbeddings(features)
      };
    } catch (error) {
      logger.error('Audio processing failed:', error);
      throw error;
    }
  }

  async processVideo(input) {
    try {
      // Extract frames and audio from video
      const { frames, audio } = await this.extractVideoComponents(input);
      
      // Process each component
      const [processedFrames, processedAudio] = await Promise.all([
        Promise.all(frames.map(frame => this.processVision(frame))),
        this.processAudio(audio)
      ]);

      return {
        frames: processedFrames,
        audio: processedAudio,
        duration: processedAudio.duration,
        embeddings: await this.generateVideoEmbeddings(processedFrames, processedAudio)
      };
    } catch (error) {
      logger.error('Video processing failed:', error);
      throw error;
    }
  }

  async processFile(input) {
    try {
      const fileContent = await fs.readFile(input);
      const fileType = await this.detectFileType(fileContent);
      
      // Process based on detected file type
      return await this.processInput(fileContent, fileType);
    } catch (error) {
      logger.error('File processing failed:', error);
      throw error;
    }
  }

  async captureImage() {
    if (!this.webcam) {
      throw new Error('Camera is not enabled');
    }

    return await this.webcam.capture('capture');
  }

  async recordAudio(duration = 5000) {
    if (!this.microphone) {
      throw new Error('Microphone is not enabled');
    }

    return new Promise((resolve, reject) => {
      const chunks = [];
      const mic = this.microphone.startRecording();
      
      mic.on('data', chunk => chunks.push(chunk));
      
      setTimeout(() => {
        mic.stopRecording();
        resolve(Buffer.concat(chunks));
      }, duration);

      mic.on('error', reject);
    });
  }

  async processImageTensor(tensor) {
    // Implement image processing using TensorFlow.js
    // This could include object detection, face recognition, etc.
    return {
      shape: tensor.shape,
      // Add more processing results
    };
  }

  async extractAudioFeatures(buffer) {
    // Implement audio feature extraction
    // This could include spectral features, MFCC, etc.
    return {
      duration: 0,
      sampleRate: 0,
      // Add more features
    };
  }

  async extractVideoComponents(input) {
    // Implement video frame and audio extraction
    return {
      frames: [],
      audio: Buffer.from([])
    };
  }

  async generateTextEmbeddings(text) {
    // Implement text embedding generation
    return [];
  }

  async generateAudioEmbeddings(features) {
    // Implement audio embedding generation
    return [];
  }

  async generateVideoEmbeddings(frames, audio) {
    // Implement video embedding generation
    return [];
  }

  async detectFileType(buffer) {
    // Implement file type detection
    return 'text';
  }

  extractMetadata(input, type) {
    // Extract metadata based on input type
    return {
      timestamp: Date.now(),
      type,
      size: input instanceof Buffer ? input.length : 0,
      // Add more metadata
    };
  }

  async cleanup() {
    logger.info('Cleaning up Input Processor...');
    if (this.microphone) {
      this.microphone.stopRecording();
    }
    // Additional cleanup logic can be added here
  }
}