Build Your Own Vision Model

// Simplified CLIP implementation in JavaScript
class SimpleCLIP {
  constructor() {
    this.textEmbeddings = new Map();
    this.imageEmbeddings = new Map();
    
    // Pre-computed embeddings (in real CLIP, these come from neural networks)
    this.textEmbeddings.set('cat', [0.8, -0.2, 0.5, 0.1]);
    this.textEmbeddings.set('dog', [0.7, -0.1, 0.4, 0.2]);
    this.textEmbeddings.set('car', [-0.3, 0.9, -0.2, 0.6]);
    this.textEmbeddings.set('sunset', [0.1, 0.3, 0.8, -0.4]);
  }
  
  // Calculate cosine similarity between two vectors
  similarity(vec1, vec2) {
    const dot = vec1.reduce((sum, a, i) => sum + a * vec2[i], 0);
    const mag1 = Math.sqrt(vec1.reduce((sum, a) => sum + a * a, 0));
    const mag2 = Math.sqrt(vec2.reduce((sum, a) => sum + a * a, 0));
    return dot / (mag1 * mag2);
  }
  
  // Search images by text
  searchImages(textQuery) {
    const queryEmbedding = this.textEmbeddings.get(textQuery.toLowerCase());
    if (!queryEmbedding) return [];
    
    const results = [];
    for (const [image, embedding] of this.imageEmbeddings) {
      const score = this.similarity(queryEmbedding, embedding);
      results.push({ image, score });
    }
    
    return results.sort((a, b) => b.score - a.score);
  }
}

// Simplified vision-language model
class VisionLanguageModel {
  constructor() {
    this.visionEncoder = new VisionTransformer();
    this.projectionLayer = new LinearProjection(768, 4096); // Vision → Text dims
    this.languageModel = new GPT();
  }
  
  async generate(image, textPrompt) {
    // 1. Encode the image
    const imagePatches = this.preprocessImage(image); // 224x224 → 8x8 patches
    const imageEmbeddings = this.visionEncoder.encode(imagePatches); // [64, 768]
    
    // 2. Project to language model dimensions
    const projectedEmbeddings = this.projectionLayer.forward(imageEmbeddings); // [64, 4096]
    
    // 3. Tokenize text prompt
    const textTokens = this.tokenize(textPrompt); // "What do you see?" → [1234, 567, 890]
    const textEmbeddings = this.languageModel.embed(textTokens); // [3, 4096]
    
    // 4. Concatenate image + text embeddings
    const combinedInput = [...projectedEmbeddings, ...textEmbeddings]; // [67, 4096]
    
    // 5. Generate response
    const response = this.languageModel.generate(combinedInput);
    return this.decode(response);
  }
}

// Usage
const model = new VisionLanguageModel();
const response = await model.generate(catImage, "What do you see?");
console.log(response); // "I see a orange cat sitting on a blue mat..."

// Complete vision pipeline
async function analyzeImage(imageFile, question = "What do you see?") {
  // 1. Convert image to base64
  const base64Image = await fileToBase64(imageFile);
  
  // 2. Call vision API (OpenAI GPT-4V example)
  const response = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${API_KEY}`
    },
    body: JSON.stringify({
      model: 'gpt-4-vision-preview',
      messages: [{
        role: 'user',
        content: [
          { type: 'text', text: question },
          { 
            type: 'image_url', 
            image_url: { url: `data:image/jpeg;base64,${base64Image}` }
          }
        ]
      }],
      max_tokens: 500
    })
  });
  
  const data = await response.json();
  return data.choices[0].message.content;
}

// Helper function
function fileToBase64(file) {
  return new Promise((resolve, reject) => {
    const reader = new FileReader();
    reader.readAsDataURL(file);
    reader.onload = () => {
      const base64 = reader.result.split(',')[1];
      resolve(base64);
    };
    reader.onerror = error => reject(error);
  });
}

// Usage
const imageFile = document.getElementById('image-input').files[0];
const description = await analyzeImage(imageFile, "Describe this image in detail");
console.log(description);

// Fallback vision simulator
const visionSimulator = {
  responses: {
    'cat': 'I can see an orange tabby cat sitting comfortably. The cat has bright green eyes and distinctive stripes. It appears to be resting on what looks like a soft surface.',
    'dog': 'This image shows a friendly-looking dog, possibly a Golden Retriever. The dog has a warm golden coat and appears to be smiling or panting happily.',
    'car': 'I can see a car in the image. It appears to be a modern sedan with a sleek design and clean lines.',
    'landscape': 'This is a beautiful landscape photo showing natural scenery. I can see open space with natural lighting.',
    'default': 'I can see an interesting image with various elements. The composition includes different colors and shapes that create an engaging visual.'
  },
  
  analyze(imageData) {
    // In a real implementation, this would use computer vision
    // For demo purposes, we'll return a realistic response
    return this.responses.default;
  }
};