Vgpt: LLM Powered by Azure OpenAI with OCR Capabilities

Overview

In the rapidly evolving landscape of AI applications, I’ve developed VGPT (accessible at chat.visionml.tech), a specialized language model that combines the power of Azure OpenAI’s GPT-3.5 with Optical Character Recognition (OCR) capabilities through Azure Vision. Built with React, TypeScript, and Vite, VGPT represents a significant step forward in creating more versatile and accessible AI tools.

Technical Architecture

VGPT’s architecture consists of three main components:

1. Frontend Interface: Built with React, TypeScript, and Vite
2. Language Model: Powered by Azure OpenAI’s GPT-3.5
3. OCR Engine: Implemented using Azure Vision services

This architecture enables VGPT to not only process and generate text like traditional LLMs but also to “see” and interpret text from images, making it a truly multimodal AI system.

The Power of OCR Integration

The integration of OCR capabilities sets VGPT apart from standard language models. By leveraging Azure Vision’s advanced OCR technology, VGPT can:

• Extract text from uploaded images
• Process screenshots containing text
• Analyze documents and diagrams
• Interpret handwritten notes (with reasonable accuracy)

This functionality opens up numerous use cases that would be impossible with text-only models, from automating data entry to assisting visually impaired users.

Implementation Details

Frontend Development

The frontend was built using React with TypeScript for type safety and Vite for its exceptional development experience. Key implementation details include:

// Image upload component with preview functionality
const ImageUploader: React.FC = () => {
  const [image, setImage] = useState<File | null>(null);
  const [preview, setPreview] = useState<string | null>(null);
  
  const handleImageChange = (e: React.ChangeEvent<HTMLInputElement>) => {
    if (e.target.files && e.target.files[0]) {
      const selectedImage = e.target.files[0];
      setImage(selectedImage);
      
      // Create preview URL
      const reader = new FileReader();
      reader.onloadend = () => {
        setPreview(reader.result as string);
      };
      reader.readAsDataURL(selectedImage);
    }
  };
  
  const handleSubmit = async () => {
    if (!image) return;
    
    const formData = new FormData();
    formData.append('image', image);
    
    try {
      const response = await fetch('/api/process-image', {
        method: 'POST',
        body: formData,
      });
      
      // Process response...
    } catch (error) {
      console.error('Error processing image:', error);
    }
  };
  
  return (
    <div className="image-uploader">
      <input type="file" accept="image/*" onChange={handleImageChange} />
      {preview && <img src={preview} alt="Preview" className="image-preview" />}
      <button onClick={handleSubmit} disabled={!image}>
        Process Image
      </button>
    </div>
  );
};