Image to DSL Model using ViT and Transformer Decoder

This repository contains the implementation of an Image to DSL (Domain Specific Language) model. The model uses a pre-trained Vision Transformer (ViT) as an encoder to extract image features and a custom Transformer Decoder to generate DSL code from the extracted features.

Overview

1. The ViT model is used to encode input images (screenshot of a user interface) into a sequence of feature vectors.
2. The custom Transformer Decoder is trained to generate a sequence of DSL tokens corresponding to the input image's structure.

Requirements

• Python 3.6 or higher
• PyTorch 1.8 or higher
• torchvision
• transformers
• PIL (Python Imaging Library)

Installation

Install the required packages:

pip install -r requirements.txt

Dataset

down the dataset by running the get_data.sh script:

bash get_data.sh

Training

To train the model, simply run the train.py script: The training script will train the model for the specified number of epochs and save the best model weights in the current directory as best_decoder.pth.

Inference

To generate DSL code from an input image, run generate.py script. The script will load the best model weights and generate DSL code for the input image.

Server as an API with integration of Chatbot GPT

To start the server, execute the app.py script. This script will load the optimal model weights and initiate a server on port 8080. The server provides a single endpoint /generate_code, which accepts an image and an OpenAI API key as input, and returns the generated HTML code.

Model Architecture

The Image to DSL model consists of the following components:

• Input Image: An image of a web UI that serves as input to the model.
• Preprocessing: The input image is preprocessed to prepare it for the ViT model.
• ViT Model: A pretrained Vision Transformer model is used to extract features from the preprocessed input image.
• GPT-2 Pre-trained Decoder: A pre-trained Transformer decoder takes image features and generates the corresponding DSL tokens.
• Output Tokens: The generated tokens are passed through a linear layer to produce the final output tokens.
• Predicted DSL Code: The output tokens are decoded back into the DSL code.