Welcome to the future of web development where your hand-drawn designs can magically transform into working HTML code! SketchCode leverages deep learning to convert your wireframes into real, functional code. This blog will guide you through the setup process, usage examples, and troubleshooting tips for using SketchCode effectively.

What is SketchCode?

SketchCode is a deep learning model designed to take hand-drawn web mockups and convert them into HTML code. It utilizes an image captioning architecture to interpret these sketches and generate suitable HTML markup.

Setup Instructions

Before you dive into the world of converting sketches to code, you need to ensure you have the proper setup:

• Prerequisites:
  • Python 3 (not compatible with Python 2)
  • pip (Python package installer)
• Install Dependencies:
  • Run the following command in your terminal:

    pip install -r requirements.txt

Example Usage

Now, onto the fun part! Here’s how to use SketchCode to convert your hand-drawn images to HTML.

Downloading Data and Pretrained Weights

First, download the data and pretrained weights needed for the conversion:

• Clone the repository:

  git clone https://github.com/ashnkumarsketch-code.git

• Navigate to the scripts directory:

  cd sketch-code/scripts

• Get the data and pretrained weights:

  sh get_data.sh
  sh get_pretrained_model.sh

Converting a Hand-Drawn Image Into HTML Code

Use the pretrained model to convert a single drawn image into HTML code

• Run the command:

  cd src
  python convert_single_image.py --png_path ../examples/drawn_example1.png --output_folder ./generated_html --model_json_file ../bin/model_json.json --model_weights_file ../bin/weights.h5

General Usage

You can also convert multiple images in a folder, as described below:

• For a batch conversion:

  python convert_batch_of_images.py --pngs_path pathto/folderwith/pngs --output_folder folderto/output/html --model_json_file pathtomodel/json/file.json --model_weights_file pathtomodel/weights.h5

Training the Model

If you want to train the model from scratch, run the following command:

python train.py --data_input_path pathtofolderwith/pngs --validation_split 0.2 --epochs 10 --model_output_path pathto/output/model --augment_training_data 1

Evaluating Predictions

Once you’ve generated predictions, you can evaluate them using the BLEU score:

python evaluate_single_gui.py --original_gui_filepath pathtooriginal/guis/file --predicted_gui_filepath pathtopredicted/guis/file

Troubleshooting Tips

If you encounter issues while using SketchCode, here are some troubleshooting ideas:

• Ensure you have the correct version of Python installed. SketchCode is compatible only with Python 3.
• Verify that all dependencies are correctly installed using pip.
• Double-check the paths provided in your commands to make sure they are accurate.
• If you experience performance issues, consider adjusting your training parameters, such as epochs and validation splits.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By utilizing SketchCode, you can seamlessly convert hand-drawn wireframes into functional HTML code. Remember, this project is a proof of concept and may require wireframes to closely resemble the training dataset for optimal performance. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.