Have you ever wished you could turn a screenshot of a website into actual HTML and CSS code? Thanks to the revolutionary model developed by Hugging Face, this process is not only a dream but now a reality! In this article, we’ll explore how to use this model to convert screenshots into usable code, step by step.

What You Need

• Python installed on your machine.
• Access to the Hugging Face model HuggingFace M4.
• The necessary libraries: torch, PIL (Pillow), and transformers.
• Your API token from Hugging Face.

Step-by-Step Setup

Let’s break down the code needed for this transformation. You can think of the overall process like preparing a delicious meal. You gather your ingredients (the code), follow a recipe (the functions and methods), and voila! You have your beautiful plate of food (the HTML/CSS output).

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor

DEVICE = torch.device('cuda')

PROCESSOR = AutoProcessor.from_pretrained('HuggingFaceM4VLM_WebSight_finetuned', token=API_TOKEN)
MODEL = AutoModelForCausalLM.from_pretrained('HuggingFaceM4VLM_WebSight_finetuned', token=API_TOKEN, trust_remote_code=True, torch_dtype=torch.bfloat16).to(DEVICE)

def convert_to_rgb(image):
    if image.mode == 'RGB':
        return image
    image_rgba = image.convert('RGBA')
    background = Image.new('RGBA', image_rgba.size, (255, 255, 255))
    alpha_composite = Image.alpha_composite(background, image_rgba)
    return alpha_composite.convert('RGB')

# Usage of functions continues...

Understanding the Code

The above code serves as our culinary recipe:

• Ingredients: We import necessary libraries like torch and PIL (like gathering our spices and vegetables).
• Device Selection: The line DEVICE = torch.device('cuda') checks if we can use a GPU for faster processing (like choosing the right cooking surface).
• Model Initialization: Here we load the model and processor with the provided API token, analogous to preheating your oven.
• Image Conversion: The convert_to_rgb function ensures our images are in the correct format—a crucial step just like ensuring our ingredients are prepped (washed and chopped).

Generating HTML/CSS Code

Now that we have our setup ready, we will continue our recipe to generate the HTML/CSS code:

inputs = PROCESSOR.tokenizer('BOS_TOKEN fake_token_around_image image', return_tensors='pt', add_special_tokens=False)
inputs['pixel_values'] = PROCESSOR.image_processor([image], transform=custom_transform)

inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
generated_ids = MODEL.generate(**inputs, bad_words_ids=BAD_WORDS_IDS, max_length=4096)
generated_text = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)

Troubleshooting

Like any cooking endeavor, things might go awry. Here are some troubleshooting ideas:

• Model does not load: Ensure you are using the correct API token and the model path is accurate.
• Image issues: Make sure your input image is in a compatible format.
• Performance lag: If the model is running slow, check if your device supports CUDA and that you have enough resources available.

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Conclusion

And there you have it! With these instructions, you can now convert website screenshots into HTML/CSS code. 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.