Créez facilement du HTML à partir de vos designs grâce à GPT 3.5/4 et l'OCR

Table of Contents:

1. Introduction
2. Overview of the Application
3. How the Application Works
4. Gathering the Input Image
5. Using OCR to Extract Text from the Image
6. Custom Prompt Definition
7. HTML Generation Process
8. Setting up the Environment
9. Uploading the Image
10. Running the Application
11. Conclusion

Introduction

Welcome to the AI Anytime Channel. In this video, we will be developing an application that can generate code and HTML pages directly from a design. Whether You have a HAND-drawn image or a digital design, this application will help you generate the corresponding HTML code and even run it through a Streamlit component. We will be utilizing a large language model and Optical Character Recognition (OCR) techniques to achieve this.

Overview of the Application

The application we will be building in this video is designed to take an image as input, extract the text from the image using OCR techniques, and generate the corresponding HTML code using a large language model. We will then run the HTML code through a Streamlit component to Visualize the design.

How the Application Works

The application follows a step-by-step process to generate code and HTML pages from a design image. Here's a breakdown of the process:

1. Gathering the Input Image:

   • The user uploads an image containing the design elements they want to convert into HTML code.

2. Using OCR to Extract Text from the Image:

   • The application utilizes an OCR engine to detect and recognize the text present in the image.
   • The OCR engine extracts the text and provides the corresponding bounding boxes.

3. Custom Prompt Definition:

   • A custom prompt template is defined to guide the interaction with the large language model.
   • The prompt template provides instructions and Context for generating the HTML code.

4. HTML Generation Process:

   • The extracted text and the prompt template are used as input to the large language model.
   • The language model generates the HTML code Based on the provided instructions and the design elements.

5. Setting up the Environment:

   • The necessary libraries and dependencies are imported, including the OCR library and the Streamlit components.

6. Uploading the Image:

   • The application provides an interface for the user to upload their design image.
   • The uploaded image is saved and passed as input to the OCR engine for text extraction.

7. Running the Application:

   • Once the image is uploaded, the user can click a "Run" button to initiate the code generation process.
   • The application executes the OCR function to extract the text from the image.
   • The extracted text, along with the custom prompt template, is used to generate the HTML code using the large language model.
   • The generated HTML code is displayed in a Streamlit component, allowing the user to visualize the design.

Gathering the Input Image

To start the process, the user needs to upload the design image they want to convert into HTML code. The application provides a "Upload Your Design" button, allowing the user to select the image file from their local storage. The selected image is then passed as input to the OCR engine for further processing.

Using OCR to Extract Text from the Image

The application utilizes an OCR engine (specifically, the Ego CR library in Python) to extract text from the uploaded image. The OCR engine scans the image and identifies the text elements present within it. It then provides the extracted text along with their corresponding bounding boxes.

Custom Prompt Definition

To guide the large language model in generating the HTML code, a custom prompt template is defined. The template provides a structured format for the model to understand the task at hand and generate accurate results. The prompt includes details such as the purpose of the application, the desired output (HTML code), and any specific instructions related to the design elements.

HTML Generation Process

The HTML generation process involves feeding the extracted text and the custom prompt template to the large language model. In this case, the model being used is GPT 3.5 Turbo, but it is recommended to utilize GPT 4 for better results. The model generates the HTML code based on the provided instructions and the design elements extracted from the image. The generated code reflects the layout and structure of the design.

Setting up the Environment

Before running the application, the environment needs to be properly set up. This includes importing the required libraries and dependencies, such as the OCR library (Ego CR), the Streamlit components, and the language model (GPT 3.5 Turbo or GPT 4).

Uploading the Image

The user interface of the application includes an option to upload the design image. Once the user selects the image file, it is saved and passed as input to the OCR function. The OCR engine then extracts the text from the image, which will be used in the HTML generation process.

Running the Application

Once the design image is uploaded and the OCR function is executed, the user can click the "Run" button to initiate the code generation process. The application utilizes the extracted text and the custom prompt template to generate the HTML code using the large language model. The generated HTML code is displayed in a Streamlit component, allowing the user to visualize the design and obtain the corresponding code.

Conclusion

In this video, we have built an application that can generate code and HTML pages directly from a design image. By utilizing OCR techniques and a large language model, we can extract text from the image and generate accurate HTML code based on the design elements. The application also provides a visual representation of the generated design using a Streamlit component. This application can be further extended and customized to meet specific design requirements.

Highlights:

• The application allows users to generate code and HTML pages directly from a design image.
• Optical Character Recognition (OCR) techniques are used to extract text from the image.
• A large language model, GPT 3.5 Turbo, is utilized to generate the HTML code based on the extracted text and a custom prompt template.
• The Streamlit framework is used to Create a user-friendly interface for uploading the design image and visualizing the generated design.
• The application can be customized and extended to meet specific design requirements.

FAQs:

Q: What is the purpose of this application? A: The application aims to simplify the process of converting a design image into HTML code. It utilizes OCR techniques and a large language model to extract text from the image and generate accurate HTML code based on the design elements.

Q: Which OCR engine is used in this application? A: The application uses the Ego CR library, an open-source OCR library in Python, to extract text from the uploaded design image.

Q: Can I use any Type of design image with this application? A: Yes, the application can process both hand-drawn and digital design images. It utilizes OCR techniques to extract the text from the image, regardless of the design style.

Q: Is it necessary to have a large language model to generate the HTML code? A: While it is possible to generate HTML code using smaller language models or even open-source models, a larger language model like GPT 3.5 Turbo or GPT 4 is recommended for more accurate and reliable results.

Q: Can I customize the prompt template for the language model? A: Yes, the custom prompt template provides instructions and context for the language model to generate the HTML code. You can modify the template to suit your specific design requirements and desired output.

Q: Can I download the generated HTML code? A: Currently, the application does not provide a download option for the generated HTML code. However, you can manually copy the code displayed in the Streamlit component and save it as an HTML file on your local device.