Create an Image Captioning App with Text-to-Image Generation

Table of Contents

1. Introduction
2. Project Overview
3. Components of the Application
   1. Jupiter Notebooks
   2. Hugging Face
   3. Runway ML
   4. Gradio
4. Architecture Breakdown
5. Code Walkthrough
6. Image Captioning Process
7. Image Generation Process
8. Application Demo
9. Conclusion
10. Contact Information

Introduction

In this article, we will explore how to build an image captioning application using Gradio. We will start with a quick project overview, followed by a breakdown of the components used. Then, we will delve into the architecture breakdown and take a step-by-step code walkthrough. Finally, we will demonstrate the application's capabilities. The image captioning and generation application will allow users to upload an image, generate a description of the image, and output a new image based on the generated description.

Project Overview

The project involves building an image captioning application with advanced capabilities. By leveraging Large Language Models and machine learning techniques, the application enables users to upload an image and receive a descriptive caption. Additionally, the application allows for text-to-image generation, where users can input a text description and obtain a corresponding image.

Components of the Application

Jupiter Notebooks

Jupiter Notebooks serve as the foundation for storing and organizing the application's code. These interactive computational environments provide a seamless platform for developing and executing code, making them an ideal choice for this project.

Hugging Face

Hugging Face is a unique platform that focuses on artificial intelligence (AI) models and resources. It offers an extensive collection of language models that can be utilized for various machine learning tasks. For this project, we will leverage the Salesforce BLiP Image Captioning Base model for image-to-text conversion. Additionally, we will use the Runway ML Stable Diffusion V1-5 model for text-to-image generation.

Hugging Face provides a model card for each machine learning model, offering essential information about the model's architecture and capabilities. Users can test these models on the Hugging Face website to understand their functionality.

Runway ML

Runway ML plays a crucial role in our application by enabling text-to-image generation. By integrating this powerful tool, we can convert text descriptions into corresponding images. This further enhances the application's functionality and provides users with a comprehensive experience.

Gradio

Gradio plays a pivotal role in creating the application's interactive user interface. It empowers users to upload images through a web interface and obtain Captions and generated images seamlessly. Gradio serves as a critical bridge between the user and the underlying machine learning models.

Architecture Breakdown

The application's architecture can be divided into several key components. First, users will create a Jupiter notebook using Anaconda, which will be displayed via the internet. Next, we retrieve the necessary language models from Hugging Face, allowing us to perform image captioning and text-to-image generation. Hugging Face serves as a dedicated platform for building, training, and deploying machine learning models within the AI community.

For this project, we utilize two different models: Salesforce BLiP Image Captioning Base for image-to-text conversion and Runway ML Stable Diffusion V1-5 for text-to-image generation. Both models have been carefully selected to provide accurate and efficient results.

Code Walkthrough

Let's now take a detailed walkthrough of the code, line by line, to gain a better understanding of the application's functionality.

1. Importing necessary libraries:
   • import os: Enables file and directory manipulation.
   • import io: Provides tools for handling data streams and memory.
   • from IPython.display import Image, display, HTML: Allows image display and HTML content embedding.
   • import base64: Encodes binary data into text.
2. Setting up environment variables:
   • from dotenv import load_dotenv, find_dotenv: Loads variables from a local .env file.
   • load_dotenv(find_dotenv()): Loads environment variables.
3. Importing libraries for making HTTP requests and handling JSON responses:
   • import requests: Interacts with web APIs and handles JSON responses.
4. Setting up API endpoints:
   • HF_API_TTI_BASE = os.environ.get('HF_API_TTI_BASE'): Retrieves the Runway ML Stable Diffusion V1-5 model's API endpoint.
   • TTI_ENDPOINT = os.environ.get('TTI_ENDPOINT'): Retrieves the Salesforce BLiP Image Captioning Base model's API endpoint.
5. Image-to-base64-STRING conversion function:
   • def image_to_base64_string(pil_image): Converts the image to a base64 encoded string.
6. Base64-string-to-PIL-image conversion function:
   • def base64_to_pil(b64_image): Decodes the base64 encoded image string to a PIL image object.
7. Captioning function:
   • def captioner(image): Converts the image to a base64 encoded string and calls the get_completion() function to generate a Caption using the TTI endpoint.
8. Text-to-image generation function:
   • def generate(Prompt): Calls the get_completion() function with the text prompt and the TTI endpoint. Converts the base64 encoded image from the API's response to a PIL image using the base64_to_pil() function.
9. Application interface creation:
   • def caption_and_generate(image): Creates a Gradio interface with an image uploaded component, a button to trigger the image captioning and generation process, and text and image components to display the results.

Image Captioning Process

The image captioning process involves uploading an image through the Gradio interface and clicking the "Caption and Generate" button. The application then generates a descriptive caption for the image, which is displayed alongside the generated image. The caption provides Relevant information about the subject and context of the uploaded image.

Image Generation Process

The image generation process allows users to input a descriptive text prompt instead of uploading an image. The Gradio interface provides text and image components for input and output, respectively. After providing the text prompt and clicking the button, the application utilizes the Runway ML Stable Diffusion V1-5 model to generate an image corresponding to the provided text description. The generated image is then displayed in the interface.

Application Demo

Now, let's see how the application works by running a demonstration. Double-clicking on the application, we can choose an image to upload. In this demo, we will use the famous "Mona Lisa" painting. After selecting the image, clicking the "Caption and Generate" button initiates the process. The application takes a few seconds to process the image and generate the results.

In our demo, the generated caption reads, "A picture of a painting of a woman with a smile" - a fitting description for the Mona Lisa herself. Additionally, a new generated image is displayed, showing a woman with a smile and a picture-like appearance. Remember, you can use any image you like, and the application will provide accurate captions and corresponding images accordingly.

Conclusion

In this article, we explored the process of building an image captioning application using Gradio. By leveraging Jupiter Notebooks, Hugging Face, Runway ML, and Gradio, we created a user-friendly interface that allows for image captioning and text-to-image generation. This application enables users to upload images or input text prompts and Instantly receive accurate captions and corresponding images.

Feel free to reach out by email at J singor Tech @ yahoo.com for any questions or further information about this project. Thank you for reading!

Contact Information

To contact the author or for further inquiries regarding this project, please use the following email address: J singor Tech @ yahoo.com.

FAQ

Q: How accurate are the generated captions? A: The accuracy of the generated captions depends on the performance of the underlying language models used for image captioning. The chosen models, Salesforce BLiP Image Captioning Base and Runway ML Stable Diffusion V1-5, have been trained on extensive datasets and are known for their high accuracy in image analysis and text generation.

Q: Can I use my own custom language models with this application? A: Yes, you can utilize your own custom language models with this application by integrating them into the codebase. However, it would require adapting the code to match the specific APIs and endpoints of your custom models.

Q: Are there any limitations when using large language models for image captioning and text-to-image generation? A: While large language models offer impressive capabilities, they can be resource-intensive and require substantial computing power. Additionally, generating high-quality images from textual prompts may not always produce the desired results as it heavily relies on the training data and the model's understanding of context.

Q: Can I modify the application to perform other image-related tasks? A: Yes, you can modify the application to perform other image-related tasks by incorporating additional machine learning models or techniques. The code provided offers a solid foundation to build upon and customize according to your specific requirements.