Create Image Captioning Web App with Wit to Digital GPT-2

Create Image Captioning Web App with Wit to Digital GPT-2

Table of Contents:

1. Introduction
2. Overview of the Image Captioning Model
3. Steps to Use the Model for a Web Application 3.1. Installing Required Libraries 3.2. Importing Dependencies 3.3. Downloading Models and Image 3.4. Processing the Image 3.5. Generating the Caption
4. Handling Biases in the Model
5. Converting the Script into a Gradio Web Application
6. Deploying the Application on Hugging Face Spaces
7. Testing the Application
8. Conclusion
9. FAQs

Introduction

Welcome to One Little Coder! In this article, we will explore a new image captioning model released by Sachin. We will learn how to use this model to Create a simple web application using Radio. By uploading an image, You will receive a caption as an output. We will also discuss the biases that may exist in the model and ways to handle them.

Overview of the Image Captioning Model

The image captioning model we will be using is called Wit to Digital GPT-2. It utilizes Vision Encoder-Decoder models and pre-trained tokenizers to extract text from the image and generate a caption. The code for this model has been slightly modified to improve readability and cleanliness.

Steps to Use the Model for a Web Application

To create a web application with the image captioning model, we will follow these steps:

1. Install the required libraries
2. Import the necessary dependencies
3. Download the models and image
4. Process the image using the feature extractor
5. Generate the caption using the tokenizer

Handling Biases in the Model

When using image captioning models, biases may exist in the generated text. In this article, we will address the possibility of biases and discuss ways to handle them. We have performed stress testing on the model using various images, but biases may still exist. We encourage users to flag any biased outputs they encounter.

Converting the Script into a Gradio Web Application

To convert the script into a Gradio web application, we will create an inference function that takes an image as input. We will then use Gradio's input and output sections to allow users to upload an image and display the caption.

Deploying the Application on Hugging Face Spaces

Once the application is ready, you can deploy it on Hugging Face Spaces. Create a new space, upload the app.py file, and specify the necessary details like the title, description, and examples. The application will be built and can be accessed through the provided link.

Testing the Application

You can test the deployed application by uploading different images and checking the generated Captions. You may identify any biases in the output and flag them accordingly.

Conclusion

In conclusion, we have successfully built an image captioning solution using the Wit to Digital GPT-2 model. By following the steps outlined in this article, you can create your own web application and generate captions for uploaded images. While using the model, it is important to consider biases and address them appropriately for a fair representation of the captions.

FAQs

Q: How accurate is the generated caption? A: The accuracy of the generated caption may vary based on the input image. The model provides decent results, but it may not always be perfect.

Q: How can I handle biases in the model? A: If you encounter biased captions, you can flag the image using the provided button in the application. This helps highlight biases to the model's creator and allows improvements to be made.

Q: Can I change the examples provided in the application? A: Yes, you can add or modify examples by uploading images of your choice. Just make sure the examples are in the required format.

Q: How can I deploy the application on Hugging Face Spaces? A: Follow the steps outlined in the article to upload the app.py file and specify the necessary details. The application will be built and ready to use through the provided link.