Build a Chat Website with Image Recognition using OpenAI Vision API

Build a Chat Website with Image Recognition using OpenAI Vision API

Table of Contents:

1. Introduction
2. Building a Website with Open AI's Vision Feature
3. Understanding the Basics of the Application
4. Uploading Images and Providing Instructions
5. Analyzing Images and Receiving Feedback
6. The New Vision Model: GPT-4 Vision Preview
7. Access to GPT-4 Vision Model for Developers
8. Billing and Tokens for Image Analysis
9. Model Change: Content Property as an Array
10. Using Base 64 Encoding for Image Uploads
11. Creating a Next.js Project and Setting Up the Front-End
12. Managing State in the Chat Container Component
13. Handling Image Changes and Image Removal
14. Managing the Message Input
15. Sending Messages and Constructing the Payload
16. Sending Payload to the Back-End Endpoint
17. Validating and Parsing Request Data
18. Making the Request to Open AI
19. Retrieving the Response Data
20. Updating the Front-End Display with the New Message

Building a Website with Open AI's Vision Feature

In this tutorial, You will learn how to build a website that utilizes Open AI's brand new vision feature. This feature allows you to chat with images using Open AI's API. If you're a developer Interested In AI, this tutorial is for you. We will be using Next.js to Create a simple one-page application where you can upload an image and receive feedback and analysis from Open AI's vision model. Let's dive in and explore the details of what we will be building.

Introduction

In this tutorial, we will be creating a website that leverages Open AI's vision feature. This feature allows users to chat with images using Open AI's API. We will be using Next.js to build a simple one-page application where users can upload an image and receive feedback and analysis from Open AI's vision model.

Understanding the Basics of the Application

Our application is a one-page application built using Next.js. The AI magic happens on the right side of the page, where users can upload an image and provide instructions. The uploaded image is analyzed by Open AI's vision model, and feedback is provided in response. The application provides a user-friendly interface for analyzing images and improving their quality.

Uploading Images and Providing Instructions

In our chat container, users can upload an image and provide instructions for improvement. By clicking on the PaperClip icon, users can select an image file to upload. They can then enter instructions, such as "Please provide feedback on how to improve my YouTube thumbnail for a higher click-through rate." The image and instructions are then sent to Open AI's vision model for analysis.

Analyzing Images and Receiving Feedback

After uploading an image and providing instructions, the application sends the data to Open AI's vision model for analysis. The user is shown loading animations to indicate that the model is processing the request. Once the analysis is complete, Open AI's model provides feedback on the uploaded image. The feedback includes suggestions on improving the image, such as making elements larger and bolder for better focus.

The New Vision Model: GPT-4 Vision Preview

Open AI's new vision model is called GPT-4 Vision Preview. It allows developers to submit images to the backend API for analysis. This model is available to all developers who have access to GPT-4 or GPT 3.5 turbo. Developers do not need to join a waitlist or meet any specific criteria to access the new vision model.

Access to GPT-4 Vision Model for Developers

Developers can access the GPT-4 vision model if they already have access to GPT-4 or GPT 3.5 turbo. There are no additional prerequisites or restrictions for accessing the vision model. Developers can start using it right away to analyze images and receive feedback.

Billing and Tokens for Image Analysis

The cost of using Open AI's vision model is Based on tokens. Tokens are correlated to the size and Detail of the images being analyzed. Larger and more detailed images require more tokens, resulting in higher charges. However, the cost is generally affordable, especially for images of moderate size. Developers can monitor their token usage and manage their billing accordingly.

Pros:

• Accessible to developers who already have access to GPT-4 or GPT 3.5 turbo
• No waitlist or access restrictions for the vision model
• Affordable pricing based on token usage
• Reliable analysis and feedback on uploaded images

Cons:

• Higher charges for larger or more detailed images

Model Change: Content Property as an Array

A significant change in the new model is the structure of the content property. In previous versions, content would be a STRING. However, with the new vision model, content can be an array. This array can contain both text and image objects. Developers can pass Prompts and multiple images in the array to provide Context and input for the analysis. This change allows for more flexibility and comprehensive image analysis.

Using Base 64 Encoding for Image Uploads

To upload images to Open AI's vision model, developers have the option to use image URLs or base 64 encoding. While the documentation uses URLs, developers can also upload their own images by converting them to base 64 format. The application in this tutorial enables uploading and encoding images as base 64 strings, providing a seamless user experience.

Creating a Next.js Project and Setting Up the Front-End

To start building the application, we will create a Next.js project. Next.js is a framework for building React applications that provides server-side rendering and other useful features. We will use the project to set up the front-end components, including the chat container and the logic for uploading images and displaying messages.

Managing State in the Chat Container Component

The chat container component is responsible for managing the state of the application. It keeps track of the images uploaded, the message input, the messages displayed, and whether a request is being sent to Open AI. These states play a crucial role in updating the user interface and processing the image analysis.

Handling Image Changes and Image Removal

When users select an image to upload, the application handles the image change event. The selected image is converted into an array and added to the list of uploaded images. The application limits the number of images to five for a better user experience. Additionally, users can remove an uploaded image by clicking on the "X" button next to it.

Managing the Message Input

The message input functionality allows users to Type their instructions or prompts for image analysis. The application captures the text input and updates the message state accordingly. This ensures that the input message is incorporated in the payload sent to Open AI for analysis.

Sending Messages and Constructing the Payload

When users click on the send button, the application triggers the send message function. This function prepares the payload for sending to Open AI's backend API. It merges the messages and images, constructs the payload with the necessary structure, and updates the front-end messages to provide Instant feedback to the user.

Sending Payload to the Back-End Endpoint

Once the payload is constructed, the application sends it to the back-end endpoint. The endpoint receives the request data, validates it using schemas, and parses it for further processing. If the request data is invalid, an error response is sent back to the front end. Otherwise, the payload is packaged and sent to Open AI for analysis.

Validating and Parsing Request Data

The back-end endpoint validates and parses the request data using schemas created with the Zod Package. The schemas ensure that the request data adheres to the required structure and format. If the data is invalid, an error response is generated. This step ensures the integrity of the input data before sending it to Open AI.

Making the Request to Open AI

With the validated request data, the back-end endpoint makes a POST request to Open AI's chat completions endpoint. The payload, headers, and authorization key are included in the request. This is where the magic happens, and Open AI's vision model analyzes the uploaded image based on the provided instructions and prompts.

Retrieving the Response Data

Once the response is received from Open AI, the back-end endpoint extracts the Relevant data. The response object contains an array of choices, and the endpoint retrieves the first element from that array. Within the choice, the message is extracted, containing the analyzed feedback from Open AI's vision model.

Updating the Front-End Display with the New Message

Finally, the back-end sends the new message back to the front end, indicating a successful analysis. The front-end updates its messages list by appending the new message to the existing ones. This ensures a seamless chat-like conversation with Open AI's vision model, providing users with immediate feedback and suggestions for image improvement.

Highlights:

• Create a website using Open AI's vision feature to chat with images
• Utilize Next.js for building a simple one-page application
• Upload images and provide instructions for analysis
• Receive feedback and analysis from Open AI's vision model
• Understand the changes in the content property for the new vision model
• Encode images as base 64 strings for uploading
• Manage state in the chat container component
• Handle image changes and removals
• Process and send messages to Open AI's backend API
• Validate and parse request data using schemas
• Make requests to Open AI and retrieve response data
• Update the front-end display with the new message

FAQ:

Q: How much does it cost to use Open AI's vision feature? A: The cost is based on token usage, which is correlated to the size and detail of the images being analyzed. Larger and more detailed images require more tokens, resulting in higher charges. However, the pricing is generally affordable, especially for images of moderate size.

Q: Can I access the GPT-4 vision model without joining a waitlist? A: Yes, if you already have access to GPT-4 or GPT 3.5 turbo, you also have access to the new vision model. There are no additional waitlists or access restrictions for developers.

Q: How can I upload images to Open AI's vision model? A: You can upload images by converting them to base 64 format and passing them as part of the payload to Open AI's backend API. Alternatively, you can also provide image URLs if they are available online.

Q: How can I handle errors or invalid requests? A: The application includes error handling mechanisms both on the front end and the back end. Invalid requests or errors during analysis will trigger appropriate error messages and notifications for the user.

Q: Can I integrate this vision feature into my existing applications? A: Yes, you can integrate this vision feature into your applications by following the steps and using the provided code as a starting point. However, make sure to adapt the code to fit your specific needs and project structure.