Have you ever been curious about independently developed AI products? Today, we’ll take a deep dive into the implementation principles behind the Sora AI video generator. This AI generator independently developed by programmers is not only impressive, but also reveals the infinite possibilities of AI technology.
First, let’s take a look at the technology stack of Sora AI video generator. The project uses the React front-end framework and combines Nest JS and TypeORM for back-end development. For those who are not familiar with JavaScript and TypeScript, these two technologies are almost standard skills for getting started with independent development. JS can be directly used to write front-end and back-end code, making development more flexible and efficient.
In the root directory of the project, we can find a key CONFIG file. This file reveals the structure of the project: the front-end code is located in the components folder within the src/app folder, while the back-end code is located in the API folder. This clear project structure makes it easier for developers to understand and maintain the code.
So, how is the homepage of Sora AI video generator implemented? We know that the page path of the Nest project and the path of the configuration file in the code are mapped to each other. Through the concepts of dynamic routing and routing groups, we can find the code corresponding to the current page. Specifically, the hero component is responsible for the upper part of the page, the tab component is responsible for the three tabs in the middle, and the video component is responsible for displaying the video list.
However, during the development process, the author encountered a problem: the video list had no video data. In order to solve this problem, the author turned to the API interface to find the answer. They discovered an interface called updateVideo, which fetches all video data through POST requests and inserts it into the database. But where does this video data come from?
After further investigation, we discovered that the video data is read from an environment variable called videoDataFile. This environment variable points to a JSON file containing video data. However, because the author may have neglected to submit this file, the video data does not load correctly. In order to solve this problem, we can create a new data.json file according to the data field conversion name and add the corresponding video data. At the same time, we also need to set a unique user ID (adminUserId) to mark which user generated each video.
When we call the updateVideo interface, all video data will be added to the database. In order to trigger this interface, we can find a .http file in the debug folder and use the Rest client plug-in to send the request. Once the request is sent successfully, we can see in the database that the video data has been successfully added.
However, when we refreshed the project local address, we found that the video list was still not displayed. Why is this? It turns out that the way to obtain video data in the code is to read the 1st to 50th data by calling the getLadiesVideo method. However, due to some reasons (perhaps the database query conditions are set incorrectly), these data are not returned correctly. In order to solve this problem, we need to check and modify the database query conditions to ensure that the required video data can be obtained correctly.
In addition, Sora AI video generator also uses advanced technologies such as server-side rendering (SSR) and headless components (Headless UI). Server-side rendering makes it possible to complete operations such as database queries on the server side, thereby improving page loading speed and performance. Headless components allow developers to download and use specific components as needed, greatly reducing the packaged size of the project.
Finally, it is worth mentioning that Sora AI video generator also implements internationalization functions. By listening to all requests and using the getLocale method to obtain the language information in the request header, the project is able to match the language that best suits the user and return the corresponding content. This international design enables the project to better meet the needs of users in different countries and regions.
In short, the implementation principle behind Sora AI video generator involves knowledge and technology in multiple fields. Through an in-depth analysis of this project, we can not only learn many practical development skills and lessons, but also gain a deeper understanding and expectation for the future development of AI technology. If you are interested in independent development and AI technology, you might as well try this project!
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