How To Develop Applications with GPT-3 and Dash: Transforming Human Language into Source Code

How To  Develop Applications with GPT-3 and Dash: Transforming Human Language into Source Code

“Welcome to the Era of Transformers”

Transformers, specifically in the field of artificial intelligence, are poised to revolutionize how we approach application development. Not too far in the future, a large portion of low-value code that developers laboriously craft might be generated by AI and Transformers. Transformers are essentially deep learning models that utilize self-attention mechanisms to compute representations, sidestepping the traditional sequence-aligned Recurrent Neural Networks (RNNs).One such Transformer making waves in the AI community is OpenAI's GPT-3. GPT-3, the latest iteration of the Generative Pretrained Transformer, boasts an impressive 175 billion machine learning parameters. It has the uncanny ability to generate human-like texts that range from storytelling and poetry to source code.

Among the many advantages of GPT-3 is its ability to operate using Few Shot or Zero-Shot Learning. This drastically simplifies the process by eliminating lengthy data preparation and training phases. This blog focuses primarily on source code generation using GPT-3, along with some text generation capabilities. Accompanying the blog is the complete Python source code used to construct the web app. Keep in mind, as of writing, the GPT-3 DaVinci API is in beta, so its code generation capabilities might improve in the coming months. Essentially, the application serves as a playground for the user to input a simple request in natural language and observe the resulting source code generated.

OpenAI's GPT-3

Navigating the Capabilities of the Web Application

Navigating the Capabilities of the Web Application

This source code generation or text completion is managed by integrating with the GPT-3 API. For developers, imagine being able to simply state, "Please generate Python code that predicts salary based on age, position, and experience using the Random Forest algorithm", or "Please generate a Java class named VIP Person to represent a person with name, age, and gender attributes", and instantly get the generated code. To assist users, we've prepared a preset list of typical requests that can be customized to suit individual needs. All it takes is a click of a button to generate the code and view the results. The technology stack used in the construction of this application includes Python, Dash for the Python web application, and the GPT-3 API by OpenAI. Let’s explore in detail:

1. GPT-3 API integration: The heart of this application is its integration with GPT-3 API, which enables source code generation and text completion.

2. Dream come true for developers: With this system, developers can simply express their coding requirements in natural language. For instance, they could ask, "Generate Python code that predicts salary considering variables like age, position, and experience, employing the Random Forest algorithm", or "Craft a Java class named VIP Person to encapsulate attributes such as name, age, and gender of a person".

3. Instant code generation: The system promptly returns the requested code, making code generation a breeze for developers.

4. Pre-configured requests: A selection of preset, common requests is available for user convenience. These can be modified as per user requirements to generate specific code snippets.

5. User-friendly operation: Generating code is as simple as clicking a button. Upon pressing the button, the source code is instantaneously generated and displayed, making it a user-friendly tool for both developers and non-developers.

6. Robust technology stack: The application is built on a reliable technology stack that includes Python, Dash for the Python web application, and the OpenAI's GPT-3 API, ensuring a smooth and robust application experience.

A Peek Into the Application’s Capabilities

Here's a brief rundown of what you'll be able to accomplish with the application after following the instructions provided in the subsequent sections.The user interface is incredibly straightforward. Users can select a Preset of a Request Type from a dropdown menu. Following this, a text area field is populated with a standard Request written in natural language. Users can modify the Request if desired. After making the necessary changes, they can click on the Generate button to view the corresponding source code generated and displayed.

1. Code Generation:-

SQL Query : One of the key features of the application is generating SQL queries. Users need to frame their request in natural language, specifying fields and tables along with their data selection requirements. The application then generates an SQL query adhering to PostgreSQL syntax based on this request, thus facilitating the creation of robust, accurate queries. Example-This feature takes a user-inputted request in natural language, which describes fields, tables, and data selection criteria, and translates it into an SQL query. This is accomplished by utilizing the powerful natural language processing capabilities of the GPT-3 engine. A PostgreSQL query is generated and returned, providing users with a syntactically accurate and efficient query.

SQL Query

AWS CloudWatch Alarm : The application is also capable of generating code to configure AWS CloudWatch Alarms. As an example, users can leverage an SDK like Boto to generate a CloudWatch Alarm that is triggered when the CPU usage exceeds a certain threshold (e.g., 70%). This feature simplifies the configuration process for these crucial alarms, promoting efficient resource usage monitoring.

AWS CloudWatch Alarm

Predicting Salary Using Random Forest Algorithm : The application can also generate complete code scripts to predict salaries using various factors, including age, position, and experience. The produced Python code harnesses the power of RandomForestRegressor for this task. In addition to generating the code, the application also plots a relevant graph using the Pyplot library, providing users with a visual representation of the prediction algorithm.

Generating a Java Class :Generating a Java Class is another area where this application shines. For example, it can generate a basic Java Class complete with attributes based on user inputs. The GPT-3 engine can optimize code completion accuracy by beginning the description with a Java comment and a Class name.

2. Developer Task List

This application also accommodates task lists as input requests. The generated code defaults to Python, but users can specify a different programming language if required. This feature simplifies task list coding, thus making it easier for developers to manage their tasks and responsibilities.

3. Text Processing:-

Summarize Text : Text summarization is another powerful capability of this application. Leveraging GPT-3's text generation process, the application can summarize large pieces of text. Users can adjust several parameters in the code to determine the length of the final summary, providing a high degree of customization and control.

Rephrase Text :The application can also rephrase text to make it understandable to a young audience, such as a 10-year-old child. This feature taps into GPT-3's ability to adjust the complexity and comprehensibility of a given text.Quick Start: From Registration to ExecutionTo kick-start your journey with the provided code on my GitLab, you need to first register with OpenAI and obtain your API key. After acquiring the key, follow these steps:

- Visit OpenAI’s website, sign up, and acquire your API Key.

- Head to my GitLab project named "GPT-3-Dash-Playground."

- Open the Jupyter Python notebook with Google Colab.

- Replace the openai.api_key value in the code with your API key.

- Navigate to the 'Execution' menu and select 'Run All'.

- A web application should launch in a few moments. Click on the URL generated at the bottom of the page to display it.

- Breaking Down the Code to get a better grasp of the application, it's crucial to understand the different segments of the code.

4. Understanding the Application Code: A Deeper Dive

To fully harness the power of this application, it is essential to have a strong grasp of its underpinnings. The code base is mainly divided into two critical segments: GPT-3 initialization and the presets. Both parts play an essential role in the smooth functioning of the application.

GPT-3 Initialization : This segment involves the setup and configuration of the OpenAI GPT-3 library. To make API calls, the GPT-3 library relies on dependencies that need to be installed and initialized correctly. This section of the code handles the installation process, ensuring that all necessary dependencies are in place. Additionally, your unique API key, obtained upon registration with OpenAI, is required here. It is a crucial identifier allowing your application to communicate with the GPT-3 engine, authenticate requests and receive responses.

GPT-3 Initialization

The Presets : To facilitate user interaction, the application includes ten presets: eight geared toward source code generation and two focused on text completion. Each preset is essentially a pre-programmed natural language request formulated to extract specific functionality from the GPT-3 engine. Once a user selects a preset, the application crafts a GPT-3 API call with carefully calibrated parameters. The call is then dispatched to the GPT-3 engine, which processes the request and returns a result. This result, depending on the request, could be a code snippet, a text summary, or a rephrased sentence.

The Presets

The Playground Web Application

Built on the Dash framework, this web application facilitates building and running web applications in Python. More specifically, the Jupyter-dash library allows for the execution of web apps directly from your Jupyter Colab notebook.Let’s delve deeper into the Playground Web Application:-The web application is built on a series of dependencies and components. Understanding how they work together can provide a deeper understanding of how the application functions.

Dependencies Import : The first section imports all the necessary Dash dependencies. It's important to note that Dash provides two types of components: Core (built-in) components and HTML components.

The Playground Web Page : The layout of a simple web page is built in this section. It consists of a title and four main elements that facilitate user interaction and display of results initiated when the 'Generate' button is pressed, triggering a call to the GPT-3 API, which then returns the completed code or text.

7. Conclusion

The power of GPT-3 lies not just in its ability to understand and generate human-like text, but also in the applications it can power, like the one we've explored. This powerful application, with its robust initialization of the GPT-3 API and its comprehensive array of presets, showcases the breadth and depth of GPT-3's capabilities. It also provides a glimpse into the future of AI in software development, where AI could assist in routine coding tasks, freeing developers to focus more on complex problem-solving and innovative work. As we continue to explore the capabilities of GPT-3 and other AI technologies, one thing is clear: we're only scratching the surface of what's possible. By integrating AI with web applications, we open a world of possibilities. Leveraging GPT-3's capabilities in code generation and text completion can significantly enhance a developer's productivity and efficiency. Building this simple web application demonstrates how AI can help transform a basic request in natural language into executable source code or helpful text summaries. This, however, is only the tip of the iceberg. Given the rapid advancements in AI and Transformer models, we can anticipate more complex and extensive applications in the near future. Remember, the OpenAI GPT-3 DaVinci API is currently in beta version, and the capabilities may advance in the coming months. So, keep experimenting, learning, and improving your applications to harness the full potential of this transformative technology. Lastly, the code for this web application is fully open-source and available for anyone to adapt, modify, and improve upon. Don't hesitate to contribute and share your versions. Let's collaboratively shape the future of code generation with AI. Happy coding!

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Frequently Asked Questions

Q1: How secure is the usage of my OpenAI API Key within this application?

A1. The security of your OpenAI API Key is paramount as it is the access gateway to the GPT-3 services. This application doesn't store your API key beyond the scope of your current session. However, you should still handle it with care and ensure you don't share it unintentionally. If you suspect it's been compromised, OpenAI's interface allows for key regeneration, which is recommended in such scenarios.

Q 2: Can the generated code from the GPT-3 API be directly used in production?

A2. While the GPT-3 powered application generates impressively accurate code, it is not infallible. The produced code should serve as a solid foundation and a helpful tool during development. However, it is strongly advised to thoroughly review and test the generated code before integrating it into any production environment to avoid unexpected behavior and ensure it meets your requirements.

Q 3: What happens if the generated code or text from a preset doesn't match my needs?

A3. The power of GPT-3 lies in its ability to learn from interactions. If the output doesn't meet your requirements, it is encouraged to tweak the natural language request or even adjust the parameters of the GPT-3 API call. With experimentation, you can harness GPT-3's capabilities better, allowing it to deliver results that closely align with your expectations.

About The Author

Jesse Anglen, Co-Founder and CEO Rapid Innovation
Jesse Anglen
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