To create a more dynamic HTML demo, we’ll inject JavaScript powered by GPT-3.5 Turbo responses. Here’s an example HTML file that requests code snippets and displays them in a live editor:
Below is a Python script that uses GPT-3.5 Turbo to generate functions with inline comments. This approach ensures your code is both functional and self-documented.
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import openaiopenai.api_key = 'YOUR_API_KEY'def generate_python_function(prompt: str) -> str: """ Generate a Python function based on the provided prompt. Returns the generated code as a string. """ response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}], temperature=0.2, max_tokens=250 ) return response.choices[0].message.contentif __name__ == '__main__': prompt_text = "Create a Python function that checks if a number is prime, with comments." code = generate_python_function(prompt_text) print("Generated Python Code:\n", code)
Be sure to install the OpenAI Python client with pip install openai. Keep your API key secure and never commit it to a public repository.
For dataset management, GPT-3.5 Turbo can auto-generate ANSI-compliant SQL to create tables, insert data, and run queries. Below is an example prompt and the expected output:
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-- Prompt: "Generate SQL to create a users table with id, name, email; insert three records; select all users."CREATE TABLE users ( id INT PRIMARY KEY, name VARCHAR(100), email VARCHAR(100) UNIQUE);INSERT INTO users (id, name, email) VALUES (1, 'Alice Smith', 'alice@example.com'), (2, 'Bob Johnson', 'bob@example.com'), (3, 'Carol White', 'carol@example.com');SELECT * FROM users;
Always review generated SQL before executing it against production databases to avoid unintended data loss.