Python Code Generation Using Transformers

Python’s code generation capabilities streamline development, empowering developers to focus on high-level logic. This approach enhances productivity, creativity, and innovation by automating intricate code structures, revolutionizing software development.

Automated Code Generation

• Automated code generation using Python finds extensive applications across diverse domains, offering tailored solutions to complex problems.
• One prominent application is the creation of repetitive or boilerplate code, where Python scripts can dynamically generate routine structures, saving developers significant time and effort.
• Additionally, code generation is invaluable in the area of data processing and analysis, facilitating the creation of optimized algorithms for tasks like sorting, filtering, or aggregating data.

Contemporary Code Generation Tools

• In the ever-changing terrain of software development, a few state-of-the-art tools and frameworks support the relentless quest for streamlined code generation. One of them is Hugging Face Transformers library that gives access to advanced language models such as GPT-2, GPT-3, GPT-Neo and even ChatGPT. These models are pre-trained or finetuned on a large corpus of text data that enables them to understand the intricacies of natural languages and generate code fragments relevant in terms of context and syntax.
• To automatically generate Python codes using GPT-Neo, we will focus on employing Hugging Face models. We want to show how interfacing with these models can lead us to write short but meaningful Python scripts. What they aim at is to help programmers who need to do their job faster, make it more effective, as well as encourage them to solve problems differently by using creativity. Throughout practical examples and exploration we will demonstrate how versatile Hugging Face Models can be in generating code tailored for various programming scenarios.

Step-by-step implementation

Installing required modules

At first, we will install Torch and Transformers module to our runtime.

!pip install torch transformers

Importing required libraries

Now we will import the Python libraries like Torch and transformers. And random seeding will be set to handle the resource randomness.

Python3

import torch
from transformers import pipeline
# handling randomness
def set_seed(seed):
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

Defining the model

Now we will load a publicly available finetuned model.

Python3

# Load the model 
pipe = pipeline("text-generation", model="GuillenLuis03/PyCodeGPT")

Code generation

• Now a text generation pipeline using the Hugging Face Transformers library is employed to create a Python code snippet. The specified prompt, “function to reverse a string,” serves as a starting point for the model to generate relevant code. We can use any different prompt.
• The `max_length` parameter determines the maximum length of the generated code, and the `temperature` influences the randomness of the output. By setting `num_return_sequences` to 1, the model produces a single code sequence.
• The resulting Python code snippet, which may include a function to reverse a string, is then printed to the console. This demonstrates the simplicity and power of using pre-trained language models for code generation tasks with just a concise prompt and a few configuration parameters.

Python3

# Example 1
prompt = "short function to reverse a string"
generated_code = pipe(prompt, 
                      max_length=28, 
                      temperature=0.7, 
                      num_return_sequences=1
                      )[0]['generated_text']

print("Generated Python code-->")
print(generated_code)  # output format: given prompt then generated code

Output:

Generated Python code-->
short function to reverse a string.
def reverse_string(s):
    return s[::-1]

So, in this we can change the prompt and generate code based on it. However, only simple codes can be generated.