Building Your Own Code Generation Tool Using LLM

In today's world, where artificial intelligence is becoming increasingly accessible, many people wonder how to harness the potential of large language models (LLMs) to automate code writing. In this article, I will present a practical guide to building your own code generation tool using LLM.

Introduction

Large language models, such as Mistral, are capable of generating code in various programming languages. They can be used to create tools that will help programmers in their daily work. In this article, we will discuss how to build such a tool step by step.

Choosing the Model

The first step is to choose an appropriate model. You can choose one of the available open-source models or use the API provided by a cloud provider. In this example, we will use the Mistral model.

Building the Basic Tool

1. Installing Required Libraries

To get started, we need a few libraries. In this example, we will use the transformers library to load the model and torch for computations.

pip install transformers torch

2. Loading the Model

Next, load the Mistral model.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "mistral"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

3. Generating Code

Now we can write a function that will generate code based on the given prompt.

def generate_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

4. Testing the Tool

Let's test our tool by generating a simple Python code.

prompt = "Write a function that adds two numbers."
print(generate_code(prompt))

Expanding Functionality

1. Adding Context

You can expand our tool by adding context that will help the model understand what code to generate.

def generate_code_with_context(prompt, context):
    full_prompt = f"{context}\n\n{prompt}"
    return generate_code(full_prompt)

2. Improving the Quality of Generated Code

To improve the quality of the generated code, you can add a mechanism for verification and error correction.

def verify_and_fix_code(code):
    verification_prompt = f"Check this code and fix errors:\n\n{code}"
    return generate_code(verification_prompt)

Deploying the Tool

1. Creating a User Interface

You can create a simple user interface that will allow easy use of the tool.

def main():
    print("Welcome to the code generation tool!")
    while True:
        prompt = input("Enter a prompt (or 'exit' to end): ")
        if prompt.lower() == 'exit':
            break
        code = generate_code(prompt)
        print("\nGenerated code:")
        print(code)
        print("\n")

if __name__ == "__main__":
    main()

2. Deploying on a Server

To make the tool available to others, you can deploy it on a server. You can use the Flask library to create a simple API.

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/generate', methods=['POST'])
def generate():
    data = request.json
    prompt = data.get('prompt', '')
    code = generate_code(prompt)
    return jsonify({'code': code})

if __name__ == '__main__':
    app.run(debug=True)

Summary

In this article, we discussed how to build your own code generation tool using large language models. We showed how to load the model, generate code, and expand the tool's functionality. You can further develop this tool by adding more features and improving the quality of the generated code.

Example Code

Here is the complete example code that you can use as a starting point for your own tool.

from transformers import AutoModelForCausalLM, AutoTokenizer
from flask import Flask, request, jsonify

# Loading the model
model_name = "mistral"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generating code
def generate_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# API Interface
app = Flask(__name__)

@app.route('/generate', methods=['POST'])
def generate_api():
    data = request.json
    prompt = data.get('prompt', '')
    code = generate_code(prompt)
    return jsonify({'code': code})

if __name__ == '__main__':
    app.run(debug=True)

Conclusions

Building your own code generation tool using LLM is a fascinating task that can significantly facilitate the work of programmers. Thanks to the availability of advanced language models, such as Mistral, everyone can create their own tool tailored to individual needs.