Build Your Own Code Optimizer with Lyzr

In today's fast-paced software development landscape, optimizing code is a crucial aspect of enhancing performance and efficiency. From reducing time complexity to minimizing the number of operations performed, every optimization can make a significant difference in the overall functionality of an application. With the advent of advanced AI technologies, streamlining this process has become more accessible than ever before.

Code Optimizer using lyzr- a revolutionary tool designed to empower developers in optimizing their code effortlessly. Leveraging the power of OpenAI's cutting-edge models and seamlessly integrated with the intuitive Streamlit framework, this application redefines the way developers approach code optimization

Setting Up the Environment

pip install streamlit lyzr_automata openai

import streamlit as st
from lyzr_automata.ai_models.openai import OpenAIModel
from lyzr_automata import Agent, Task
from lyzr_automata.pipelines.linear_sync_pipeline import LinearSyncPipeline
from PIL import Image
from dotenv import load_dotenv
import os

load_dotenv()
api = os.getenv("OPENAI_API_KEY")

streamlit for creating the web interface.
lyzr_automata for interacting with agents and models.
PIL for handling images.
dotenv for loading environment variables.
os for accessing environment variables.
Load the OpenAI API key from a .env file.

Input and OpenAI model setup:
code = st.text_area("Enter your Code: ", height=300)

open_ai_text_completion_model = OpenAIModel(
    api_key=api,
    parameters={
        "model": "gpt-4-turbo-preview",
        "temperature": 0.2,
        "max_tokens": 1500,
    },
)

Create a text area for users to enter their code.
Create an instance of the OpenAI text completion model with specified API key, model, temperature, and maximum tokens.

Code Optimization Function:

def code_optimization(code):
    optimizer_agent = Agent(
        role="maths expert",
        prompt_persona=f"Your task is to Optimize the given Code and improve the code."
    )

    prompt = f"""find complexity and Optimize a {code} to improve its time complexity and reduce the number of operations performed,
     while ensuring the output remains sorted correctly.
    [!Important] Only give code and what optimized and How?

    Give Output As Below:
    Code
    What is Optimized?
    How it's Optimized?

    """

    optimization_task = Task(
        name="Maths Problem Solver",
        model=open_ai_text_completion_model,
        agent=optimizer_agent,
        instructions=prompt,
    )

    output = LinearSyncPipeline(
        name="Code Optimization Pipline",
        completion_message="Optimization completed",
        tasks=[
            optimization_task
        ],
    ).run()

    answer = output[0]['task_output']

    return answer

Create an optimizer agent with the role of "maths expert".
Construct a prompt with instructions for the agent, including the code to be optimized and specific optimization goals.
Create an optimization task using the model, agent, and instructions.
Run the task in a linear sync pipeline.
Extract the optimized code and explanation from the task output.
Return the solution as a string.

Output:

if st.button("Optimize"):
    solution = code_optimization(code)
    st.markdown(solution)

Create an "Optimize" button:
When clicked, call the code_optimization function with the user's code.
Display the formatted solution using Streamlit's markdown function.

Code Optimizer using lyzr stands as a testament to the transformative potential of AI in software development. By harnessing the synergy between advanced AI models and intuitive interfaces, developers can unlock new frontiers in code optimization. Whether it's enhancing time complexity, reducing operations, or ensuring output integrity,Code Optimizer using lyzr is poised to revolutionize the way developers optimize their code.

try it now: https://lyzr-code-optimizer.streamlit.app/
For more information explore the website: Lyzr