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Building a Code Optimization Assistant with Streamlit, Electron.js, and LangChain

Building a Code Optimization Assistant that helps developers optimize their Python and C++ code.

The application leverages LangChain for interacting with a Large Language Model (LLM), Streamlit for a web-based interface, and Electron.js for a cross-platform desktop application. By the end of this post, you’ll have a fully functional application that analyzes code and provides optimization suggestions.

The Code Optimization Assistant is a tool designed to help developers optimize their Python and C++ code. It analyzes code snippets, identifies inefficiencies, and provides optimization suggestions using a Large Language Model (LLM). The application is available in two versions:

Desktop: Built using Electron.js.

Web-based: Built using Streamlit.

Table of Contents

1. Introduction

As developers, we often write code that works but isn’t optimized for performance or readability. Manually identifying inefficiencies can be time-consuming. Enter the Code Optimization Assistant—a tool that automates this process by analyzing your code and providing actionable suggestions for improvement.

This application is built using:

  • LangChain: To interact with an LLM (e.g., OpenAI GPT) for generating optimization suggestions.
  • Streamlit: For a simple and intuitive web-based interface.
  • Electron.js: For a cross-platform desktop application.

2. Features

  • Code Input: Accepts Python or C++ code.
  • Code Analysis: Detects inefficiencies like unused variables, redundant loops, and inefficient algorithms.
  • Optimization Suggestions: Provides actionable suggestions for improving code.
  • Multi-Language Support: Supports both Python and C++.
  • User Interface:
  • Streamlit: Web-based interface.
  • Electron.js: Desktop application.

3. Architecture

The application consists of three main components:

  1. Backend:
  • Code Analysis Module: Analyzes code for inefficiencies.
  • LLM Integration Module: Uses LangChain to interact with an LLM.
  1. Frontend:
  • Streamlit: Web-based interface.
  • Electron.js: Desktop interface.
  1. Communication:
  • Streamlit: Directly integrates with the backend.
  • Electron.js: Uses child_process to communicate with the Python backend.

4. Step-by-Step Implementation

4.1. Setting Up the Environment
  1. Install Python and Node.js:

2. Clone the Repository:

   git clone https://github.com/your-repo/code-optimization-assistant.git
   cd code-optimization-assistant
  1. Set Up the Python Environment:
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
  1. Set Up the Electron.js Environment:
   cd electron-app
   npm install
  1. Add OpenAI API Key:
  • Create a .env file and add your OpenAI API key:
    OPENAI_API_KEY=your_openai_api_key_here

4.2. Building the Backend

The backend consists of three modules:

  1. Input Module: Handles code input.
  2. Analysis Module: Analyzes code for inefficiencies.
  3. LLM Module: Generates optimization suggestions using LangChain.

Code Analysis Module

import ast

def analyze_python_code(code):
    issues = []
    try:
        tree = ast.parse(code)
        # Check for unused variables
        for node in ast.walk(tree):
            if isinstance(node, ast.Assign):
                for target in node.targets:
                    if isinstance(target, ast.Name):
                        var_name = target.id
                        # Check if the variable is used elsewhere
                        used = any(isinstance(n, ast.Name) and n.id == var_name for n in ast.walk(tree))
                        if not used:
                            issues.append(f"Unused variable: {var_name}")
    except SyntaxError as e:
        issues.append(f"Syntax Error: {e}")
    return issues

LLM Integration Module

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from dotenv import load_dotenv
import os

load_dotenv()

llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"), temperature=0.7)

prompt_template = PromptTemplate(
    input_variables=["code", "issues"],
    template="Analyze the following code and suggest optimizations:\n\nCode:\n{code}\n\nIssues:\n{issues}\n\nSuggestions:"
)

chain = LLMChain(llm=llm, prompt=prompt_template)

def get_optimization_suggestions(code, issues):
    response = chain.run(code=code, issues="\n".join(issues))
    return response

4.3. Creating the Streamlit Web App

The Streamlit app provides a simple web-based interface for the application.

Streamlit App Code

import streamlit as st
from analysis_module import analyze_python_code, analyze_cpp_code
from llm_module import get_optimization_suggestions

def main():
    st.title("Code Optimization Assistant")
    st.write("Paste your Python or C++ code below and get optimization suggestions!")

    code = st.text_area("Paste your code here:", height=200)
    language = st.radio("Select language:", ("Python", "C++"))

    if st.button("Optimize Code"):
        if code.strip():
            if language == "Python":
                issues = analyze_python_code(code)
            else:
                issues = analyze_cpp_code(code)
            suggestions = get_optimization_suggestions(code, issues)
            st.subheader("Optimization Suggestions:")
            st.write(suggestions)
        else:
            st.warning("Please paste some code!")

if __name__ == "__main__":
    main()

4.4. Building the Electron.js Desktop App

The Electron.js app provides a cross-platform desktop interface for the application.

Electron.js Main Process

const { app, BrowserWindow } = require("electron");
const path = require("path");

function createWindow() {
    const win = new BrowserWindow({
        width: 800,
        height: 600,
        webPreferences: {
            preload: path.join(__dirname, "preload.js"),
        },
    });

    win.loadFile("index.html");
}

app.whenReady().then(() => {
    createWindow();
});

app.on("window-all-closed", () => {
    if (process.platform !== "darwin") {
        app.quit();
    }
});

Frontend Interface

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Code Optimization Assistant</title>
</head>
<body>
    <h1>Code Optimization Assistant</h1>
    <textarea id="code-input" placeholder="Paste your code here..." rows="10" cols="50"></textarea>
    <br><br>
    <label for="language-select">Select language:</label>
    <select id="language-select">
        <option value="python">Python</option>
        <option value="cpp">C++</option>
    </select>
    <br><br>
    <button id="optimize-button">Optimize Code</button>
    <h2>Optimization Suggestions:</h2>
    <pre id="suggestions-output"></pre>

    <script src="renderer.js"></script>
</body>
</html>

5. Example Workflow

  1. Input Code:
   x = 10
   y = 20
   z = x + y
   print(z)
  1. Optimization Suggestions:
   1. Remove the unused variable `y` as it is not used elsewhere in the code.
   2. Simplify the code by directly printing the result: `print(x + y)`.

6. Future Enhancements

  • C++ Support: Integrate tools like cppcheck for static analysis.
  • Advanced Analysis: Add support for detecting inefficient loops, redundant calculations, and memory leaks.
  • GUI Improvements: Add syntax highlighting and detailed explanations for suggestions.

7. Conclusion

The Code Optimization Assistant is a powerful tool for developers looking to improve their code quality. By leveraging LangChain, Streamlit, and Electron.js, we’ve built an application that is both versatile and user-friendly. Whether you prefer a web-based interface or a desktop application, this tool has you covered.

8. Resources

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