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AI-Powered Python Library for Code Generation

Project description

🚀 Keeya - AI-Powered Python Code Generation

Python 3.7+ License: MIT PyPI version

Transform natural language into executable Python code for data analysis, cleaning, and machine learning.

✨ Features

  • 🤖 Natural Language to Code: Generate Python code from plain English
  • 🧹 Smart Data Cleaning: Automatically handle missing values, duplicates, and data types
  • 📊 Instant Analysis: Get comprehensive statistics and insights
  • 📈 Auto Visualizations: Generate relevant plots based on your data
  • 🎯 ML Pipeline Generation: Build complete machine learning workflows
  • 🔒 Safe & Secure: Review code before execution, powered by Google Gemini

🚀 Quick Start

Installation

pip install keeya

Setup (One-time)

import keeya
keeya.setup()  # Enter your free Gemini API key

Basic Usage

import pandas as pd
import keeya

# Load your data
df = pd.read_csv('data.csv')

# Generate code for any task
code = keeya.generate("create a function to calculate fibonacci numbers")

# Clean your data
cleaned_code = keeya.clean(df)

# Analyze your data  
analysis = keeya.analyze(df)

# Create visualizations
viz_code = keeya.visualize(df)

# Build ML pipeline
ml_code = keeya.train(df, target='target_column')

🎯 Why Keeya?

  • Simple: One consistent API across all functions
  • Smart: Context-aware code generation based on your actual data
  • Safe: Always returns code for you to review and execute
  • Free: Powered by Google Gemini's free tier
  • Fast: Get results in seconds, not hours

🔑 Getting Your API Key

  1. Visit Google AI Studio
  2. Click "Create API Key"
  3. Copy the key and paste it when running keeya.setup()

📖 Documentation

Run keeya.help() for detailed usage information and examples.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

⚠️ Disclaimer

Keeya generates code using AI. Always review generated code before executing in production environments.

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