keeya 0.4.0

AI-Powered Python Library for Code Generation

Keeya

AI-Powered Python Library for Code Generation

Keeya is a simple Python library that uses AI to generate clean, executable Python code on-demand. Unlike traditional code completion tools, Keeya runs in your Python environment and generates production-ready code based on your requirements.

Installation

pip install keeya

Setup

No setup required! Keeya works out of the box with Google Gemini AI.

Free Tier:

• 🚀 Gemini 1.5 Flash: 6M tokens/day (60 requests/minute)
• ⚡ Fast & Reliable: No rate limits for demo usage
• 🎯 High Quality: Clean, production-ready code generation

Quick Start

import keeya

# Generate any Python function
code = keeya.generate("function to add two numbers")
print(code)

# Generate complex algorithms
code = keeya.generate("function to implement quicksort")
print(code)

Examples

Basic Code Generation

import keeya

# Generate any Python function
code = keeya.generate("function to add two numbers")
print(code)
# Output: def add_numbers(a, b): return a + b

# Generate data processing function
code = keeya.generate("function to calculate mean of a list")
print(code)
# Output: def calculate_mean(numbers): return sum(numbers) / len(numbers)

Data Science Operations

import keeya
import pandas as pd

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

# AI-powered data cleaning
cleaned_df = keeya.clean(df)

# AI-powered analysis
insights = keeya.analyze(df)

# AI-powered visualization
keeya.visualize(df, plot_type='scatter')

# AI-powered ML training
model = keeya.train(df, target='price')

Features

• Simple API: Just call keeya.generate() or keeya.clean()
• AI-Powered: Uses AI to generate code based on your data
• Context-Aware: Understands your DataFrames and generates appropriate code
• Google Gemini Integration: Powered by Google's advanced AI models
• Jupyter Ready: Works seamlessly in notebooks and Colab
• Safe Execution: Safely executes generated code and returns results
• Clean Code Generation: Produces production-ready Python code with inline comments

Examples

Basic Functions

# Generate utility functions
code = keeya.generate("function to reverse a string")
code = keeya.generate("function to find duplicates in a list")
code = keeya.generate("function to sort a dictionary by values")

Data Science

# Data cleaning
cleaned_df = keeya.clean(df)

# Data analysis
analysis = keeya.analyze(df)

# Visualizations
keeya.visualize(df, plot_type='histogram')
keeya.visualize(df, plot_type='correlation')

# Machine learning
model = keeya.train(df, target='target_column')
predictions = model.predict(test_df)

Model Selection

Keeya uses Google Gemini by default, but you can specify different models:

# Use default Gemini 1.5 Flash (fast and reliable)
code = keeya.generate("function to calculate fibonacci")

# Use Gemini 1.5 Pro (more capable for complex tasks)
code = keeya.generate("complex machine learning pipeline", model="gemini-1.5-pro")

# Get available models
models = keeya.get_available_models()
print(models)

API Reference

keeya.generate(prompt, model=None)

Generate Python code from natural language prompt.

keeya.clean(df, model=None)

AI-powered data cleaning. Returns cleaned DataFrame.

keeya.analyze(df, model=None)

AI-powered data analysis. Returns analysis results.

keeya.visualize(df, plot_type=None, model=None)

AI-powered visualization. Creates and displays plots.

keeya.train(df, target, model=None)

AI-powered ML model training. Returns trained model.

keeya.get_available_models()

Get available models and their descriptions.

Requirements

• Python 3.8+
• pandas
• requests

License

MIT License