keeya 0.3.0

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 free AI models and automatic fallback.

Optional API Keys for Higher Limits:

• OpenRouter: export OPENROUTER_API_KEY="your_key_here"
• Google Gemini: export GEMINI_API_KEY="your_key_here"
• Hugging Face: export HF_API_KEY="your_key_here"

Free Tier Limits:

• 🔥 OpenRouter: 200 requests/day
• 🚀 Gemini: 6M tokens/day (60 requests/minute)
• 🤗 Hugging Face: 300 requests/hour

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
• Smart Model Selection: Automatically chooses the best AI model based on task complexity
• Jupyter Ready: Works seamlessly in notebooks and Colab
• Safe Execution: Safely executes generated code and returns results
• Multi-Model Support: GPT-OSS-20B (fast), Qwen2.5-32B (balanced), Qwen3-480B (powerful)

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)

Smart Model Selection

Keeya automatically selects the best AI model based on task complexity:

• GPT-OSS-20B (2-4 seconds): Fast fallback for simple tasks
• Qwen2.5-32B (3-6 seconds): Sweet spot for balanced performance
• Qwen3-480B (6-12 seconds): Worth the wait for complex tasks

Manual Model Selection

You can also specify a model manually:

# Use specific model
code = keeya.generate("complex function", model="qwen3-480b")
cleaned_df = keeya.clean(df, model="gpt-oss-20b")

# See 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