semvec/pypi-packages huggingface dataset:
heycli 1.0.1
Your AI Buddy for Daily Tasks
Requires Python: >=3.9
Keywords: ai, ai-assistant, artificial-intelligence, assistant, automation, chatbot, cli, code-assistant, command-line, developer-tools, file-management, gpt, natural-language, productivity, python-cli, system-tools, task-automation, terminal, terminal-assistant, workflow

# HeyCLI 🤖 - Your AI Buddy for Daily Tasks

![HeyCLI Preview](./heycli.jpg)

Stop wasting time on repetitive chores! `HeyCLI` is your AI-powered command-line assistant that simplifies everyday tasks. Forget writing scripts. Just run commands like `heycli remove all empty folders`. 

Zero setup, maximum efficiency!

For example, need to convert all your `.png` images to `.jpg`? Just type `heycli convert *.png to *.jpg`. Or, if you want to extract the first column of your `contacts.csv` and save it to `names.txt`, simply use `heycli extract column 1 from contacts.csv to names.txt`.

**heycli** shows the code and confirms execution before running, ensuring you're always in control.

Also, by default, it works in your current directory, so you don't have to specify the current working directory every time in your prompt.

**If you can describe it, HeyCLI can do it.**
Get your time back.

For getting free API keys, check out [Zuki Journey](https://cas.zukijourney.com/).

https://github.com/user-attachments/assets/d3e53d75-7614-45ca-a347-dca77aba51e8

## 🚀 Install
```bash
pipx install heycli (recommended)

# Set up config file at ~/.heycli/config.yaml 
# (on Windows: C:\Users\YourUsername\.heycli\config.yaml)
# Then use it!
heycli rename all files sequentially from 1
heycli sort file names by size (descending) and save in a text file
heycli change all images format to jpg
heycli find large files in my documents folder
heycli print all emails from contact-list.txt
heycli generate 10 random numbers between 100 and 1000
```

## 📝 Sample Config File

```yaml
# List faster, more reliable providers and more capable models first.
# They will be attempted in the order listed.

providers:
  - base_url: "https://api.naga.ac/v1"
    api_key: "Your API key"
    models:
      - "gpt-4o"
      - "deepseek-chat"
      - "gemini-2.0-flash"

  - base_url: "https://api.electronhub.top/v1"
    api_key: "Your API Key"
    models:
      - "claude-3-7-sonnet-20250219"
      - "gpt-4o"

  - base_url: "https://openrouter.ai/api/v1"
    api_key: "Your API key"
    models:
      - "deepseek/deepseek-chat:free"
      - "google/gemini-2.0-pro-exp-02-05:free"
```
sikumim 0.1.5
A simple tool to download lecture notes, written by Regev Yehezkel Imra

# Sikumim

## Introduction
A simple tool to quickly and easily download lecture notes (and more).

All notes are published with the courtesy of **Regev Yehezkel Imra**.

##  Instructions
1. Install the library:
    ```
    pip install sikumim
    ```
2. Run the following command to download course material:
    ```
    dl_lec <course-name> <output-path>
    ```
   For example,
   ```
   dl_lec Topology Desktop
   ```
iqr-outlier 0.1.5
A library to detect and remove outliers using IQR.
Requires Python: >=3.6

# IQR Outlier Detection Library

This Python package helps in detecting and removing outliers from a dataset using the **Interquartile Range (IQR)** method.

## Features
- Detects outliers using IQR.
- Removes outliers from a dataset.
- Works with Pandas DataFrames and Series.

## Installation

```bash
pip install iqr-outlier
```

### Import the package:
```python
from iqr_outlier.detection import detect_outliers # Returns list with outliers
from iqr_outlier.removal import remove_outliers # Return a pandas series with outliers removed
import pandas as pd
```

### Example
```python
# Sample dataset
data = pd.Series([10, 12, 14, 15, 18, 20, 22, 24, 30, 100])

# Detect outliers
outliers = detect_outliers(data)
print("Outliers:", outliers.tolist())

# Remove outliers
cleaned_data = remove_outliers(data)
print("Cleaned Data:", cleaned_data.tolist())
```

## Project Structure
```
iqr_outliers/
│-- iqr_outlier/
│   │-- __init__.py
│   │-- detection.py
│   │-- removal.py
│-- setup.py
│-- test.py
│-- README.md
```
ao_ann 0.1.6
American Options Pricing ANN
Requires Python: >=3.13

# American Options ANN

> This is still a work in progress, all `v0.1.*` releases are testing releases.

This is a Python package for American Options Pricing using Artificial Neural Networks (ANN)
that assumes the option follows a GARCH process. The package will contain 3 stages of datasets
for 3 GARCH models:

1. HN-GARCH
2. Duan-NGARCH
3. GJR GARCH


## Project Structure
- `ann.py`: Contains training and evaluation of the ANN model, as well as the main entry point for the program (`ao_ann_main(...)`)
- `loss.py`: Contains the function to calculate the different loss measures between target and predicted values.
- `model.py`: Contains the implementation of the ANN model used for pricing American Options.
- `dataset.py`: Contains parsing the CSV files and preparing the data for training and testing.
- `utils.py`: Contains utility functions for the package.


## Installation

```bash
pip install ao_ann
```

## Running Locally

This project uses the Python package manager `uv`, this can be installed using the following command:
```bash
$ git clone https://github.com/Mustafif/AO_ANN.git
$ cd AO_ANN
$ pip3 install uv # install uv
$ uv sync
$ uv run main.py # run the main.py file
```

## Todo
- [X] Comments in the code
- [ ] Documentation
- [ ] Example program