semvec/pypi-packages huggingface dataset:
xf-cli 0.0.6
XF makes repeated python commands faster through import preloading
Requires Python: >=3.9

# XF

XF stands for extra fast. XF imports your key packages on first call, so you don't pay a recurring tax for imports. Usually saves 1-10 seconds per invocation after startup.

Key features
- Comes with two modes: ipython mode and shell mode. Ipython feels like an ipython console; shell operates like a terminal.
- Extensible: we will accept PRs for additional commands. See the `commands` directory for examples.
- Easily profile any command using the `prof` prefix.
- Can use your own environment or run in an isolated environment using the `--isolated` flag.

Who would benefit from this:
- You are a non-python developer using a python cli
- You are a python developer

Who would not benefit from this:
- Production use cases
- Long-running commands where a few seconds of savings don't make a difference
pysend-cli 1.0.0
A simple command-line tool for sending emails to yourself
Requires Python: >=3.6

# Pysend

A simple command-line tool for sending emails to yourself.

## Installation

```bash
pip install pysend-cli
```

## Usage

First time setup:
```bash
pysend login
```

Send an email:
```bash
pysend google.com
pysend google.com -s "A search engine"
```

## License

MIT
pykitml 0.1.3
Machine Learning library written in Python and NumPy.
Requires Python: >=3.10
Keywords: pykitml

![pykitml logo](https://raw.githubusercontent.com/RainingComputers/pykitml/master/pykitml128.png)

# pykitml (Python Kit for Machine Learning)
Machine Learning library written in Python and NumPy.

### Installation

```
python3 -m pip install pykitml
```

### Documentation

https://pykitml.readthedocs.io/en/latest/

# Demo (MNIST)
### Training
``` python
import os.path

import numpy as np
import pykitml as pk
from pykitml.datasets import mnist
    
# Download dataset
if(not os.path.exists('mnist.pkl')): mnist.get()

# Load dataset
training_data, training_targets, testing_data, testing_targets = mnist.load()
    
# Create a new neural network
digit_classifier = pk.NeuralNetwork([784, 100, 10])
    
# Train it
digit_classifier.train(
    training_data=training_data,
    targets=training_targets, 
    batch_size=50, 
    epochs=1200, 
    optimizer=pk.Adam(learning_rate=0.012, decay_rate=0.95), 
    testing_data=testing_data, 
    testing_targets=testing_targets,
    testing_freq=30,
    decay_freq=15
)
    
# Save it
pk.save(digit_classifier, 'digit_classifier_network.pkl')

# Show performance
accuracy = digit_classifier.accuracy(training_data, training_targets)
print('Train Accuracy:', accuracy)        
accuracy = digit_classifier.accuracy(testing_data, testing_targets)
print('Test Accuracy:', accuracy)
    
# Plot performance graph
digit_classifier.plot_performance()

# Show confusion matrix
digit_classifier.confusion_matrix(training_data, training_targets)
```

### Trying the model
```python
import random

import numpy as np
import matplotlib.pyplot as plt
import pykitml as pk
from pykitml.datasets import mnist

# Load dataset
training_data, training_targets, testing_data, testing_targets = mnist.load()

# Load the trained network
digit_classifier = pk.load('digit_classifier_network.pkl')

# Pick a random example from testing data
index = random.randint(0, 9999)

# Show the test data and the label
plt.imshow(training_data[index].reshape(28, 28))
plt.show()
print('Label: ', training_targets[index])

# Show prediction
digit_classifier.feed(training_data[index])
model_output = digit_classifier.get_output_onehot()
print('Predicted: ', model_output)
```

### Performance Graph

![Performance Graph](https://raw.githubusercontent.com/RainingComputers/pykitml/master/docs/demo_pics/neural_network_perf_graph.png)

### Confusion Matrix

![Confusion Matrix](https://raw.githubusercontent.com/RainingComputers/pykitml/master/docs/demo_pics/neural_network_confusion_matrix.png)
osmanthus 0.1.4
An algorithmic chess engine, written in Python.
Requires Python: >=3.9
Keywords: chess

[![build status](https://github.com/i13e/osmanthus/actions/workflows/main.yml/badge.svg)](https://github.com/i13e/osmanthus/actions/workflows/main.yml)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/i13e/osmanthus/master.svg)](https://results.pre-commit.ci/latest/github/i13e/osmanthus/master)

![](https://github.com/i13e/osmanthus/assets/62034540/493798b0-39c1-4566-8b99-83941e2e6994)

# Osmanthus

Osmanthus is a sophisticated chess engine that employs advanced algorithms to provide a challenging
and enjoyable playing experience. It offers a range of features, including self play, puzzle
solving, and an intuitive command-line interface.

## Installation

Osmanthus can be easily installed using pip:

```sh
pip install osmanthus
```

## Usage

To start the CLI interface, enter the following command:

```sh
osmanthus-cli
```

Moves can be input in Standard Algebraic Notation (SAN) or Universal Chess Interface notation. We
recommend using SAN. If you are new to this notation, we have provided a short guide on our [wiki
page](https://github.com/i13e/osmanthus/wiki/san).

If you are new to playing chess, we suggest checking out [this
video](https://www.youtube.com/watch?v=OCSbzArwB10) to learn the fundamentals.

## Contributing

Contributions to Osmanthus are highly appreciated! For suggestions, we recommend looking at any open
issues, particularly those tagged as "Good First Issue" if you are a first-time contributor. We are
also opening new issues related to planned features and bugs that need to be fixed.

When contributing, we ask that you follow the coding style laid out in [Google's Python Style
Guide](https://google.github.io/styleguide/pyguide.html).

Thank you for your interest in Osmanthus, and we look forward to your contributions!

<!-- 谢谢，李桂花。我愛你 -->
tiktok_info 0.1.2
TikTok Info: Module for retrieving and processing information from TikTok.

# TikTokInfo

**TikTokInfo** is a Python library and command-line tool for fetching TikTok video metadata, including the author, title, thumbnail, and avatar.

---

## 📦 **Installation**

```bash
pip install tiktok_info
```
## **run**

run manually using the command:
```
tiktok_info -url <url_tiktok> -help
```
run using the script:
```
from tiktok_info import TikTokInfo

url = "https://vm.tiktok.com/vid_id/"
tiktok = TikTokInfo(url)

print("Author:", tiktok.author())
print("Title:", tiktok.title())
print("Thumbnail:", tiktok.thumbnail())
print("Avatar:", tiktok.avatar())
print("Endpoint URL:", tiktok.endpoint())
recaptcha-solver-v2 1.0.2
A Python module to solve reCAPTCHA using audio challenges.
Requires Python: >=3.6

# Recaptcha Solver

This package provides a solution for solving Google reCAPTCHA challenges using audio recognition.

## Installation

Install the package via pip:

```bash
pip install recaptcha_solver_v2
```


## Example Usage

`recaptcha_solver_v2` module:

```python
from DrissionPage import ChromiumPage
from recaptcha_solver_v2 import RecaptchaSolver
import time

driver = ChromiumPage()
recaptchaSolver = RecaptchaSolver(driver)

driver.get("https://www.google.com/recaptcha/api2/demo")

t0 = time.time()
recaptchaSolver.solveCaptcha()
print(f"Time to solve the captcha: {time.time()-t0:.2f} seconds")

driver.ele("#recaptcha-demo-submit").click()

driver.close()
```