semvec/pypi-packages huggingface dataset:
tikfo 0.0.1
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.

---

```bash
pip install tiktok_info
```
```bash
tiktok_get -url <url_tiktok> -help
```
```bash
#run using the script

from tiktok_get_info import TiktokGetInfo
url = "https://vm.tiktok.com/vid_id/"
tiktok = TiktokGetInfo(url)

print("Author:", tiktok.author())
print("Title:", tiktok.title())
print("Username:", tiktok.username())
print("Thumbnail:", tiktok.thumbnail())
print("Avatar:", tiktok.avatar())
print("Endpoint URL:", tiktok.endpoint())
crypto-price-mcp 0.1.0
一个基于MCP的加密货币价格查询服务器
Requires Python: >=3.7

# Crypto Price MCP

![License](https://img.shields.io/badge/license-MIT-blue.svg)

这是一个基于Model Context Protocol (MCP)的加密货币价格查询服务器。它提供了各种工具来获取虚拟币价格、市场趋势、详细信息和K线数据。

## 目录

* [安装](#安装)
* [使用方法](#使用方法)
* [工具](#工具)
* [资源](#资源)
* [许可证](#许可证)

## 安装

### 通过pip安装

```bash
pip install crypto-price-mcp
```

### 手动安装

克隆仓库并安装依赖：

```bash
git clone https://github.com/yourusername/crypto-price-mcp.git
cd crypto-price-mcp
pip install -r requirements.txt
```

## 使用方法

### 直接运行

安装后，可以直接使用以下命令运行MCP服务器：

```bash
crypto-price-mcp
```

### 在Claude桌面应用中配置

将以下配置添加到Claude桌面客户端的配置文件中：

```json
{
  "mcpServers": {
    "crypto-price": {
      "command": "crypto-price-mcp",
      "args": [],
      "env": {}
    }
  }
}
```

### 在Cursor中配置

将以下配置添加到`~/.cursor/mcp.json`文件中：

```json
{
  "mcpServers": {
    "crypto-price": {
      "command": "crypto-price-mcp",
      "args": [],
      "env": {}
    }
  }
}
```

### 在Windsurf中配置

将以下配置添加到`./codeium/windsurf/model_config.json`文件中：

```json
{
  "mcpServers": {
    "crypto-price": {
      "command": "crypto-price-mcp",
      "args": [],
      "env": {}
    }
  }
}
```

## 工具

* `get_coin_price` - 获取指定虚拟币的当前价格
* `get_trending_coins` - 获取当前热门虚拟币列表
* `get_coin_detail` - 获取虚拟币的详细信息
* `get_global_market_data` - 获取全球加密货币市场数据
* `search_coins` - 搜索虚拟币
* `get_common_coins_prices` - 获取常见虚拟币的价格信息
* `get_k_line_data` - 获取虚拟币的K线数据

## 资源

* [CoinGecko API](https://www.coingecko.com/en/api)
* [Bitget API](https://bitgetlimited.github.io/apidoc/en/spot)
* [Model Context Protocol](https://github.com/hyperbrowserai/mcp)

## 许可证

此项目采用MIT许可证。
st-file-uploader 0.2.2
A customizable file uploader component for Streamlit
Requires Python: >=3.7

# st_file_uploader

[![image](https://img.shields.io/pypi/v/st_file_uploader.svg)](https://pypi.python.org/pypi/st_file_uploader)
[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://st-fileuploader.streamlit.app/)

This is a custom component that allows you to split files and send them from your browser to Streamlit.

## Installation instructions

```sh
pip install st_file_uploader
```

## Usage instructions

```python
import streamlit as st
import st_file_uploader as stf

# Set page title and description
st.title("Custom File Uploader Demo")
st.write("This demo shows different ways to customize the file uploader component.")

# Using fully custom version
st.subheader("Fully Custom Version")
custom = stf.create_custom_uploader(
    uploader_msg="Drop your amazing file here!",
    limit_msg="Maximum size is 200MB",
    button_msg="Select File",
    icon="MdFileUpload"
)

file_custom = custom.file_uploader(
    "Upload with custom text",
    type=["xlsx", "csv"],
    accept_multiple_files=True,
)

# Basic usage (English default)
st.subheader("Basic Usage (Default English)")
file = stf.file_uploader(
    "Upload a CSV file",
    type="csv",
)

# Using Spanish version
st.subheader("Spanish Version")
file_es = stf.es.file_uploader(
    "Sube un archivo CSV",
    type="csv",
)

# Mix of language with custom overrides
st.subheader("French with overrides")
file_fr = stf.file_uploader(
    "Télécharger un fichier",
    type=["jpg", "png", "gif"],
    accept_multiple_files=True,
    button_msg="Sélectionner une image",
)
    
# Show multiple types
st.subheader("Multiple file types demo")
file_types = stf.file_uploader(
    "Upload documents",
    type="csv",
    accept_multiple_files=True,
)
```
libspeech 0.0.0
Best library for speech
Requires Python: >=3.8

<p align="center">
  <a href="https://github.com/mohammadraziei/libspeech">
    <img src="https://github.com/MohammadRaziei/libspeech/raw/master/docs/images/logo/libspeech-logo-1.svg" alt="Logo">
  </a>
  <h3 align="center">

[//]: # (    Fastest domain extractor library written in C++ with python binding.)
  </h3>
  <h4 align="center">

[//]: # (    First and complete library for parsing url in C++ and Python and Command Line)
  </h4>
</p>

[![mohammadraziei - libspeech](https://img.shields.io/static/v1?label=mohammadraziei&message=libspeech&color=white&logo=github)](https://github.com/mohammadraziei/libspeech "Go to GitHub repo")
[![stars - libspeech](https://img.shields.io/github/stars/mohammadraziei/libspeech?style=social)](https://github.com/mohammadraziei/libspeech)
[![forks - libspeech](https://img.shields.io/github/forks/mohammadraziei/libspeech?style=social)](https://github.com/mohammadraziei/libspeech)

[![PyPi](https://img.shields.io/pypi/v/libspeech.svg)](https://pypi.org/project/libspeech/)
![Python](https://img.shields.io/badge/Python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)
![Cpp](https://img.shields.io/badge/C++-17-blue)


[![GitHub release](https://img.shields.io/github/release/mohammadraziei/libspeech?include_prereleases=&sort=semver&color=purple)](https://github.com/mohammadraziei/libspeech/releases/)
[![License](https://img.shields.io/badge/License-MIT-purple)](#license)
[![issues - libspeech](https://img.shields.io/github/issues/mohammadraziei/libspeech)](https://github.com/mohammadraziei/libspeech/issues)


[![SonarCloud](https://sonarcloud.io/images/project_badges/sonarcloud-white.svg)](https://sonarcloud.io/summary/new_code?id=MohammadRaziei_libspeech)

[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=MohammadRaziei_libspeech&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=MohammadRaziei_libspeech)
[![CodeFactor](https://www.codefactor.io/repository/github/mohammadraziei/libspeech/badge/master)](https://www.codefactor.io/repository/github/mohammadraziei/libspeech/overview/master)
[![snyk.io](https://snyk.io/advisor/python/libspeech/badge.svg)](https://snyk.io/advisor/python/libspeech)

[//]: # ([![View site - GH Pages]&#40;https://img.shields.io/badge/View_site-GH_Pages-2ea44f?style=for-the-badge&#41;]&#40;https://mohammadraziei.github.io/libspeech/&#41;)



## Stats
[![Stars](https://starchart.cc/mohammadraziei/libspeech.svg?variant=adaptive)](https://starchart.cc/mohammadraziei/libspeech)

## Contact

<!-- Gal Ben David - gal@intsights.com -->

Project Link:
- [https://github.com/mohammadraziei/libspeech](https://github.com/mohammadraziei/libspeech)
- [https://pypi.org/project/libspeech](https://pypi.org/project/libspeech)



[license-shield]: https://img.shields.io/github/license/othneildrew/Best-README-Template.svg?style=flat-square
fastarithmetic 2.0.0
Optimized arithmetic operations for large numbers with high-speed algorithms.
Requires Python: >=3.8


fastarithmetic is a Python package designed for fast and efficient arithmetic operations on very large numbers.
It implements optimized algorithms like Karatsuba multiplication and Newton-Raphson division to provide
significantly faster performance than Python's native arithmetic operators.

### Features
- **Fast Addition:** Performs addition of large numbers in O(1) time complexity.
- **Fast Subtraction:** Performs subtraction of large numbers in O(1) time complexity.
- **Optimized Multiplication:** Uses the Karatsuba algorithm (O(n^1.58)) for large integer multiplication.
- **High-Speed Division:** Implements the Newton-Raphson method (O(M(n))) for efficient division.

### Use Cases
- Scientific computations with extremely large integers.
- Cryptography where high precision and fast operations are required.
- Big data applications involving large-scale numerical processing.

### Installation
To install fastarithmetic, run the following command:

    pip install fastarithmetic

### Example Usage

    from fastarithmetic import fast_add, fast_sub, fast_mul, fast_div

    a = 987654321987654321987654321
    b = 123456789123456789

    print("Addition:", fast_add(a, b))
    print("Subtraction:", fast_sub(a, b))
    print("Multiplication:", fast_mul(a, b))
    print("Division:", fast_div(a, b))

### Performance Comparison
Compared to native Python arithmetic, fastarithmetic is optimized for large numbers and provides faster computation
for multiplication and division due to the use of advanced algorithms.
IRSTD 0.0.0.1
It is a DeepLearning package to foster developed by Aohua Li
Requires Python: >=3.7

# IRSTD
## Introduction
该项目是红外小目标工具包，用于方便大家使用该工具进行训练红外小目标检测方向的模型，从而减少了研究人员的对于繁琐无关代码的使用，从而更加去关注模型本身的设计。
metaforecast 0.2.2
Meta-learning and Data-centric Forecasting
Requires Python: >=3.8
Keywords: Data Science, Forecasting, Machine Learning, Time Series

# metaforecast

[![PyPi Version](https://img.shields.io/pypi/v/metaforecast)](https://pypi.org/project/metaforecast/)
[![Documentation Status](https://readthedocs.org/projects/metaforecast/badge/?version=latest)](https://metaforecast.readthedocs.io/en/latest/?badge=latest)
[![GitHub](https://img.shields.io/github/stars/vcerqueira/metaforecast?style=social)](https://github.com/vcerqueira/metaforecast)
![Pylint](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/vcerqueira/7ad63bc9902a43eb21993a755006f5de/raw/pylint-badge.json)
[![Downloads](https://static.pepy.tech/badge/metaforecast)](https://pepy.tech/project/metaforecast)


metaforecast is a Python package for time series forecasting using meta-learning and data-centric techniques.

This package implements various techniques to improve forecasting accuracy 
based on dynamic model combination, data augmentation, and adaptive learning, building upon Nixtla’s awesome ecosystem of state-of-the-art forecasting methods.

## Features

metaforecast currently consists of three main modules:

1. **Dynamic Ensembles**: Combining multiple models with adaptive ensemble techniques.
2. **Synthetic Time Series Generation**: Creating realistic synthetic time series data for robust model training and testing. 
Includes a special callback for online data augmentation.
3. **Long-Horizon Meta-Learning**: Instance-based meta-learning for multi-step forecasting.


## Installation

You can install metaforecast using pip:

```bash
pip install metaforecast
```

### [Optional] Installation from source

To install metaforecast from source, clone the repository and run the following command:

```bash
git clone https://github.com/vcerqueira/metaforecast
pip install -e metaforecast
cd metaforecast
pre-commit install
```

## Documentation

Check the [documentation](https://metaforecast.readthedocs.io/en/latest/index.html) for 
the API reference and module descriptions. 
You can get started with a few [tutorials](https://metaforecast.readthedocs.io/en/latest/notebooks.html).

----

### **⚠️ WARNING**

> metaforecast is in the early stages of development. 
> The codebase may undergo significant changes. 
> If you encounter any issues, please report
> them in [GitHub Issues](https://github.com/vcerqueira/metaforecast/issues)
pyecharts-json-render 0.0.3
A simple wrapper using pyecharts to render html from Echarts option(JSON).

You can use Echarts option JSON (similar way in JavaScript) directly to make Echarts html.Usage:
from pyecharts_json_render import render
jsn = render.read_json_file('./TEST_JSON.json')
render.render_html_from_ec_option_json(jsn)

option = render.pyecharts_html_to_echarts_option(html)
render.render_html_from_ec_option_json(option)