semvec/pypi-packages huggingface dataset:
dscribeai 0.0.3
dScribe AI is an API to extract transcriptions and visual summaries from social media videos like TikTok, YouTube, Instagram, and X.
Requires Python: >=3.10

# dScribe AI

## Overview

[dScribe AI](https://dscribeai.com?utm_source=pypi) helps developers turn social media videos into actionable insights by extracting transcriptions, generating video summaries, and enabling direct question-answering from video content.

## Features

- **Video Transcription**: Extract text from video content across multiple languages
- **Content Analysis**: Analyze and generate insights from videos with simple prompts
- **Multiplatform Support**: Process content from YouTube, TikTok, X, Instagram, Facebook, Spotify, Apple Podcasts, and more
- **Multilingual Support**: Process content in 16+ languages
- **Real-time Processing**: Get insights on the fly without waiting for indexing
- **Structured Output**: Receive structured insights in markdown format, ready for AI applications

## Installation

```bash
pip install dscribeai
```

## Quick Start

```python
from dscribeai import DScribeClient

# Initialize client with your API key
client = DScribeClient(api_key="your_api_key_here")

post_url = "https://www.youtube.com/watch?v=UF8uR6Z6KLc"
# Process a social media video
results = client.transcribe(url=post_url)

# Access transcription
transcript = results.transcript
print(f"Transcript: {transcript}")

# Ask specific questions about video content
answer = client.analyze(
    url=post_url,
    prompt="What are the main themes of this video?"
)
print(f"Answer: {answer.query_result.answer}")
```

## Use Cases

- **Social Listening**: Monitor brand mentions and sentiment across social platforms
- **Content Analysis**: Extract key topics and opinions from video content
- **Engagement Tracking**: Measure audience interaction with content
- **AI Agents**: Power your AI assistants with video understanding capabilities

## Documentation

For comprehensive documentation, visit our [official documentation](https://docs.dscribeai.com?utm_source=pypi).

## Authentication

You'll need an API key to use dScribe AI. Sign up at [dscribeai.com](https://dscribeai.com?utm_source=pypi) to get your API key.

## Support

- **Email**: jordan@dscribeai.com
- **Discord**: [Join our community](https://discord.gg/5AwtMsddXq)
payload42 0.0.1
payload
Requires Python: >=3.9
Keywords: payload, payload42

# Payload42

PAYLOAD: Payload Abstraction Yields Layered Ordered Annotated Data

The `payload42` module provides the reference implementation of Payload42, a
type-length-value (TLV) encoding scheme designed for misuse-resistant
serialization in network protocols.

# What is Payload42?

Payload42 is type length value (TLV) encoding scheme designed to be misuse
resistant when used for serializing data sent by network protocols.

## Status of `payload42` implementing Payload42

This is pre-release software, under active development.

# How to use Payload42?

Python applications using network communication can encode messages using the
provided pure functions, producing structured, self-synchronizing payloads and
corresponding misuse-resistant decoders.
aipowershell 0.2.0
An AI-powered smart shell with command suggestions and natural language processing
Requires Python: >=3.6
Keywords: ai shell cli powershell assistant openai deepseek

# AI Power Shell

An AI-powered smart shell with command suggestions and natural language processing.

## What's New in 0.2.0
- Improved error handling and analysis
- Better command suggestions
- Support for project structure analysis
- Enhanced natural language processing
- Performance improvements

## Installation

```bash
pip install aipowershell
```

## Usage

1. Set your OpenRouter API key in your environment:
```bash
export DEEPSEEK_API="your-api-key-here"
```

2. Run the shell:
```bash
aipowershell
```

## Features

- AI-powered command suggestions
- Natural language command processing (prefix with ?)
- Error analysis and fixing
- Project structure analysis
- Improved command completion
- Better error handling

## Examples

```bash
$ ?show current directory  # Will translate to: pwd
$ ?list all files         # Will translate to: ls -la
$ !error ModuleNotFoundError: No module named 'pandas'  # Will analyze and fix the error
```

## Requirements
- Python 3.6+
- OpenAI API key or OpenRouter API key
pylexflp 0.1.3
A Python Package for Fuzzy Linear Programming with the Lexicographic Method
Requires Python: >=3.7
Keywords: Fuzzy Optimization, Fuzzy Linear Programming, Fuzzy Set, Fuzzy Number, Operations Research

# PyLexFLP

**PyLexFLP** is a Python package for solving **Fuzzy Linear Programming** problems using the [**Lexicographic Method**](https://doi.org/10.1016/j.eswa.2019.01.041). This tool is designed to help researchers and practitioners in **Operations Research** and **Fuzzy Optimization** to model and solve fuzzy optimization problems effectively.

## Features

- Solves fuzzy linear programming models in which the coefficients are arbitrary triangular fuzzy numbers and the decision variables are non-negative triangular fuzzy numbers.
- Utilizes **Lexicographic Ranking** criteria for ordering triangular fuzzy numbers.
- Built on top of the [**PuLP**](https://github.com/coin-or/pulp) optimization framework.

## Installation

You can install PyLexFLP from GitHub:

```bash
pip install git+https://github.com/bpcanedo/pylexflp.git
```

Or, from PyPI:

```bash
pip install pylexflp
```

## Requirements

- **Python** \>= 3.7
- **PuLP** \>= 2.6
- **SciPy** \>= 1.21

## Usage

Here's a simple example of how to use PyLexFLP:

```python
from pylexflp import FLP, TFN_Var, TFN, flpMaximize, getSolver

# Lexicographic ranking method
def f1(x): return (x.al+2*x.am+x.au)/4
def f2(x): return x.am
def f3(x): return x.au-x.al
criteria = [f1, f2, f3]

# The problem
p = FLP(criteria=criteria, sense=flpMaximize)

# Variable definition
x1, x2, I1, I2, I3 = TFN_Var("x1"), TFN_Var("x2"), TFN_Var("I1"), TFN_Var("I2"), TFN_Var("I3")

# Let PyLexFLP know the variables
p += x1
p += x2
p += I1
p += I2
p += I3

# Insert the constraints
p += I1 + TFN(0.15,0.20,0.25)*I1 == x1+I2
p += I2 + TFN(0.10,0.15,0.20)*I2 == x2+I3
p += I1 == TFN(950, 1000, 1050)
p += I3 >= TFN(622, 624, 627)

# The last expression or variable entered becomes the objective function
p += x1 + x2

# Select the linear programming solver
# msg = True if you want verbosity
solver = getSolver("COIN_CMD", msg=False)

# Solve the problem
# status = [1, 1, 1] means the three criteria were successfully optimized
status = p.solve(solver=solver)

# Fuzzy proposition example
## Membership function
def High(x): return max(0, min(1, (x-500)/500))

# Print the solution
solution = f"""
Status: {status}
Solution:
    x1 = {x1}
    x2 = {x2}
    obj = {x1+x2}
    I1 = {I1}
    I2 = {I2}
    I3 = {I3}
    # Fuzzy proposition
    x2 is High = {x2.IS(High)}
"""
print(solution)
```

## Documentation

Detailed documentation and examples can be found on the [GitHub
repository](<https://github.com/bpcanedo/pylexflp>).

## Contributing

Contributions are welcome! Please feel free to submit issues or pull
requests to the [GitHub repository](<https://github.com/bpcanedo/pylexflp>).

## License

This project is licensed under the MIT License. See the LICENSE file for
details.

## Authors

**Boris Pérez-Cañedo**

Email: <bpcanedo@gmail.com>

GitHub: [bpcanedo](<https://github.com/bpcanedo>)

**Eduardo René Concepción-Morales**

Email: <econcep@ucf.edu.cu>

**José Luis Verdegay**

Email: <verdegay@decsai.ugr.es>
ld-ipc.py 0.0.2
Python SDK for LDPlayer IPC.
Requires Python: >=3.6

<div align="center">

# ld-ipc.py

Python SDK for LDPlayer IPC.

[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/EvATive7/ld-ipc.py/package.yml)](https://github.com/EvATive7/ld-ipc.py/actions)
[![Python](https://img.shields.io/pypi/pyversions/ld-ipc.py)](https://pypi.org/project/ld-ipc.py)
[![PyPI version](https://badge.fury.io/py/ld-ipc.py.svg)](https://pypi.org/project/ld-ipc.py)
[![Coverage Status](https://coveralls.io/repos/EvATive7/ld-ipc.py/badge.svg?branch=develop&service=github)](https://coveralls.io/github/EvATive7/ld-ipc.py?branch=master)
[![License](https://img.shields.io/github/license/EvATive7/ld-ipc.py.svg)](https://pypi.org/project/ld-ipc.py/)

</div>

## Usage

Install the package using pip: `pip install ld-ipc.py`  

```python
from pathlib import Path

from PIL import Image
from ldipc import LDPlayer

# Set the path and index of LDPlayer
ldplayer_path = Path(r"C:\leidian\LDPlayer9")  # Replace with your LDPlayer path
player_index = 0  # Replace with the index of players you want to capture

try:
    # Create an LDPlayer instance
    ld_player = LDPlayer(ldplayer_path, player_index)

    # capture screenshots
    screenshot_array = ld_player.capture()

    # Convert numpy array to PIL image and save
    screenshot_image = Image.fromarray(screenshot_array)
    screenshot_image.save("screenshot.png")

    print("Screenshot has been saved as screenshot.png")
except Exception as e:
    print(f"Error capturing screenshot: {e}")

```

## Platform

Only Windows is supported.
speedclickpro 1.0.7
Advanced auto-clicker with animation sequencing capabilities
Requires Python: >=3.7

# SpeedClick Pro (สปีดคลิก โปร)

โปรแกรมออโต้คลิกเกอร์ขั้นสูงพร้อมความสามารถในการจัดลำดับอนิเมชัน

## คุณสมบัติ

- **คลิกความเร็วสูง** - คลิกได้เร็วที่สุดเท่าที่ระบบของคุณจะรองรับได้
- **ลำดับอนิเมชัน** - สร้างลำดับการคลิกตามเวลาที่กำหนดในตำแหน่งต่างๆ
- **การจัดการตำแหน่ง** - บันทึก, แก้ไข และโหลดตำแหน่งการคลิก
- **รองรับหลายภาษา** - มีให้ใช้ในภาษาไทย, อังกฤษ, จีน, ญี่ปุ่น และเกาหลี
- **ระบบโปรไฟล์** - บันทึกและโหลดการตั้งค่าที่แตกต่างกัน

## วิธีติดตั้ง

### วิธีที่แนะนำ (ใช้งานได้ทันทีใน cmd):

```bash
# ติดตั้ง pipx ครั้งแรก

pip install pipx
pipx ensurepath

# ติดตั้ง SpeedClick Pro
pipx install speedclickpro
```

### วิธีทั่วไป:

```bash
pip install speedclickpro
```

**หมายเหตุ**: การติดตั้งแบบทั่วไปด้วย pip อาจต้องการการตั้งค่าเพิ่มเติมสำหรับการเรียกใช้ใน cmd

## วิธีใช้งาน

หลังจากติดตั้งแล้ว คุณสามารถเริ่มใช้งาน SpeedClick Pro ได้โดยการรัน:

```bash
speedclickpro
```

**หมายเหตุ**: หากคำสั่ง `speedclickpro` ไม่ทำงาน คุณอาจต้องเพิ่ม Python Scripts ไปยัง PATH ของระบบ:

### สำหรับ Windows:
1. เปิด "System Properties" (คลิกขวาที่ This PC > Properties > Advanced system settings)
2. คลิกที่ "Environment Variables"
3. เลือก "Path" ใน User variables แล้วคลิก "Edit"
4. คลิก "New" และเพิ่ม `C:\Users\<ชื่อผู้ใช้>\AppData\Roaming\Python\Python310\Scripts`
5. คลิก OK และรีสตาร์ท command prompt

หรืออีกทางเลือก คุณสามารถใช้ path เต็มในการรัน:
```bash
%USERPROFILE%\AppData\Roaming\Python\Python310\Scripts\speedclickpro.exe
```

หรือจากไพธอน:

```python
from speedclickpro import main
main()
```

## คู่มือเริ่มต้นอย่างรวดเร็ว

1. เปิดโปรแกรม SpeedClick Pro
2. คลิกที่ "เพิ่มตำแหน่ง" เพื่อกำหนดตำแหน่ง
3. เลือกระหว่างโหมดปกติหรือโหมดลำดับอนิเมชัน
4. เริ่มคลิกด้วยปุ่ม "เริ่มคลิก" หรือกด Ctrl+G

## ลิขสิทธิ์

MIT License

## เกี่ยวกับ

SpeedClick Pro ถูกออกแบบมาเป็นเครื่องมือออโต้คลิกแบบมืออาชีพพร้อมความสามารถในการจัดลำดับอนิเมชัน ทำให้เหมาะอย่างยิ่งสำหรับการทำงานซ้ำๆ อัตโนมัติ