semvec/pypi-packages huggingface dataset:
finsenti 0.1.0
A package for financial sentiment analysis using news articles.
License: MIT
Requires Python: >=3.7

# FinSenti

FinSenti is a comprehensive financial sentiment analysis library designed to help users analyze and interpret sentiment in financial texts.

It uses financial RoBERTa models to provide accurate sentiment analysis for financial news articles.

## Features
- **Financial Sentiment Analysis**: Analyze sentiment in financial texts using state-of-the-art RoBERTa models.
- **Compound Score Aggregation**: Aggregate sentiment scores to provide a comprehensive view of sentiment over time.

## Installation

```bash
pip install finsenti
```

## Usage

1. Preprocess your financial text data.

```python
import pandas as pd
from finsenti import finsenti_pipeline
# Input DataFrame df with a 'body' column and a list of tickers
df = pd.DataFrame({'body': ["The stock market is bullish today.", 
                             "Economic downturn expected next quarter.",
                            "Google's new product launch boosts investor confidence."]})
tickers = ['AAPL', 'GOOGL']
df = finsenti_pipeline(tickers=tickers, df=df,text_column='body', gemini_api_key='your_api_key', aggregate=True, aggregation_method='mean')
print(df.info())
```

## Dependencies
- pandas
- marketminer
- python-dotenv
- google-genai
- transformers
- torch
- hf_xet
- scipy

## License

MIT License. Free to use and modify.
video-compressor-mcp 0.1.1
MCP server for video compression and optimization: bitrate, resolution, frame rate, and codec adjustment
License: MIT
Requires Python: >=3.12
Keywords: bitrate, codec, compression, ffmpeg, mcp, optimization, video

# Video Compressor MCP

A Model Context Protocol (MCP) server for video compression and optimization: bitrate, resolution, frame rate, and codec adjustment.

## Features

- **Bitrate Control**: Adjust video bitrate for size optimization
- **Resolution Scaling**: Change video resolution with aspect ratio preservation
- **Frame Rate Adjustment**: Modify video frame rates
- **Codec Selection**: Choose optimal video codecs for different use cases
- **Smart Fallback**: Automatic audio copy with re-encoding fallback

## Installation

Install via uvx (recommended):

```bash
uvx video-compressor-mcp
```

Or install via pip:

```bash
pip install video-compressor-mcp
```

## Usage

Run the MCP server:

```bash
video-compressor-mcp
```

## Requirements

- Python >=3.12
- FFmpeg installed on your system

## Tools Available

1. `set_video_bitrate` - Adjust video bitrate
2. `set_video_resolution` - Change video resolution
3. `set_video_frame_rate` - Modify frame rate
4. `set_video_codec` - Change video codec

## License

MIT License
coally 0.1.4
Coally — your Ally for Google Colab. Zero-setup toolkit at hand.
License: MIT
Requires Python: >=3.8

# Coally

Coally — your Colab Ally. Modular helpers for data tasks in notebooks.  
Explore CSV/XLSX with SQL directly in Google Colab & Drive — no databases, no local setup.

[![PyPI](https://img.shields.io/pypi/v/coally.svg)][pypi status]
[![Status](https://img.shields.io/pypi/status/coally.svg)][pypi status]
[![Python Version](https://img.shields.io/pypi/pyversions/coally)][pypi status]
[![License](https://img.shields.io/pypi/l/coally)][license]

[![Read the documentation at https://coally.readthedocs.io/](https://img.shields.io/readthedocs/coally/latest.svg?label=Read%20the%20Docs)][read the docs]
[![Tests](https://github.com/marcoderspace/coally/workflows/Tests/badge.svg)][tests]

[![Tests](https://github.com/marcoderspace/coally/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/marcoderspace/coally/actions/workflows/tests.yml?query=branch%3Amain)

[![Codecov](https://codecov.io/gh/marcoderspace/coally/branch/main/graph/badge.svg)][codecov]

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)][pre-commit]
[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)][black]

## Features

- `coally-sql`: run ad-hoc SQL over CSV/XLSX directly in Google Colab.

## Requirements

- Python 3.8+

## Installation

You can install _Coally_ via [pip] from [PyPI]:

```bash
pip install coally
```

## Quick start (Colab)

```python
from coally import AllySequelUI, UIOptions

# Full UI: drag & drop CSV/XLSX, run SQL, download result
AllySequelUI.show()
```

## Contributing

Contributions are very welcome. See the [Contributor Guide].

## License

Distributed under the terms of the [MIT license][license], _Coally_ is free and open source software.

## Issues

If you encounter any problems, please [file an issue] with details.

---

[pypi status]: https://pypi.org/project/coally/
[read the docs]: https://coally.readthedocs.io/
[tests]: https://github.com/marcoderspace/coally/actions?workflow=Tests
[codecov]: https://app.codecov.io/gh/marcoderspace/coally
[pre-commit]: https://github.com/pre-commit/pre-commit
[black]: https://github.com/psf/black
[license]: https://github.com/marcoderspace/coally/blob/main/LICENSE
[Contributor Guide]: https://github.com/marcoderspace/coally/blob/main/CONTRIBUTING.md
[file an issue]: https://github.com/marcoderspace/coally/issues
[pip]: https://pip.pypa.io/