semvec/pypi-packages huggingface dataset:
predictramdatalab 1.0.7
A Python API for accessing and analyzing stock market data
Requires Python: >=3.8
Keywords: stocks finance market data analysis

# Predictram DataLab

A comprehensive Python API for stock market data analysis and visualization.

## Features

- Natural language query interface
- Stock screening and filtering
- Financial metrics analysis
- Data visualization tools
- Excel export capabilities

## Installation

```bash
pip install predictramdatalab
streamlit_lyrics_sync 0.2.0
A compoennt for syncing lyrics to an audio waveform
Requires Python: >=3.9

# streamlit_lyrics_sync

A compoennt for syncing lyrics to an audio waveform

## Installation

```bash
pip install streamlit_lyrics_sync
```


## Development

To develop the component, run the following command:

```bash
streamlit run streamlit_lyrics_sync/__init__.py
```

Then run the frontend development server:

```bash
cd streamlit_lyrics_sync/frontend
npm run dev
```

This will start the streamlit development server.

## Building

To build the component, run the following command:
mcp-video2text 0.1.2
Convert online video to text, read it quickly
Requires Python: >=3.10

# mcp-video2text

Convert online video to text, read it quickly

![screenshoot](https://raw.githubusercontent.com/yanyaoer/mcp-video2text/refs/heads/main/screenshoot/example.png)

## Usage

```bash
## require
$ curl -LsSf https://astral.sh/uv/install.sh | sh  # install uv package manager
$ brew install ffmpeg  # or `sudo apt install ffmpeg` for linux

## install
$ uv pip install mcp-video2text[mlx]  # recommend with mlx for silicon machine
$ uv pip install mcp-video2text[cpp]  
```

for mcp client, pass `uv run mpc-video2text` to `command` field.

```json
{
  "mcpServers": {
    "mcp-video2text": {
      "command": "uv",
      "args": ["run", "mcp-video2text"],
    }
  }
}
```

The first run will download the Whisper model and may take a few minutes.  
It's recommended to update the timeout settings in your client.

or your can custom your own client, view example `test_mcp_client.py`

```python
import asyncio
import os

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client


async def client():
  server_params = StdioServerParameters(
    command="uv", args=["run", "mcp-video2text"], env=os.environ
  )
  async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
      await session.initialize()
      print(await session.list_tools())
      result = await session.call_tool(
        "video2text", {"url": "https://www.bilibili.com/video/BV1gdERzuEYB/"}
      )
      print(result.content[0].text)


if __name__ == "__main__":
  asyncio.run(client())
```

## Thanks
- https://github.com/yt-dlp/yt-dlp
- https://github.com/k2-fsa/sherpa-onnx
- https://github.com/ml-explore/mlx-examples/tree/main/whisper
- https://github.com/ggerganov/whisper.cpp
- https://github.com/absadiki/pywhispercpp
- https://github.com/pydantic/pydantic-ai
- https://github.com/yetone/avante.nvim
simple-mcp-server 0.1.0
A simple MCP server with hello and dice rolling tools
Requires Python: >=3.7

# Simple MCP Server

A simple MCP server implementation with two example tools:

1. `say_hello(name: str) -> str` - Returns a greeting message
2. `roll_dice(sides: int = 6) -> int` - Returns a random dice roll

## Installation

```bash
pip install simple-mcp-server
```

## Usage

After installation, run:

```bash
simple-mcp-server
```

The server will start and be ready to accept MCP connections.
gaussian-suite 1.14.2
A package for working with Gaussian input and output files
Requires Python: >=3.10

# gaussian_suite

`gaussian_suite` is a python package for working with Gaussian input and output files

# Installation via `pip`

Install `gaussian_suite` using `pip` (if using a shared machine, add the `--user` argument after `install`)

```
pip install gaussian_suite
```

# Updating

Update the code using `pip` (if using a shared machine, add the `--user` argument after `install`)

```
pip install gaussian_suite --upgrade
```

# Installation with `pip` editable install

**Only do this if you are developing (i.e. changing) the code.**

Clone a copy of this repository, preferably while within a directory called git

```
mkdir -p git; cd git
git clone https://gitlab.com/chilton-group/gaussian_suite
```

Navigate to the package directory

```
cd gaussian_suite/packages
```

and install the package in editable mode (if using a shared machine, add the `--user` argument after `install`)

```
pip install -e .
```

When you're done developing (i.e. your changes have been merged to the master), 
or if you just want to use the current release version of the package, uninstall using `pip`

```
pip uninstall gaussian_suite
```

and follow the Installation via `pip` instructions above.

# Building a `.whl` file (Advanced)

**Only do this if you are told to.**

To build a copy of the `gaussian_suite` `.whl` file, move to the `package` directory.

Now run

```
./build_binaries.sh
```

Then install the `.whl` file with `pip` (if using a shared machine, add the `--user` argument after `install`)

```
pip install dist/*.whl
```


# Documentation

The [documentation](https://chilton-group.gitlab.io/gaussian_suite/) for this package is hosted by gitlab, and is automatically generated whenever new code is committed to the `main` branch. The automatic generation of this documentation relies on a common layout for comments and docstrings within the code, see [contributing](https://gitlab.com/chilton-group/group-wiki/-/wikis/Contributing:-General) for more information.

# Development

Before making changes to this repository, please follow the steps outlined in the [Chilton group wiki](https://gitlab.com/chilton-group/group-wiki/-/wikis/Contributing:-General).

# Bugs

If you believe you have a bug, *please check that you are using the most up to date version of the code*. 

If that does not fix the problem, please create an issue on GitLab detailing the following:
 - The commands you entered
 - The error message

Remember to simplify the problem as much as possible in order to provide a minimum working example, e.g. an example for a small molecule rather than one with 100000 atoms.

Then, look at the code, try and figure out what you think is wrong if possible, and include this in your issue.