semvec/pypi-packages huggingface dataset:
trax-tui 0.8.0
TrackingTime TUI
Requires Python: >=3.12
Keywords: trackingtime, time-tracking, tui, terminal, cli

# trax-tui

<p align="center">
  <img src="https://codeberg.org/jax/trax-tui/raw/branch/main/assets/trax_tui_avatar.png" alt="trax-tui" width="200">
  <br>
  A TUI for <a href="https://trackingtime.co">TrackingTime</a>.
</p>

## Installation

```shell
pipx install trax-tui
```

Requires a keyring backend (e.g., GNOME Keyring, macOS Keychain, Windows Credential Manager).

## Setup

```shell
tt login
```

Enter your TrackingTime email and App Password. You can create an App Password in your TrackingTime
settings under Apps & Integrations. Credentials are stored securely in your system keyring.

## Usage

```shell
tt
```

Opens an interactive terminal interface for logging time:

- **Navigation** - `Ctrl+J`/`Ctrl+K` to cycle focus between elements
- **Week calendar** - `h`/`l` (days), `j`/`k` (weeks), `g` (today)
- **Entries list** - `j`/`k` to scroll entries
- **Global** - `h`/`l` (move day), `y` (yank event), `d` (delete event), `p` (submit form), `q` (quit)

Single keys type normally when an input is focused. `Ctrl+J`/`Ctrl+K` always works.

### Duration format

The duration field accepts `2h30m`, `90m`, etc. You can also specify a start time with `@`:

- `2h` — logs 2 hours starting after the last event (or 9am if none)
- `2h@1pm` — logs 2 hours starting at 1:00 PM
- `1h30m@14:30` — logs 1h30m starting at 2:30 PM
pydantic-media-type 0.1.0
Pydantic model for MIME/Media types
Requires Python: <4.0,>=3.10

# Pydantic Media Type

This package provides a [Pydantic](https://docs.pydantic.dev/) type that can be used
to validate [media types](https://en.wikipedia.org/wiki/Media_type). It uses the
media types from the [mimetypes](https://docs.python.org/3/library/mimetypes.html)
module of [The Python Standard Library](https://docs.python.org/3/library/).

## Installation
Install using uv:
```shell
uv add pydantic-media-type
```
or using pip:
```shell
pip install pydantic-media-type
```

## Example
```python
from pydantic import BaseModel

from pydantic_media_type import MediaType


class Model(BaseModel):
    media_type: MediaType

print(Model(media_type=MediaType("image/png")).media_type)  # Will print: image/png
Model(media_type=MediaType("invalid"))  # Will raise pydantic.ValidationError with code media_type_unsupported
```

## Development

Getting the project up and running can be achieved without much effort using Docker
Compose. Doing so ensures that there should be very little difference in
environment between all of us devs.

### Starting the project

```shell
docker compose build
docker compose run --rm pydantic-media-type sh
```

The last command gives you a shell within the container.

### Dependency management

We shouldn't have any other dependencies other than python and pydantic.

However, we do have some dev dependencies like:
- ruff
- mypy
- pytest

We will do our best to keep those up to date. Should you want to upgrade them
yourself, please do it from the shell inside the container using:
```shell
uv sync --upgrade
```
Similarly, installing new dependencies is possible using the command:
```shell
uv add name-of-the-dependency-to-install
```

### Dev tools
This project uses `ruff`, `mypy` and `pytest`.
The preferred way to run them is by using the container, for example:
```shell
docker compose build
docker compose run --rm pydantic-media-type sh
uv run ruff check
uv run mypy .
uv run pytest -v
```
sqlite-ai 0.7.59
Python prebuilt binaries for SQLite AI extension for all supported platforms and architectures.
Requires Python: >=3

## SQLite AI Python package

This package provides the sqlite-ai extension prebuilt binaries for multiple platforms and architectures.

### SQLite-AI

SQLite-AI is an extension for SQLite that brings artificial intelligence capabilities directly into the database. It enables developers to run, fine-tune, and serve AI models from within SQLite using simple SQL queries — ideal for on-device and edge applications where low-latency and offline inference are critical. The extension is actively developed by [SQLite AI](https://sqlite.ai), some API and features are still evolving.

More details on the official repository [sqliteai/sqlite-ai](https://github.com/sqliteai/sqlite-ai).

### Documentation

For detailed information on all available functions, their parameters, and examples, refer to the [comprehensive API Reference](https://github.com/sqliteai/sqlite-ai/blob/main/API.md).

### Supported Platforms and Architectures

| Platform      | Arch         | Subpackage name         | Binary name |
| ------------- | ------------ | ----------------------- | ----------- |
| Linux (CPU)   | x86_64/arm64 | sqliteai.binaries.cpu   | ai.so       |
| Linux (GPU)   | x86_64/arm64 | sqliteai.binaries.gpu   | ai.so       |
| Windows (CPU) | x86_64       | sqliteai.binaries.cpu   | ai.dll      |
| Windows (GPU) | x86_64       | sqliteai.binaries.gpu   | ai.dll      |
| macOS (CPU)   | x86_64/arm64 | sqliteai.binaries.cpu   | ai.dylib    |

## Usage

> **Note:** Some SQLite installations on certain operating systems may have extension loading disabled by default.   
If you encounter issues loading the extension, refer to the [sqlite-extensions-guide](https://github.com/sqliteai/sqlite-extensions-guide/) for platform-specific instructions on enabling and using SQLite extensions.

```python
import importlib.resources
import sqlite3

# Connect to your SQLite database
conn = sqlite3.connect("example.db")

# Load the sqlite-ai extension
# pip will install the correct binary package for your platform and architecture
# Choose between CPU or GPU variant
ext_path = importlib.resources.files("sqliteai.binaries.cpu") / "ai"

conn.enable_load_extension(True)
conn.load_extension(str(ext_path))
conn.enable_load_extension(False)


# Now you can use sqlite-ai features in your SQL queries
print(conn.execute("SELECT ai_version();").fetchone())
```