semvec/pypi-packages huggingface dataset:
spegel 0.1.3
Spegel - Reflect the web through AI. A terminal browser with multiple AI-powered views.
Requires Python: >=3.11
Keywords: ai, browser, llm, terminal, textual, tui, web

# Spegel - Reflect the web through AI

Automatically rewrites the websites into markdown optimised for viewing in the terminal.
Read intro blog post [here](https://simedw.com/2025/06/23/introducing-spegel/)

This is a proof-of-concept, bugs are to be expected but feel free to raise an issue or pull request.

##  Screenshot
Sometimes you don't want to read through someone's life story just to get to a recipe
![Recipe Example](https://simedw.com/2025/06/23/introducing-spegel/images/recipe_example.png)


## Installation

Requires Python 3.11+

```bash
$ pip install spegel
```
or clone the repo and install it in editable mode

```bash
# Clone and enter the directory
$ git clone https://github.com/simedw/spegel.git
$ cd spegel

# Install dependencies and the CLI
$ pip install -e .
```

## API Keys
Spegel is using [litellm](https://github.com/BerriAI/litellm), which allows the use of the  common LLMs, both local and external. 

By default `Gemini 2.5 Flash Lite` is used, which requires you to set the `GEMINI_API_KEY`, see [env_example.txt](/env_example.txt)


## Usage

### Launch the browser

```bash
spegel                # Start with welcome screen
spegel bbc.com        # Open a URL immediately
```

Or, equivalently:

```bash
python -m spegel      # Start with welcome screen
python -m spegel bbc.com
```

### Basic controls
- `/`         – Open URL input
- `Tab`/`Shift+Tab` – Cycle links
- `Enter`     – Open selected link
- `e`         – Edit LLM prompt for current view
- `b`         – Go back
- `q`         – Quit

## Editing settings

Spegel loads settings from a TOML config file. You can customize views, prompts, and UI options.

**Config file search order:**
1. `./.spegel.toml` (current directory)
2. `~/.spegel.toml`
3. `~/.config/spegel/config.toml`

To edit settings:
1. Copy the example config:
   ```bash
   cp example_config.toml .spegel.toml
   # or create ~/.spegel.toml
   ```
2. Edit `.spegel.toml` in your favorite editor.

Example snippet:
```toml
[settings]
default_view = "terminal"
app_title = "Spegel"

[ai]
default_model="gpt-4.1-nano"

[[views]]
id = "raw"
name = "Raw View"
hotkey = "1"
order  = "1"
prompt = ""

[[views]]
id = "terminal"
name = "Terminal"
hotkey = "2"
order = "2"
prompt = "Transform this webpage into the perfect terminal browsing experience! ..."
model="claude-3-5-haiku-20241022"
```

## Local Models with Ollama

To run with a local model using Ollama, first pull and serve your desired model:

```bash
$ ollama pull llama2
$ ollama serve
```
Then set the model in `.spegel.toml` as follows:

```toml
model = "ollama/llama2"
```
Ollama supports models like Llama, Mistral, and many others.

## License
MIT License - see LICENSE file for details.


For more, see the code or open an issue!
timelap 0.0.1
A simple time measuring library
Requires Python: >=3.7

[![License: CC0-1.0 license](https://img.shields.io/npm/l/badge-maker.svg)](http://creativecommons.org/publicdomain/zero/1.0/)

# Timelap


### Usage
```py
from timelap import timelap

timelap.start("Starting timer") # Starts the timer. Text is optional.
# Some code
timelap.now("Checkpoint") # Prints the amount of time that's passed since timelap.start(). Text is optional.
# Some more code
timelap.stop("Now stopped") # Prints the amount of time passed a final time, then stopping. Text is optional.
```

## License
No Copyright

The person who associated a work with this deed has dedicated the work to the public domain by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information below.

 In no way are the patent or trademark rights of any person affected by CC0, nor are the rights that other persons may have in the work or in how the work is used, such as [publicity or privacy](https://creativecommons.org/publicdomain/zero/1.0/#ref-publicity-rights) rights.
Unless expressly stated otherwise, the person who associated a work with this deed makes no warranties about the work, and disclaims liability for all uses of the work, to the fullest extent permitted by applicable law.
When using or citing the work, you should not imply [endorsement](https://creativecommons.org/publicdomain/zero/1.0/#ref-endorsement) by the author or the affirmer.
pyeogpr 2.4.7
Python based library to use Earth Observation data to retrieve biophysical maps using Gaussian Process Regression

<div style="display: flex; flex-direction: column; align-items: center; justify-content: center;">
  <img src="https://github.com/user-attachments/assets/a3ede50e-acbb-4375-bcfd-a3892f8c3c7d" alt="logo" width="250"/>
  <div style="display: flex; justify-content: center; margin-top: 10px;">
    <img src="https://github.com/user-attachments/assets/9e748a0f-6594-4ed8-bb55-1e0ce53a1577" alt="logo" width="200"/>
    <img src="https://github.com/user-attachments/assets/37f31ff8-b4da-42a6-8497-ed0566555f82" alt="logo" width="200"/>
  </div>
</div>



# pyeogpr [![GitHub](https://img.shields.io/badge/GitHub-pyeogpr-purple.svg)](https://github.com/daviddkovacs/pyeogpr)   [![Documentation](https://img.shields.io/badge/docs-pyeogpr-blue.svg)](https://pyeogpr.readthedocs.io/en/latest/pyeogpr.html) [![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.13373838-green)](https://doi.org/10.5281/zenodo.13373838)



Python based machine learning library to use Earth Observation data to map biophysical traits using Gaussian Process Regression (GPR) models. Works with Google Earth Engine and openEO cloud back-ends.

# Features

- Access to GEE/openEO is required. Works best with the Copernicus Data Space Ecosystem. Register [here](https://code.earthengine.google.com/register) or [here](https://docs.openeo.cloud/join/free_trial.html)
- Hybrid retrieval methods were used: the Gaussian Process Regression retrieval algorithms were trained on biophysical trait specific radiative transfer model (RTM) simulations
- Uncertainties provided! 
- Runs "in the cloud" with the GEE/openEO Python API. No local processing is needed.
- Resulting maps in .tiff or netCDF format

# Get started

Refer to the [Documentation](https://pyeogpr.readthedocs.io/en/latest/pyeogpr.html) for instructions and examples.


# Satellites and biophysical variables

You can select from a list of trained variables developed for the following satellites:

[Sentinel-2 L1C](https://pyeogpr.readthedocs.io/en/latest/sensors.html#)

[Sentinel-2 L2A](https://pyeogpr.readthedocs.io/en/latest/sensors.html#)

[Sentinel-3](https://pyeogpr.readthedocs.io/en/latest/sensors.html#sentinel-3-ocean-and-land-colour-instrument-olci)

# Cite as / Contact

- Kovács DD, Reyes-Muñoz P, Salinero-Delgado M, Mészáros VI, Berger K, Verrelst J. Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine. Remote Sensing. 2023; 15(13):3404. https://doi.org/10.3390/rs15133404
  
or

- Dávid D.Kovács. (2024). pyeogpr (zenodo). Zenodo. https://doi.org/10.5281/zenodo.13373838
- david.kovacs@tuwien.ac.at

## 
Supported by the European Union (European Research Council, FLEXINEL, 101086622) project.

<a href="https://leoipl.uv.es/flexinel/">
  <img src="https://github.com/user-attachments/assets/940bf34f-04d3-4fb0-9d68-8d6f19c14bab" alt="ERC Logo">
</a>