semvec/pypi-packages huggingface dataset:
streamlit-google-analytics-tag 1.1
Streamlit component that allows you to integrate Google Analytics by tag and send events to Google Analytics
Requires Python: >=3.7

# Streamlit Google Analytics Tag (gtag)

A Streamlit component that allows you to integrate Google Analytics by tag and send events to Google Analytics.

## Installation

```bash
pip install streamlit-google-analytics-tag
```

## Usage

**Important**: The package name is `streamlit-google-analytics-tag` but the module name is `streamlit_gtag`.

```python
import streamlit as st
from streamlit_gtag import st_gtag  # Note: import from streamlit_gtag, not streamlit_google_analytics_tag

# Initialize Google Analytics
st_gtag(
    gtag_id="GA_MEASUREMENT_ID",
    config={
        "send_page_view": True
    }
)

# Send custom events
st_gtag(
    event="custom_event",
    parameters={
        "event_category": "engagement",
        "event_label": "button_click"
    }
)
```

## Features

- Easy integration with Google Analytics 4
- Support for custom events
- Streamlit-native component
- Lightweight and fast

## License

MIT License
simpsons-rdf 0.3.0
An example rdflib graph with some simpsons.
Requires Python: <4.0,>=3.9

# The Simpsons in RDF
This is an example file modeling the Simpsons Family in RDF.
It was originally taken from

http://sws.ifi.uio.no/inf3580/v15/oblig/3/simpsons.ttl

where it is part of an assignment:

https://www.uio.no/studier/emner/matnat/ifi/INF3580/v17/undervisningsmateriale/oblig_4.pdf

I have slightly adopted it and moved it to the `<https://simpsons.example.org/>` namespace.

The simpsons graph is also included in the [JekyllRDF tutorial](https://github.com/white-gecko/JekyllRDF-Tutorial): https://github.com/white-gecko/JekyllRDF-Tutorial/blob/master/_data/graph.ttl ([JekyllRDF](https://github.com/AKSW/jekyll-rdf))

## Usage in Python

To use it in python do:

```sh
pip install simpsons-rdf
```

or

```sh
poetry add simpsons-rdf
```

use it like any other [RDFLib](https://rdflib.dev/) [Graph](https://rdflib.readthedocs.io/en/stable/apidocs/rdflib.html#rdflib.graph.Graph) object.

```python
from simpsons import simpsons, SIM, FAM

print({p: o for _, p, o in simpsons.graph.triples((SIM.Homer, None, None))})
print([spouse for _, _, spouse in simpsons.graph.triples((SIM.Homer, FAM.hasSpouse, None))])
print(simpsons.graph.serialize(format="turtle"))
```
streamlit-state-attribute 0.2.3
A lightweight utility to bind class attributes directly to Streamlit's session state, making it easy to persist and share values across reruns.
Requires Python: >=3.11
Keywords: streamlit, session state, state management, attributes, sync

# Streamlit State Attribute

<!-- START doctoc generated TOC please keep comment here to allow auto update -->
<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
**Contents**

- [Idea](#idea)
- [Advantages](#advantages)
- [Install](#install)
- [Examples](#examples)
- [Background](#background)

<!-- END doctoc generated TOC please keep comment here to allow auto update -->

## Idea
Instead of using `st.session_state["some_key"]`, define a typed attribute of some class which is automatically synced
with the session state.

```python
from streamlit_state_attribute import StateAttribute
import streamlit as st

class SomeWidget:
    some_attribute: str = StateAttribute(default="test")

some_widget = SomeWidget()
some_widget.some_attribute = "3"
assert st.session_state["SomeWidget.some_attribute"] == "3"
```

## Advantages
 * Handling `st.session_state` is abstracted away
 * Autosuggestions + type hints
 * Logging each state change (default logging level = debug, can be configured per-attribute)
 * Easily build Widgets with their own local state

## Install
```bash
uv pip install streamlit-state-attribute
```

## Examples
```python
import streamlit as st
from streamlit_state_attribute import StateAttribute

class SomeWidgetWithKey:
    key: str
    some_attribute: str = StateAttribute(default="test", unique_attribute="key")

    def __init__(self, key: str) -> None:
        self.key = key


# Each key will have a separate State
other_widget = SomeWidgetWithKey(key="test")
other_widget.some_attribute = "4"
assert st.session_state["SomeWidgetWithKey.test.some_attribute"] == "4"
```
See also [counter.py](src/examples/counter.py) and [global_state.py](src/examples/counter.py).

## Background
Made to play around with [descriptors](https://docs.python.org/3/howto/descriptor.html) after a [workshop descriptors
at Pycon2025](https://pretalx.com/pyconde-pydata-2025/talk/WJPEQH/).
mcp-meteo 0.2.6
Serveur MCP pour accéder aux données météo via Open-Meteo
Requires Python: >=3.10


# MCP-Meteo

Ce projet est un serveur MCP (Model Context Protocol) permettant d'obtenir des informations météo en temps réel ou en prévision, basé sur l'API Open-Meteo.

## Fonctionnalités

- **Météo actuelle par coordonnées** : Obtenez la météo actuelle en fournissant la latitude et la longitude.
- **Météo actuelle par ville** : Obtenez la météo actuelle en indiquant le nom d'une ville.
- **Prévisions météo (1 à 7 jours) par coordonnées** : Récupérez les prévisions pour plusieurs jours en fournissant la latitude et la longitude.
- **Prévisions météo (1 à 7 jours) par ville** : Récupérez les prévisions pour plusieurs jours en indiquant le nom d'une ville.

## Installation

1. Clonez le dépôt :
	```sh
	git clone <url-du-repo>
	```
2. Installez les dépendances (voir `pyproject.toml`) :
	```sh
	pip install -r requirements.txt
	```
	ou
	```sh
	pip install fastmcp requests
	```

## Utilisation

Lancez le serveur MCP :
```sh
python -m mcp-meteo.server
```
Le serveur démarre en mode `stdio` et expose plusieurs outils MCP pour interroger la météo.

## Intégrer directement à Claude Desktop/VS code

Config json nécessaire 

```json
{
    "mcpServers":{
        "meteo":{
            "command":"uvx",
            "args": "mcp-meteo"
        }
    }
}
```

## Structure du projet

- `mcp-meteo/server.py` : Serveur MCP principal, expose les outils météo.
- `client_test.py` : Exemple de client.
- `pyproject.toml` : Dépendances et configuration du projet.


## API utilisées

- [Open-Meteo Forecast](https://open-meteo.com/)
- [Open-Meteo Geocoding](https://open-meteo.com/en/docs/geocoding-api)

## Liste d'outils MCP

- `get_weather(latitude, longitude)`
- `get_weather_by_city(city)`
- `get_forecast(latitude, longitude, days)`
- `get_forecast_by_city(city, days)`



## Licence

Ce projet est open-source, voir le fichier `LICENSE` si présent.