semvec/pypi-packages huggingface dataset:
streamlit-image-button 0.2.2.13
Streamlit component that allows you to create image buttons.
Requires Python: >=3.7

# streamlit-custom-component

Streamlit component that allows you to add clickable buttons with .png or .jpg image replacing text.

## Installation instructions

```sh
pip install streamlit-image-button
```

## Usage instructions

```python
import streamlit as st

from st_image_button import st_image_button

st_image_button("Title", "icon.png", "20px", "outlined", onClick)
```
hydrology-api-uk-client 0.0.1
Load data from the UK Hydology API
Requires Python: >=3.9
Keywords: Hydology,API,UK,Water,Data,Environment Agency,River

# Hydrology API

**This package was build for internal use, please feel free to use it but it may have bugs**

This package is designed to read data from the UK government [Hydrology API](https://environment.data.gov.uk/hydrology/doc/reference).
The API provides:

-   River Level, Flow and Water Quality Data
-   Rainfall Data
-   Groundwater Level Data

This package currently only provides access to the River Level, Flow, and Rainfall.

## Example

```python
from hydrology_api import HydrologyAPI, Measure, process_hydrology_data

api = HydrologyApi(max_threads=5)

stations = api.get_stations_on_river('River Wear', Measure.LEVEL)
print(f"Loading data for {len(stations)} stations: {stations['label'].values}")
level_df = api.load(Measure.LEVEL, stations)
level_df = process_hydrology_data(level_df)
level_df.info()
>>> Loading data for 5 stations: ['Chester Le Street' 'Witton Park' 'Sunderland Bridge' 'Stanhope'
 'Durham New Elvet Bridge']
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=e7d8bbb6-5bba-4057-9f49-a299482c3348-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=05784319-693a-4d75-b29e-32f01a99ee4f-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=ddedb4d9-b2be-47c1-998d-acbc0ffb124b-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=b29c481a-5012-40f5-bb0c-f9370be34975-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=ba3f8598-e654-430d-9bb8-e1652e6ff93d-level-i-900-m-qualified&mineq-date=2007-01-01
>>> <class 'pandas.core.frame.DataFrame'>
>>> DatetimeIndex: 590967 entries, 2007-01-01 00:00:00 to 2023-11-08 21:30:00
>>> Freq: 15T
>>> Data columns (total 5 columns):
>>>  #   Column                   Non-Null Count   Dtype
>>> ---  ------                   --------------   -----
>>>  0   Chester Le Street        590967 non-null  float16
>>>  1   Durham New Elvet Bridge  563826 non-null  float16
>>>  2   Stanhope                 501615 non-null  float16
>>>  3   Sunderland Bridge        589418 non-null  float16
>>>  4   Witton Park              585167 non-null  float16
>>> dtypes: float16(5)
>>> memory usage: 10.1 MB
```
hydrology-api-client-uk 0.0.1
Load data from the UK Hydology API
Requires Python: >=3.9
Keywords: Hydology,API,UK,Water,Data,Environment Agency,River

# Hydrology API

**This package was build for internal use, please feel free to use it but it may have bugs**

This package is designed to read data from the UK government [Hydrology API](https://environment.data.gov.uk/hydrology/doc/reference).
The API provides:

-   River Level, Flow and Water Quality Data
-   Rainfall Data
-   Groundwater Level Data

This package currently only provides access to the River Level, Flow, and Rainfall.

## Example

```python
from hydrology_api import HydrologyAPI, Measure, process_hydrology_data

api = HydrologyApi(max_threads=5)

stations = api.get_stations_on_river('River Wear', Measure.LEVEL)
print(f"Loading data for {len(stations)} stations: {stations['label'].values}")
level_df = api.load(Measure.LEVEL, stations)
level_df = process_hydrology_data(level_df)
level_df.info()
>>> Loading data for 5 stations: ['Chester Le Street' 'Witton Park' 'Sunderland Bridge' 'Stanhope'
 'Durham New Elvet Bridge']
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=e7d8bbb6-5bba-4057-9f49-a299482c3348-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=05784319-693a-4d75-b29e-32f01a99ee4f-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=ddedb4d9-b2be-47c1-998d-acbc0ffb124b-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=b29c481a-5012-40f5-bb0c-f9370be34975-level-i-900-m-qualified&mineq-date=2007-01-01
>>> Loading from cache: https://environment.data.gov.uk/hydrology/data/batch-readings/batch/?measure=ba3f8598-e654-430d-9bb8-e1652e6ff93d-level-i-900-m-qualified&mineq-date=2007-01-01
>>> <class 'pandas.core.frame.DataFrame'>
>>> DatetimeIndex: 590967 entries, 2007-01-01 00:00:00 to 2023-11-08 21:30:00
>>> Freq: 15T
>>> Data columns (total 5 columns):
>>>  #   Column                   Non-Null Count   Dtype
>>> ---  ------                   --------------   -----
>>>  0   Chester Le Street        590967 non-null  float16
>>>  1   Durham New Elvet Bridge  563826 non-null  float16
>>>  2   Stanhope                 501615 non-null  float16
>>>  3   Sunderland Bridge        589418 non-null  float16
>>>  4   Witton Park              585167 non-null  float16
>>> dtypes: float16(5)
>>> memory usage: 10.1 MB
```
deairequest 0.0.20
A Decentralised AI library which allows to run remote AI jobs
Requires Python: >=3.7

# The Decentralised AI Request   
  
Enable for any AI python code to be executed on decentralised AI compute.  
   
Supported AI compute:  
[Bacalhau](https://bacalhau.org)  
   
Required:
Have a working IPFS Desktop App running on the same machine. You can download it [here](https://docs.ipfs.tech/install/ipfs-desktop/).  
  
Commands:  
python -m build # build the project   
python -m unittest # run all unit tests   

Usage:  
from deairequest import DeProtocolSelector as selector  
bp = selector.DeProtocolSelector("Bacalhau")  
bp.get_docker_images()  
if a user wants a custom image  
bp.add_docker_image("custom_images")  
For each request you need to set the docker image to use  
bp.set_docker_image("the docker image to use")  
  
To add datasets of type URL:  
bp.add_dataset(bp.get_url_data_type(),"https://www...")  
Of type File:  
bp.add_dataset(bp.get_file_data_type(),"/path/to/file/name.csv")  
Of type Directory:  
bp.add_dataset(bp.get_directory_data_type(),"/path/to/directory/")  
Of type IPFS:  
bp.add_dataset(bp.get_ipfs_data_type(),"AW233dfD23dsds32323sdds") # The IPFS CID  
To get all the datasets:  
bp.get_datasets()  
output: [{"url":"https://www..."},{"file":"/path/to/file/name.csv"},{"directory":"/path/to/directory/"},{"ipfs":"AW233dfD23dsds32323sdds"}]  
  
Afterwards you can submit a job  
job = bp.submit_job(Path("/path/to/notebook.jpynb"))  
get the logs  
state = bp.get_state(job)   
and when the job is finished and the state changes from "InProgress" to either "Complete" or "Error"  
result = bp.get_results(job,Path("/path/to/output/directory/"))
recipemaster 1.0.0
Este proyecto ha sido realizado por Asier Aranoa y Paul Magunazelaia. El objetivo principal de este proyecto es crear una herramienta que permita a los usuarios obtener recetas de cocina en función de los ingredientes que tienen disponibles en casa.

# progra_TrabajoGrupal
Trabajo grupal donde se pondrán en práctica los fundamentos adquiridos en clase para desplegar una librería en PyPi