semvec/pypi-packages huggingface dataset:
lanisapi 0.5.0.post1.dev1
Eine unoffizielle Python-Library für Lanis.
Requires Python: >=3.11
Keywords: Lanis, SPH, Schulportal Hessen, Hessen, API

<div align="center">
  <img width="750" src="https://raw.githubusercontent.com/kurwjan/LanisAPI/rewrite/header.svg">
</div>

## Work in progress
Diese Branch enthält noch nicht alle alten Features und kein Wiki, aber sie
werden bald kommen. Aktuell implementiert ist:
+ Grundstruktur `LanisClient`
  + Applets _(Verfügbare, Unterstützung)_
  + Client und Initialisierung entkoppelt _(→ core/initialization/types.py)_
    + Cookie-Initialisierung
    + Account-Initialisierung mit Schul-Id
+ Modularer Modulaufbau _(auch Custom Module)_
  + Vertretungsplan-Modul

## Installation
Jede Version ab **0.5.0** sind von der rewrite branch, sie sind auch immer als Pre-Release markiert.

### Erste Methode (Git)
+ Habe Python 3.11
+ Downloade den Source-Code (z. B. `git clone https://github.com/kurwjan/LanisAPI.git`)
+ Führe `pip install .` im Root-Ordner _("LanisAPI")_ aus.

### Zweite Methode (Pip)
+ Habe Python 3.11
+ Führe `pip install lanisapi==VERSION` aus um die spezifische Prerelease-Version downloaden zu können.
  + Liste aller Prerelease- und Release-Versionen findest du hier: [Github](https://github.com/kurwjan/LanisAPI/releases) und [PyPi](https://pypi.org/project/lanisapi/#history).
kite_connect_lite 0.0.3
A user-friendly wrapper for the Kite Connect library
Requires Python: <4.0,>=3.9

# Kite Connect Lite Package

This project uses Poetry for dependency management and packaging.

## Prerequisites

Before getting started, ensure that you have the following installed on your system:

-   Python (version X.X.X)
-   pipx (version X.X.X) ( `brew install pipx` )

## Installation

1. Install Poetry using pipx:

    ```bash
    pipx install poetry
    ```

    pipx is a tool that allows you to install and run Python applications in isolated environments. By installing Poetry with pipx, you ensure that it doesn't interfere with other Python projects on your system.

2. Clone the repository

3. Navigate to the project directory
4. Install the project dependencies using Poetry:

    ```bash
    poetry install
    ```

    This command reads the `pyproject.toml` file and installs all the required dependencies in a virtual environment specific to the project.

## Running the Project

To run the Python file using Poetry, follow these steps:

1. Ensure that you are in the project directory
2. Activate the virtual environment created by Poetry:

    ```bash
    poetry shell
    ```

    This command spawns a shell with the virtual environment activated.

3. Run the Python file:

    ```bash
    python relative_path_to_your_script.py
    ```

    Replace `relative_path_to_your_script.py` with the actual relative path of your Python file.

    Alternatively, you can run the Python file without activating the virtual environment by using the `poetry run` command:

    ```bash
    poetry run python relative_path_to_your_script.py
    ```

    This command executes the Python file within the context of the virtual environment managed by Poetry.

## Additional Commands

Here are a few additional Poetry commands that you might find useful:

-   `poetry add <package>`: Adds a new dependency to the project.
-   `poetry update`: Updates the project dependencies to their latest versions.
-   `poetry remove <package>`: Removes a dependency from the project.
-   `poetry show`: Lists all the installed packages and their versions.
openai-requests-utils 0.0.8
Useful utils to use with openai models
Requires Python: >=3.8

# openai_requests_utils

You need to create an environment variable called "OPENAI_API_KEY" which contains your OpenAI API key. You also need to create a config file called oai_config.json in the same folder as your script to choose the model and the API call parameters. Every parameter except "model" is optional and can be removed.

Example of oai_config.json:
{
    "model": "gpt-3.5-turbo-1106",
    "max_response_length": 400,
    "timeout": 10,
    "temperature": 1,
    "top_p": 0.95,
    "presence_penalty": 0.5,
    "frequency_penalty": 0.5
}
input-field-component 0.0.2
Streamlit component that allows you to create sentence with input field
Requires Python: >=3.7

# streamlit-custom-component

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install bestcomponentever
```

## Usage instructions

```python
import streamlit as st

from bestcomponentevers import input_field

value = input_field("I love [G_A_P] apples")

st.write(value)
```
input-field-component-v2 0.0.2
Streamlit component that allows you to create sentence with input field
Requires Python: >=3.7

# streamlit-custom-component

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install bestcomponentever
```

## Usage instructions

```python
import streamlit as st

from bestcomponentevers import input_field

value = input_field("I love [G_A_P] apples")

st.write(value)
```
execdata 5.5.1
Preprocessing dataset
Requires Python: >=3.6

<!--
 * @Author: BDFD
 * @Date: 2021-10-27 18:39:19
 * @LastEditTime: 2021-10-27 21:22:31
 * @LastEditors: Please set LastEditors
 * @Description: In User Settings Edit
 * @FilePath: \6.0-PyPI_Missing_Value_Table\README.md
-->

# Package Name

Package function description

## Installation

`pip install execdata`

## How to use it?

import execdata as exe
exe.encode(df),
exe.split(df),

exe.model_evaluate(X_train, X_test, y_train, y_test)
exe.algo_accuracy(y_test, y_predict)
result_comparision(y_test, y_predict)

## License

copyright @ 2021 BDFD

This repository is licensed under the MIT license. See LICENSE for details.

### References

https://github.com/bdfd/3.1-PyPI_Template
rm-pycache 1.0.2
Remove all __pycache__ in specified directory
Requires Python: <4.0,>=3.8

## installation ##
```
python3 -m pip install rm-pycache
```

## usage ##
rm_pycache(***path***: str=None, ***stdout***: bool=False)

```python
from rm_pycache import rm_pycache


rm_pycache(path='./src', stdout=True)
```
pytorch-sidu 1.1.3
SIDU: SImilarity Difference and Uniqueness method for explainable AI
Requires Python: >=3.6

# **pytorch-sidu**

[![size](https://img.shields.io/github/languages/code-size/MarcoParola/pytorch-sidu)]()

SIDU: SImilarity Difference and Uniqueness method for explainable AI from the [original paper](https://arxiv.org/pdf/2006.03122.pdf)

- Pytorch implementation of the SIDU method. 
- Simple interface for loading pretrained models by specifying one of the following [string name](https://pytorch.org/vision/stable/models.html#table-of-all-available-classification-weights)
- Clear interface for generating saliency maps

Some examples made with VGG19 on [Caltech-101 dataset](https://paperswithcode.com/dataset/caltech-101):

![img1](https://github.com/MarcoParola/pytorch-sidu/assets/32603898/e2bc0085-11c8-4fd7-975e-72e49ff7ee77)
![img7](https://github.com/MarcoParola/pytorch-sidu/assets/32603898/860492cf-fc24-4f40-ad65-6d42a6a539a8)
![img9](https://github.com/MarcoParola/pytorch-sidu/assets/32603898/83c7c206-5927-438d-93af-aa3e94914461)


## Installation

```
pip install pytorch-sidu
```

## Usage

Load models from the pretrainde ones available in pytorch

```py
import pytorch_sidu as sidu

model_name = 'ResNet34_Weights.IMAGENET1K_V1'
model = load_torch_model_by_string(model_name)
```

After instantianting your model, generate saliency maps from Dataloader

```py
data_loader = <your dataloader>
target_layer = 'layer4.2.conv2'
image, _ = next(iter(data_loader))
saliency_maps = sidu.sidu(model, target_layer, image)
```

### A complete example on CIFAR-10

```py
import torch
import torchvision
from matplotlib import pyplot as plt
import pytorch_sidu as sidu


transform = torchvision.transforms.Compose([torchvision.transforms.Resize((224, 224)), torchvision.transforms.ToTensor()])
data_loader = torch.utils.data.DataLoader(
    torchvision.datasets.CIFAR10(root='./data', download=True, transform=transform), batch_size=2)

target_layer = 'layer4.2.conv2'
model_name = 'ResNet34_Weights.IMAGENET1K_V1'
model = sidu.load_torch_model_by_string(model_name)

for image, _ in data_loader:
    saliency_maps = sidu.sidu(model, target_layer, image)
    image, saliency_maps = image.cpu(), saliency_maps.cpu()

    for j in range(len(image)):
        plt.figure(figsize=(5, 2.5))
        plt.subplot(1, 2, 1)
        plt.imshow(image[j].permute(1, 2, 0))
        plt.axis('off')
        plt.subplot(1, 2, 2)
        plt.imshow(image[j].permute(1, 2, 0))
        plt.imshow(saliency_maps[j].squeeze().detach().numpy(), cmap='jet', alpha=0.4)
        plt.axis('off')
        plt.show()
```

## upcoming features:
- integration of xai metrics
- make methods work on both single images and dataloaders
- adding `device` flag to sidu function to allow device selection