semvec/pypi-packages huggingface dataset:
pykeboola 0.0.12
Python package for interacting with Keboola
Requires Python: >=3.8

# pykeboola
Python package for interacting with Keboola. I have just started this and will build it as needed for future development.
For now it includes only queueing jobs and checking on their statuses and listing all tables available to the token.

# How to use
You can use the `Client` class to gateway into individual functionalities:
```
from pykeboola import Client
client = Client('url', 'token')
```

For queueing jobs and checking their statuses you can use E.g.:
```
client.jobs.check_job_status(id_of_the_job)
```
drmmsdk 0.0.5
Datto RMM Unofficial Python SDK
Requires Python: >=3.10

# Datto RMM Unofficial Python SDK

This is an unofficial Python SDK for Datto RMM. It was created to provide a Python interface for interacting with the Datto RMM API.

## Installation

You can install the package using pip:

```bash
pip install drmmsdk
```

## Usage

Initialize the SDK by providing your API key, API secret, and server. You can find your API key and API secret in the Datto RMM dashboard. The server is the URL of your Datto RMM instance, such as `concord`, `vidal,`, `zinfandel`, `merlot`, `pinotage`, or `syrah`.

```python
from drmmsdk import drmmsdk

drmm = drmmsdk(api_key="your_api_key", api_secret="your_api_secret", server="your_server")
```

## Methods

The SDK currently supports the following methods:

1. `create_site()`: Creates a new site.
2. `create_site_variable()`: Creates a new site variable.
3. `move_device()`: Moves a device.
4. `create_quick_job()`: Creates a quick job.
5. `create_account_variable()`: Creates an account variable.
6. `reset_api_keys()`: Resets the API keys.
7. `fetch_site_data()`: Fetches site data.
8. `update_site()`: Updates a site.
9. `update_site_variable()`: Updates a site variable.
10. `delete_site_variable()`: Deletes a site variable.
11. `create_proxy_settings()`: Creates proxy settings.
12. `delete_site_proxy_settings()`: Deletes site proxy settings.
13. `set_device_warranty()`: Sets device warranty.
14. `set_device_udf()`: Sets device UDF.
15. `unmute_alert()`: Unmutes an alert.
16. `resolve_alert()`: Resolves an alert.
17. `mute_alert()`: Mutes an alert.
18. `update_account_variable()`: Updates an account variable.
19. `delete_account_variable()`: Deletes an account variable.
20. `get_system_status()`: Gets system status.
21. `fetch_request_rate()`: Fetches request rate.
22. `fetch_pagination_configurations()`: Fetches pagination configurations.
23. `get_site_variables()`: Gets site variables.
24. `fetch_site_settings()`: Fetches site settings.
25. `fetch_site_filters()`: Fetches site filters.
26. `get_site_devices()`: Gets site devices.
27. `fetch_resolved_alerts()`: Fetches resolved alerts.
28. `fetch_open_alerts()`: Fetches open alerts.
29. `fetch_job_data()`: Fetches job data.
30. `fetch_job_components()`: Fetches job components.
31. `get_default_filters()`: Gets default filters.
32. `fetch_custom_filters()`: Fetches custom filters.
33. `get_device_data()`: Gets device data.
34. `fetch_device_data()`: Fetches device data.
35. `fetch_audit_data()`: Fetches audit data.
36. `fetch_audited_software()`: Fetches audited software.
37. `fetch_alert_data()`: Fetches alert data.
38. `fetch_account_data()`: Fetches account data.
39. `fetch_account_variables()`: Fetches account variables.
40. `get_user_records()`: Gets user records.
41. `fetch_site_records()`: Fetches site records.
42. `get_devices()`: Gets devices.
43. `fetch_components()`: Fetches components.

Please note that this is an unofficial SDK and is not endorsed by Datto. Always ensure that you're following all relevant policies and guidelines when interacting with the Datto RMM API.
autodistill-bioclip 0.1.0
BioCLIP model for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill BioCLIP Module

This repository contains the code supporting the BioCLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[BioCLIP](https://github.com/Imageomics/BioCLIP) is a CLIP model trained on the [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) dataset, created by the researchers who made BioCLIP. The dataset on which BioCLIP was trained included more than 450,000 classes.

You can use BioCLIP to auto-label natural organisms (i.e. animals, plants) in images for use in training a classification model. You can combine this model with a grounded detection model to identify the exact region in which a given class is present in an image. [Learn more about combining models with Autodistill](https://docs.autodistill.com/utilities/combine-models/).

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [BioCLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/bioclip/).

## Installation

To use BioCLIP with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-bioclip
```

## Quickstart

```python
from autodistill_bioclip import BioCLIP

# define an ontology to map class names to our BioCLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
classes = ["arabica", "robusta"]

base_model = BioCLIP(
    ontology=CaptionOntology(
        {
            item: item for item in classes
        }
    )
)

results = base_model.predict("../arabica.jpeg")

top = results.get_top_k(1)
top_class = classes[top[0][0]]

print(f"Predicted class: {top_class}")
```


## License

This project is licensed under an [MIT license](LICENSE).

The underlying [BioCLIP model](https://huggingface.co/imageomics/bioclip) is also licensed under an MIT license.

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
pygraphedit 0.0.5
Graph editor for Jupyter
Requires Python: >=3.10

## Interactive Graph Editor for Jupyter

An interactive graph editor function designed for Jupyter notebooks. ```edit(graph: nx.Graph)``` function allows users <br>
to manipulate a graph by creating vertices, edges, and adding labels directly within a Jupyter environment.

### Parameters
- **graph** (*networkx.Graph*): The graph object to be edited.
  It should be an instance of the NetworkX Graph class or a subclass.

### Functions of buttons (from left to right)
1. Select whether you want to edit graph structure.
2. Select whether you want to edit labels.
3. Toggle nodes clickable option.
4. Toggle edges clickable option.
5. Close editing window.

### Mouse Functions
1. Click and drag vertices to move them around the canvas.
2. To create an edge, click on one vertex and then click on another vertex.<br>
An edge will be created between the two selected vertices.
3. To create a vertex, click on empty space of a canvas.
4. To delete an object, double-click on it.

### Dependencies
- Jupyter notebook web environment.
- NetworkX library for graph manipulation.

### Notes
This function relies on Jupyter ipywidgets, so it should work only in web versions of Jupyter.

### Examples
```python
import networkx as nx
from pygraphedit import edit

# Create a sample graph
G = nx.Graph()
G.add_nodes_from([1, 2, 3])
G.add_edges_from([(1, 2), (2, 3)])

# Call the interactive graph editor
edit(G)
resume-matcher 0.2
A Python library for matching resumes with job descriptions

# Resume Matcher

A Python library for matching resumes with job description.

## Installation

You can install the required dependencies using pip:

<b>pip install resume-matcher</b>

## Usage

Here's how you can use the <b>resume_job_desc_match</b> function:

from resume_matcher.matching import resume_job_desc_match

<b>Path to the resume and job description files</b><br>
resume_path = "My_Resume.docx"
job_desc_path = "Job_desc.docx"

<b>Call the resume_job_desc_match function</b><br>
match_percentage = resume_job_desc_match(resume_path, job_desc_path)

<b>Print the match percentage</b><br>
print("Match percentage:", match_percentage)

<b>Overall Code:</b><br>

from resume_matcher.matching import resume_job_desc_match

resume_path = "My_Resume.docx"<br>
job_desc_path = "Job_desc.docx"

match_percentage = resume_job_desc_match(resume_path, job_desc_path)

print("Match percentage:", match_percentage)

<b>Note:</b> Please upload doc files of Job Description and Resume.