semvec/pypi-packages huggingface dataset:
kivy-file-manager-package 2.0
Useful file manager app written in Kivy
Requires Python: >=3.6

## What is this?
<p>This is a package of a project on the Pypi index site.</p>
<p>You download the project by copying the comand shown above and running it in the terminal.</p>
<p>You can then run the app with a specific command.</p>

# For more info look for the Pypi/HighSeas demo link section in the Readme file inside this projects Github repository!
suicide-text-predictor 0.2.4
A package to predict if a text is a suicide post or not.
Requires Python: >=3.7

# Suicide Text Predictor

This package predicts if a given text is a suicide post or not using a trained PyTorch model.

## Installation

```bash
pip install suicide_text_predictor
```
## Sample Usage
```python
import suicide_text_predictor
text = input("Input a text:\n")
result=suicide_text_predictor.predict_tweet(text)
print(result)
```
aisys 0.1.0
lil' ai buddy
Requires Python: <4.0,>=3.13

# System
> lil' ai buddy


***MAKE SURE TO READ (SETUP)[#setup] BEFORE RUNNING, OR ELSE IT WON'T WORK***

***TESTED FOR MACOS AND LINUX, WINDOWS MAY WORK BUT NO GUARANTEES***

## About
System / S.Y.S / AISYS is an AI agent I have been experimentally building for a while now. They can search recent news, surf the internet, and get you the information you need.

## Should I use this in my everyday life?
No. This is mostly highly experimental and doesn't have any real use cases, I'm just building it to learn more about AI and how it works.

## Setup
1. get python 3.13
3. use `pip3.13 install aisys`, ***MAKE SURE PACKAGE EXECUTABLES ARE IN YOUR PATH***
4. see if you have `espeak` installed on your machine, as it is used for text-to-speech. (you can verify this by running `espeak <text>` in the terminal), sys will still work without it, but you won't get any audio feedback.
2. get (ollama)[https://ollama.com] and ensure that the server is running on your machine.
3. browse the (models available on ollama)[https://ollama.com/search] and find the model you want to use. The default model for this project is `dolphin-llama3`
4. run `ollama run <model name>` in the terminal to start the model, ***WAIT FOR IT TO FINISH DOWNLOADING***
5. ctrl-d to exit the model.
6. run `aisys` in the terminal to start the AI agent.
7. if the AI model you selected is not the default, run the command `/setmodel <model name>` to change the model.
8. enjoy!

## How to run
1. run `aisys` in the terminal
2. type in your query
3. wait for the response

## Commands
- `/setmodel <model name>`: sets the model to the one specified
- `/exit`: exits the program
GAForecast 0.3.0
Prediction library for tabular data powered by neural network.
Requires Python: <3.11,>=3.9

# GAForecast
This Python library is designed for making predictions on tabular data using a neural network based on ResNet50. The library's strength lies in its ability to excel in predicting time series data presented in a tabular format, where each row contains multiple historical time points.

## Source code
https://github.com/mcXrd/GAForecast

## Requirements
Prior to utilizing this library, please ensure that you have installed TensorFlow on your system.

Additionally, having access to a GPU is highly recommended.

## Memory requirements
Table with 70 features will require cca 7gb of GPU memory.

## Installation
You can install the GAForecast library from pypi:  https://pypi.org/project/GAForecast/


## Usage
```
from gaforecast.models.binary_classifier import GAForecastBinaryClassifier

# Create an instance of the GAForecastBinaryClassifier
clf = GAForecastBinaryClassifier()

# Fit the classifier to your tabular data
clf.fit(X_train, y_train)

# Make predictions on new data
y_pred = clf.predict(X_test)
```

## Internals
Transforms tabular data into images using the Gramian Angular Field technique.
Reshapes these images to fit into the ResNet50 architecture.
Builds a classifier on top of TensorFlow's ResNet50.
Offers a common scikit-learn interface for ease of use.


## Future Updates
In future updates, the library will include:

### 1) A regressor for continuous prediction tasks.
### 2) Support for multi-class classification.
Please stay tuned for these upcoming enhancements.

# License
This library is provided under the MIT License.

# Contact
For any questions or issues, please contact [matejka.vaclav@gmail.com].

# Acknowledgments
We would like to especially acknowledge the contributions of the pandas, numpy, scikit-learn, pytz, open-cv and TensorFlow communities for their invaluable libraries and resources.