semvec/pypi-packages huggingface dataset:
ex-pro-streamlit-mdm-table 0.0.9
Streamlit component that allows for easy management of task mapping. Specific to a project. Do not use. For demonstration purposes only.
Requires Python: >=3.7

# streamlit-mdm-table

Streamlit component that allows for easy mapping of tasks and IDs to a table.
This is specific for a project and may not be useful for other projects.
Do not use.

## Installation instructions

Import the wheel file in your project.

## Usage instructions

```python
import streamlit as st

from streamlit_mdm_table import streamlit_mdm_table

value = streamlit_mdm_table(
    tasks, 
    linkedin_content, 
    youtube_content,
    google_analytics_content,
    poppulo_harmony_content,
    poppulo_email_content,
    sharepoint_content,
    viva_engage_content
)

if value is not None:
    st.dataframe(value)
```
gw-foresight 0.3.0
Glasswall Foresight API Python Wrapper
Requires Python: >=3.6

# Foresight: Advanced Malware Prediction Library

Foresight is a sophisticated malware prediction library that harnesses the power of deep Content Disarm & 
Reconstruction (CDR) analysis data from the Glasswall CDR engine. This data is then fed into a 
Machine Learning (ML) model to achieve its primary objective: providing a probability metric that indicates the 
likelihood of malware presence within a file.

The `gw-foresight` Python module serves as a convenient Python wrapper for the Glasswall Foresight library APIs. 
It allows users to seamlessly integrate malware prediction capabilities into their Python applications.

## Installation

To get started with the `gw-foresight` module, install it via pip:

```sh
pip install gw-foresight
```

## Usage
Hereâ€™s a simple example to demonstrate its usage:

```python
from gw_foresight import foresight as fs

# Initialize the Foresight object with the path to the Glasswall Foresight library
foresight = fs.Foresight(r"path-to-foresight-library")

# Define paths to the necessary files
input_file = "path-to-input-file"
model_path = "path-to-machine-learning-model"
output_file = "path-to-output-file"
config_file = "path-to-configuration-file"

# Analyze the file and get the malware probability report
fs_report, _, _ = foresight.predict(input_file, model_path, output_file, config_file)

# Print the prediction report
print(fs_report)
```
pybeario 0.0.1
Jerbeario's python style package
Requires Python: >=3.8

# pybeario
A python package for Jerbeario's brand images. 

CURRENTLY IN DEVELOPMENT

## Features so far include:

### Two personalized matplotlib style sheets (.mplsheet):
   - bauhaus_dark
   - bauhaus_light

### Colors 
   - red : 5 value gradient to cream
   - blue : 5 value gradient to cream
   - green : 5 value gradient to cream
   - yellow : 5 value gradient to cream
   - purple : 5 value gradient to cream
   - grayscale : 5 value gradient white to black 

### Functions
   - gradient : takes two colors and an int n. Returns gradient between colors in n steps.
   - divergent : takes two colors and an int n. Returns diverging palette between colors in n steps

More to come as I develop my brand image.
jerbeariob 0.0.1
Jerbeario's python style package
Requires Python: >=3.8

# pybeario
A python package for Jerbeario's brand images. 

CURRENTLY IN DEVELOPMENT

## Features so far include:

### Two personalized matplotlib style sheets (.mplsheet):
   - bauhaus_dark
   - bauhaus_light

### Colors 
   - red : 5 value gradient to cream
   - blue : 5 value gradient to cream
   - green : 5 value gradient to cream
   - yellow : 5 value gradient to cream
   - purple : 5 value gradient to cream
   - grayscale : 5 value gradient white to black 

### Functions
   - gradient : takes two colors and an int n. Returns gradient between colors in n steps.
   - divergent : takes two colors and an int n. Returns diverging palette between colors in n steps

More to come as I develop my brand image.
stack-package-aps-techinfo 0.0.1
stack module
Requires Python: >=3.8

# Stack Module

A simple stack implementation in Python.

## Installation

Simply download the `stack.py` file.

## Usage

```python
from stack import *
# Push items
#list iteam only allowed
print(push([1,2,3,4,5,6,7]))
# Peek at the top item
print(peek())

# Pop items
print(pop())
print(pop())  # Stack is Empty

# Check length
print(length())
hypersquirrel 1.0.38
Scrapes posts from various websites
Requires Python: >=3.6

# hypersquirrel

## Usage

Module

```bash
pip install hypersquirrel
```

```python
from hypersquirrel import scrape

for file in scrape("scrape_url"):
    print(file)
```

Standalone

```bash
docker-compose up --build -d
```

```bash
curl --location --request POST 'localhost:5000/scrape' \
  --header 'Content-Type: application/json' \
  --data-raw '{ "url": "scrape_url" }'
```
dataimputer 0.0.2
LLM based text categorization tool
Requires Python: >=3.8

# Categorizer


## Categorizer is a simple tool which you can use to categorise your string records into predefined -nested- categories using the power of LLMs. 

- upload your categories and subcategories ( read from yaml file or create them on the fly )
- initialize LEC. You can use different modes and your own task-specific prompts if you like
- Run the LEC and it will output a dataframe with all categories and subcategories and llm's reasoning to select them
- It is possible to leave notes for LLM for each category to help him categorize with more accuracy
- You can also use included naive classification method which supports regex based or keyword matching mechanism to reduce the LLM compute

Here is some benchmarking you to understand better. 

 category depth
 category combination size
 allowed retry

| Number of Records | Main Model      | Refiner Model   | Categorization Mode | Batch Prompting | Accuracy | Total Time | Avg Token | CPU Type           | GPU Type         |
|-------------------|-----------------|-----------------|----------------------|-----------------|----------|------------|-----------|--------------------|------------------|
| 1000              | Model A         | Refiner X       | Mode 1               | Yes             | 92.5%    | 10 mins    | 512       | Intel Xeon E5-2670 | NVIDIA Tesla K80 |
| 2000              | Model B         | Refiner Y       | Mode 2               | No              | 89.0%    | 20 mins    | 1024      | Intel Xeon E5-2680 | NVIDIA Tesla V100|
| 5000              | Model C         | Refiner Z       | Mode 3               | Yes             | 94.7%    | 50 mins    | 768       | AMD EPYC 7742      | NVIDIA A100      |
| 10000             | Model D         | Refiner W       | Mode 4               | No              | 88.3%    | 1 hr 40 mins| 2048     | Intel Xeon E5-2690 | NVIDIA RTX 3090  |

## Usage 

```pip install lec

lec = LLMEnhancedClassifier(
        llm_model=llm_model,
        llm_refiner_model=llm_refiner,
        categories_yaml_path='categories.yaml',
        meta_patterns_yaml_path='bank_patterns.yaml',
        subcategory_level=2  # Change this value to set the number of subcategories (max 4)
    )
    
lec.load_records(df)
 df = lec.classify_lvl_by_lvl()
```