semvec/pypi-packages huggingface dataset:
General-facts 0.0.1
a python module named General_facts is use to generate facts
License: MIT
Keywords: Facts

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    ||                                       General_facts                                        ||
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========
purpose:
========



1) use to generate random facts from specific topics .eg: science and general knowledge
2) can be generated in form of notification,message or by speaking
3) also used to generate youtube links for more facts.


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INFO:
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1)make sure to install plyer,pyttsx3 and googlesearch module before using this module

================
How to download:
================

first install the General_facts module using


>>> pip install General_facts


then after downloading,go to your IDE and import module 
and follow the steps below 

===========
How to use:
===========

> from General_facts.facts_generate import Facts 
> Facts.generate_random_facts(notify : True,message : False,speak : False,minutes_in_float : 60.0) 

"""the function 'generate_random_facts' will help to generate random facts and display it in form of either notify,speak or message
    in between specific period of time interval"""

> Facts.generate_science_facts(notify : False,message : True,speak : False,minutes_in_float : 40.0) 

"""the function 'generate_science_facts' will help to generate only science related facts and display it in form of either notify,speak or message
    in between specific period of time interval"""

>Facts.generate_links(notify : True,message : False,minutes_in_float : 40.0)

"""the function 'generate_links' will help to generate youtube links of 'more facts' videos and display it in form of either notify or message 
    in between specific period of time interval"""




Change Log
===========


0.0.1 (1/08/2022)
------------------

- First Release
Bio-Epidemiology-NER 0.1.3
Recognize bio-medical entities from a text corpus
License: MIT

Bio-Epidemiology-NER is an Python library built on top of biomedical-ner-all model to recognize bio-medical entities from a corpus or a medical report

| Feature  | Output  |
|---|---|
| Named Entity Recognition | Recognize 84 bio-medical entities |
| PDF Input | Read Pdf and tabulate the entities|
| PDF Annotation | Annotate Entities in a medical pdf report|


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Bio-Epidemiology-NER

```bash
pip install Bio-Epidemiology-NER
```

This package has dependency over Pytorch, please install the required configuration from this link https://pytorch.org/get-started/locally/

## Usage

## NER with Bio-Epidemiology-NER
```python
# load all the functions
from Bio_Epidemiology_NER.bio_recognizer import ner_prediction

# returns the predicted class along with the probability of the actual EnvBert model
doc = """
	CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations. 
      The symptoms occurred during rest, 2â€“3 times per week, lasted up to 30 minutes at a time 
      and were associated with dyspnea. Except for a grade 2/6 holosystolic tricuspid regurgitation 
      murmur (best heard at the left sternal border with inspiratory accentuation), physical 
      examination yielded unremarkable findings.
      """

# returns a dataframe output
ner_prediction(corpus=doc, compute='cpu') #pass compute='gpu' if using gpu

```

## Annotate the entities in a Medical Report and export as pdf/csv format 
```python
# load all the functions
from Bio_Epidemiology_NER.bio_recognizer import pdf_annotate

# enter pdf file name
pdffile = 'Alhashash-2020-Emergency surgical management.pdf'

# returns a annotated pdf file
pdf_annotate(pdffile,compute='cpu', output_format='pdf') #pass compute='gpu' if using gpu

# returns a csv file with entities
pdf_annotate(pdffile,compute='cpu', output_format='csv') #pass compute='gpu' if using gpu

# return both annotated pdf and csv file
pdf_annotate(pdffile,compute='cpu', output_format='all') #pass compute='gpu' if using gpu

# if you come across any pdf issues, please install PyMuPDF 1.20.1 version

```


## About
This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset),

Please cite us and star at: https://github.com/dreji18/biomedicalNER

## License
[MIT](https://choosealicense.com/licenses/mit/) License