Metadata-Version: 2.1
Name: cuss-inspect
Version: 1.0.2b0
Summary: A basic and simple yet powerful Python library to detect toxicity/profanity of a review or list of reveiws.
Home-page: https://github.com/tushar50896/cuss_inspect
Author: Tushar, Lalit, Gurwinder
Author-email: tushar50896@gmail.com, lalitsharma2395@gmail.com,sgurwinderr@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: scikit-learn (>=0.23.2)
Requires-Dist: pandas (>=1.1.3)
Requires-Dist: numpy (>=1.19.2)

# cuss_inspect

## How It Works

`cuss_inspect` is a logistic regression based model trained on 180K+ reviews and tested on 24K+ reviews.

### Performance


|  | 1 Prediction (ms) | 10 Predictions (ms) | 100 Predictions (ms) | 1000 Predictions (ms) | 10000 Predictions (ms)
| --------|-------------------|---------------------|-----------------------| -----------------------|-----------------------
| cuss_inspect | 0.2 | 0.3 | 0.8 | 4.3 | 24.7




### Accuracy



| | Precision | Recall | F1 Score
| --- | ------- | ------------- | ----------------------
0 | 0.84 | 0.94 | 0.89
1 | 0.99 | 0.96 | 0.98
Accuracy | | | 0.96
macro avg | 0.91 | 0.95 | 0.93
weighted avg | 0.96 | 0.96 | 0.96


### Receiver Operating Characteristics
![ROC Curve](https://github.com/LMSharma/cuss_inspect/blob/main/ROC_Curve.jpeg)

## Installation

```
$ pip install cuss_inspect
```

## Usage

```python
from cuss_inspect import predict, predict_prob

# for simple string
text_0 = "this is simple review. you have done a good job"
print(predict(text_0))
# [0]
print(predict_prob(text_0)
# [0.05]

text_1 = "son of a bitch"
print(predict(text_1))
# [1]
print(predict_prob(text_1)
# [1.]

# for list of inputs
test = ['who are you?' , 'what do you want?' , 'son of a dog' , 'how the hell can you say that' , 'fuck it']
print(predict(test))
# [0 0 1 1 1]
print(predict_prob(test))
# [0.12 0.22 0.55 0.96 1.]

```

*`predict()` and `predict_prob` return [`numpy`](https://pypi.org/project/numpy/) arrays.


