6a41e75852 | ||
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.. | ||
coffeehouse_spamdetection | ||
MANIFEST.in | ||
README.md | ||
requirements.txt | ||
setup.py |
README.md
CoffeeHouse SpamDetection
Library for detecting spam by classifying input as spam/ham
Installation
Install the following packages using the corresponding setup and makefile operations provided by the repo, or use CoffeeHouse-Server's install script to install all the required components
- Hyper-Internal-Service
- CoffeeHouse-NLPFR
- CoffeeHouse-DLTC
- CoffeeHouseMod-Tokenizer
- CoffeeHouseMod-StopWords
- CoffeeHouseMod-APT
Finally, install CoffeeHouse-SpamDetection by running python3 setup.py install
Build Model
You can update the model build by adding new data to .dat files located in
model/spam_ham/
then proceed to build the model by running ./build_model
.
This process will product a directory called spam_ham_build
which you should
copy over to coffeehouse_spamdetection/
and replace the already existing
files. This process is resource intensive so make sure you are running
this operation on supported chipsets that were manufactured after 2014.
Example Usage
from coffeehouse_spamdetection.main import SpamDetection
spam_detection = SpamDetection()
spam_detection.predict("Test")
# {'ham': 0.998092, 'spam': 0.0017609089}
Start as server
python3 -m coffeehouse_spamdetection --start-server
This process will run using port 5601
and only accepts POST requests
with the parameter input
as plain text. You should recieve a JSON
response that looks like this
{
"status": true,
"results": {
"ham": "0.998092",
"spam": "0.0017609089"
}
}