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https://github.com/pramod858/sms-spam-detection
https://github.com/pramod858/sms-spam-detection
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/pramod858/sms-spam-detection
- Owner: Pramod858
- Created: 2023-11-22T09:15:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-22T09:29:44.000Z (about 1 year ago)
- Last Synced: 2023-11-22T10:59:07.931Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 351 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SMS-Spam-Detection with Flask
This project implements a simple spam detection system using a Naive Bayes classifier and exposes it through a Flask web application. It includes Swagger documentation for easy testing.
## Getting Started
### Prerequisites
- [Docker](https://www.docker.com/) installed
### Installation
1. Clone the repository:
```bash
git clone https://github.com/Pramod858/SMS-Spam-Detection.git
```
2. Change into the project directory:
```bash
cd "SMS-Spam-Detection"
```
3. Build the Docker image:
```bash
docker build -t spam_detection_app .
```
or
```bash
docker build -t spam_detection_app -f Dockerfile.txt .
```
5. Run the Docker container:
```bash
docker run -p 5000:5000 spam_detection_app
```
The web application should now be accessible at [http://localhost:5000](http://localhost:5000).
## Usage
1. Open the web application in your browser.
2. Enter a text in the provided form and click "Predict" to see whether it's classified as spam or not.
3. Swagger documentation is available at [http://localhost:5000/apidocs/](http://localhost:5000/apidocs/) for testing the API endpoints.
## Customization
- You can modify the `app.py` file to include additional features, improve preprocessing, or enhance the model.
- Add your trained Naive Bayes model and CountVectorizer to the project.
## Acknowledgments
- [Flask](https://flask.palletsprojects.com/)
- [NLTK](https://www.nltk.org/)
- [Scikit-learn](https://scikit-learn.org/)
- [Flasgger](https://github.com/flasgger/flasgger)