How vijay-1420/spam-message-detector works
This is a Streamlit spam-message classifier. A user enters text and clicks Predict; the app transforms the message with a saved vectorizer, classifies it with a saved model, and displays Spam or Not Spam. The README also describes NLP processing and reports 98% accuracy, but the sampled runtime code does not show how that accuracy was measured or how the artifacts were trained.
Main entry points: app.py Streamlit app · vectorizer.pkl Text vectorizer · spam_model.pkl Spam model
Components 6
Web interface
- Streamlit app
app.py - Message input
app.py - Classification result
app.py
Message inference
- Text vectorizer
vectorizer.pkl - Spam model
spam_model.pkl
Other components
- User
Connections 8
- User → Message input: enters message
- User → Streamlit app: clicks Predict
- Streamlit app → Text vectorizer: loads artifact
- Streamlit app → Spam model: loads artifact
- Streamlit app → Text vectorizer: transforms text
- Streamlit app → Spam model: predicts class
- Streamlit app → Classification result: displays result
- Classification result → User: shows classification