How irtazahussain11223/smart-spam-email-sms-classifier works
This is a small Streamlit spam-classification app. A user selects SMS or email, enters text, and submits it; the app transforms that text with the saved TF-IDF vectorizer, predicts with the saved Logistic Regression model, and displays Spam or Ham. The source shows these runtime calls, but does not expose separate preprocessing code or the model-training workflow.
Main entry points: app.py Streamlit app · vectorizer.pkl TF-IDF vectorizer · model.pkl Logistic Regression
Components 7
Other components
- User
- Streamlit app
app.py - SMS input
app.py - Email input
app.py - TF-IDF vectorizer
vectorizer.pkl - Logistic Regression
model.pkl - Spam/Ham result
app.py
Connections 9
- User → Streamlit app: uses
- Streamlit app → SMS input: offers
- Streamlit app → Email input: offers
- SMS input → Streamlit app: submits text
- Email input → Streamlit app: submits text
- Streamlit app → TF-IDF vectorizer: loads and transforms
- Streamlit app → Logistic Regression: loads and predicts
- Streamlit app → Spam/Ham result: displays label
- Spam/Ham result → User: shows result