[TANISH-88]

Summary

Adds an interactive Streamlit web UI for the phishing-spam-detector project, allowing users to paste a message and get a real-time spam/phishing prediction with a confidence score.

Changes Made

• Added app.py with a Streamlit interface
• Reuses load_dataset() and preprocess_text() from train_and_predict.py
• Trains and caches a TF-IDF + Multinomial Naive Bayes model on app startup using @st.cache_resource
• Includes a text area for custom message input
• Added two sample buttons ("Test Scam Alert" and "Test College Club Message") for quick demo
• Displays prediction (Spam/Phishing vs Legitimate) with a confidence percentage and progress bar
• Added streamlit to requirements.txt
• Updated README.md with instructions on running the app, and checked off the Streamlit challenge in the Student Challenges section

Motivation & Context

The existing project only ran from the command line. This adds a simple, user-friendly web interface so students and club members can interactively test the spam classifier without touching the terminal — useful for presentations, portfolios, and hackathons.

Related Issue

Closes #7

Testing & Verification

• Code runs without syntax or runtime errors.
• Markdown renders properly without broken tables, tags, or links.
• Verified dependencies in requirements.txt (if code was added).

Screenshots / Demos

Tested both sample buttons:

• "Test Scam Alert" → correctly flagged as SPAM/PHISHING (89.6% confidence)
• "Test College Club Message" → correctly flagged as LEGITIMATE (88.5% confidence)

Contributor Checklist

• I have tested my changes locally.
• Documentation has been updated accordingly.
• No sensitive information (API keys, passwords, student personal data) is included.
• All external and relative links have been verified and are working.
• My content adheres to the repository standards and Code of Conduct.

[github-actions]

🎉 Thank you for your Pull Request, @TANISH-88!

Welcome to AIML Club OCT! 🚀

A maintainer will review your code shortly. In the meantime:

• 📋 Please ensure your branch follows our Contributing Guide.
• 💬 Showcase your work or ask questions in our Student Project Showcase.
• 📌 Track ongoing milestones on our Roadmap Board.

Happy coding! 💻✨

[TANISH-88]

please review the approval

[UmeshCode1]

Excellent work @TANISH-88! 🎉

The Streamlit web UI is clean, intuitive, and works seamlessly.
Key highlights:

• Proper model caching with @st.cache_resource for fast load times.
• Handy quick-test demo buttons (Test Scam Alert and Test College Club Message).
• Clean probability breakdown and metric gauge.
• Dependencies and documentation are properly updated.

Approved and merging now! Keep up the great contributions! 🚀