🛡️ SpamShield AI

Neural spam, scam & phishing detection — a premium cybersecurity SaaS demo built with Python, Streamlit, scikit-learn and NLP.

SpamShield AI classifies any message as Spam · Safe · Promotional · Scam · Advertisement, with confidence scoring, threat level, phishing probability, suspicious-keyword highlighting and cybersecurity recommendations — wrapped in a futuristic dark dashboard.

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✨ Features

• 🧠 Real-time AI classification (TF-IDF + Multinomial Naive Bayes, with Logistic Regression comparison)
• 🔬 Full NLP pipeline — lowercase, punctuation strip, stopword removal, stemming, TF-IDF vectorization
• 📊 Analytics dashboard — category distribution, top suspicious keywords, detection timeline, model metrics
• 🚨 Threat-level system — Low · Medium · High · Critical
• 🔗 Phishing & URL heuristics — IP-address URLs, look-alike domains, shortened links
• 🔥 Dangerous keyword highlighter
• 🗂️ Detection history in session, with CSV / JSON export
• 🛡️ Cyber recommendations for every verdict
• 🎨 Premium dark UI — glassmorphism, neon gradients, animated metrics

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🧱 Architecture

SpamShieldAI/
├── app.py                  # Streamlit dashboard
├── train_model.py          # TF-IDF + NB/LR training script
├── spam_classifier.py      # Prediction API (returns rich JSON report)
├── requirements.txt
├── README.md
├── .streamlit/config.toml  # Dark theme
├── model/
│   ├── model.pkl
│   ├── vectorizer.pkl
│   └── metrics.json
├── dataset/spam.csv        # Seed dataset (550 rows)
├── utils/
│   ├── preprocessing.py    # Clean / tokenize / stem / keyword & URL heuristics
│   ├── helpers.py          # Threat-level, tips, safer alternatives
│   └── visualizations.py   # Plotly charts
└── exported_reports/

AI Workflow

   raw message
      ▼
  [Preprocessing]  ── lowercase · strip punct · stopwords · stem · token-normalize
      ▼
  [TF-IDF (1-2 grams, 5k features)]
      ▼
  [Naive Bayes  /  Logistic Regression] ── best by F1
      ▼
  [Threat scoring] ── phishing prob · URL heuristics · keyword flags
      ▼
   JSON report ─→ Streamlit UI

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🚀 Quick start

git clone https://github.com/your-username/SpamShieldAI.git
cd SpamShieldAI
pip install -r requirements.txt
python train_model.py        # builds model/model.pkl + vectorizer.pkl
streamlit run app.py

The app opens at http://localhost:8501.

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☁️ Deployment

Streamlit Community Cloud

1. Push this repo to GitHub.
2. Go to share.streamlit.io → New app.
3. Pick the repo, set app.py as the entrypoint.

Hugging Face Spaces

1. Create a new Space → SDK: Streamlit.
2. Push this repo to the Space.

Render

1. New → Web Service, connect repo.
2. Build: pip install -r requirements.txt && python train_model.py
3. Start: streamlit run app.py --server.port=$PORT --server.address=0.0.0.0

Replit

1. Import repo.
2. .replit run command: bash -c "pip install -r requirements.txt && python train_model.py && streamlit run app.py --server.port=8080 --server.address=0.0.0.0"

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🛠️ Tech stack

Python · Streamlit · scikit-learn · NLTK · pandas · NumPy · Plotly · pickle · fpdf2

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🧭 Future improvements

• Transformer-based classifier (DistilBERT) alongside the NB baseline
• Multilingual detection (XLM-R)
• Voice input via streamlit-mic-recorder
• AI chatbot assistant for explaining verdicts
• Persistent storage (SQLite / Supabase)
• Browser extension / Gmail plug-in

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🎓 Internship Information

Project Developed During:

Artificial Intelligence Internship

Organization: Codec Technologies Pvt. Ltd.

Duration: 1 Month Internship Program

Domain: Artificial Intelligence & Machine Learning

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👨‍💻 Author

Ardhendu Sekhar Sahoo

Artificial Intelligence Intern

LinkedIn: https://www.linkedin.com/in/ardhendu-sahoo/

GitHub: https://github.com/Ardhendu-dot/SpamShield-AI

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📜 License

This project is intended for educational, learning, and research purposes.