This project provides a machine learning-based API to detect whether a given URL is phishing or legitimate. It is built using FastAPI and deployed on Render for easy public access.
- 🔍 Detects phishing URLs using a trained ML model.
- 🌐 Deployed API with interactive Swagger UI at
/docs. - ⚡ Fast and scalable with FastAPI.
- 🔧 Easy to run locally or deploy in the cloud.
- FastAPI – Web framework for building APIs.
- Uvicorn – ASGI server for local development.
- Gunicorn – Production server for deployment.
- Scikit-learn – Machine Learning model training and inference.
- Pandas, NumPy – Data processing and numerical operations.
- tldextract – Extracts domain information from URLs.
Create & activate virtual environment python3 -m venv venv source venv/bin/activate pip install -r requirements.txt
Start the ASGI server locally on port 8000: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
Phishing_url/ ├── main.py ├── Procfile ├── requirements.txt # Python dependencies
android/
├── AndroidManifest.xml # Android permissions & config
├── build.gradle # Android build file
└── src/
└── main/java/com/example/fake/
└── MainActivity.java # Android client logic
assets/
├── model.pkl # Serialized scikit‑learn model
backend/
├── app/
│ ├── main.py # FastAPI entrypoint
└── requirements.txt # Backend Python dependencies
“Apk file Video”/
└── ML_model_47.mp4 # Screen recording of Android app in action
PhishingDetector.apk # Pre‑built Android APK for quick testing
README.md # Project overview and instructions (you are here)
LICENSE # Project license (Apache 2.0)