README.md

metadata

title: PhishWatch Proxy
emoji: 🛡️
sdk: docker

Hugging Face Space - Phishing Text Classifier (Docker + FastAPI)

This Space exposes two endpoints so the Flutter app can call them reliably:

• /predict for text/email/SMS classification via Transformers. Returns { label, score } where score is the confidence for the predicted label.
• /predict-url for URL classification via your URL model. Returns { label, score, phishing_probability, backend, threshold } where:
  • phishing_probability is always the raw probability of phishing (0..1)
  • label is PHISH when phishing_probability >= threshold, else LEGIT
  • score is the confidence for the predicted label (for LEGIT, score = 1 - phishing_probability), which lets the app show "Safe Confidence" for legitimate URLs

Files

• Dockerfile - builds a small FastAPI server image
• app.py - FastAPI app that loads the model and returns normalized responses as above.
• requirements.txt - Python dependencies.

How to deploy

1. Create a new Space on Hugging Face (type: Docker).
2. Upload the contents of this hf_space/ folder to the Space root (including Dockerfile).
3. In Space Settings → Variables, add:
   • MODEL_ID = Perth0603/phishing-email-mobilebert
   • URL_REPO = Perth0603/Random-Forest-Model-for-PhishingDetection
   • URL_FILENAME = url_rf_model.joblib (set to your artifact filename)
4. Wait for the Space to build and become green. Test:
   • GET / should return { status: ok, model: ... }
   • POST /predict with { "inputs": "Win an iPhone! Click here" }
   • POST /predict-url with { "url": "https://example.com/login" }

Flutter app config

Set the Space URL in your env file so the app targets the Space instead of the Hosted Inference API:

{"HF_SPACE_URL":"https://<your-space>.hf.space"}

Run the app:

flutter run --dart-define-from-file=hf.env.json