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
• /predict-url for URL classification via your scikit-learn Random Forest model

Files

• Dockerfile - builds a small FastAPI server image
• app.py - FastAPI app that loads the model and returns { label, score }.
• 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