Fake News Detector

A fullstack Fake News Detector using FastAPI as backend and Svelte as frontend for scoring news, highlighting token contributions, and a fact-checking agent.

Features

• Transformer classifier (EN/DE) using a BERT model. The model is trained with real Data.
• Word highlighting.
• Fact-check agent using pydantic-ai
• Svelte with per Word highlighting, classifications, and fact-checks.
• Docker setup with Nginx
• Optional Mongo pipelines and dataset URLs for data ingestion and training.

Repository layout

• detector-backend/: FastAPI app (app/main.py) plus classifier, fact-check agent, pipelines, and tests.
• detector-frontend/: Svelte single-page UI; Nginx config proxies /api to the backend.
• docker-compose.yml: Local stack (frontend, backend, Mongo).
• docker-compose.prod.yml: prebuilt GHCR images.

Quick start with Docker

The default compose file builds everything locally and exposes the frontend on port 80 and backend on port 8000.

1. Clone the repo.

2. Start the stack:

   docker compose up --build
   

3. Open http://localhost for the UI. The backend API is at http://localhost:8000.

The prod compose file uses prebuilt GHCR images

Local development

Backend

• Requirements: Python 3.13, optionally MongoDB if you use the data pipelines.
• Install deps and run:

  cd detector-backend
  uv sync
  uv run uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
  

Frontend

• Requirements: Node 20+
• Install and run dev server:

  cd detector-frontend
  npm install
  VITE_API_URL=http://localhost:8000/api npm run dev -- --host
  

• Build/preview: npm run build && npm run preview

API overview

Base URL /api.

• POST /predict body: { "text": "..." } classifier label (fake|real) with confidence.
• POST /highlight body: { "text": "..." } token list with SHAP scores (score_normalized for heatmap).
• POST /fact-check body: { "text": "..." } structured fact-check (fake_score, summary_analysis, checked_claims).
• GET /health simple { "status": "ok" }.

Example:

curl -X POST http://localhost:8000/api/predict \
  -H "Content-Type: application/json" \
  -d '{"text":"Example news paragraph"}'

Deployment notes

• docker-compose.prod.yml uses published images (ghcr.io/kon-drees/fake-news-detector-{backend,frontend}:latest).
• For fact checking enter a OPENAI_API_KEY on the backend service.