docs/README_DEMO.md

Demo — Running the Deepfake Detector

Prerequisites

• Python 3.11 virtual environment at .venv311 (already set up)
• Node.js ≥ 18 (for the frontend)
• A trained model checkpoint at outputs/best.pt

Quick Start (two terminals)

Terminal 1 — Inference API

cd deepfake-detection
./scripts/start_api.sh

The API starts at http://localhost:8000
Check it's alive: curl http://localhost:8000/health

Terminal 2 — Web Frontend

cd deepfake-detection
./scripts/start_frontend.sh

Open http://localhost:5173 in your browser.

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Using the Demo

1. Open http://localhost:5173
2. Drag & drop a video (MP4, MOV, AVI, MKV, WebM) or click to browse
3. Click Analyse Video
4. The result panel shows:
   • REAL (green) or FAKE (red) verdict
   • Confidence score and blink rate
   • Per-sequence probability chart

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No checkpoint yet?

The API runs in mock mode until outputs/best.pt exists.
Mock mode returns a hard-coded FAKE prediction so you can test the UI
before training finishes.

Once training completes (or after epoch 1), the checkpoint is saved automatically at outputs/best.pt.

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API Reference

POST /predict

Upload a video file (multipart form-data):

curl -X POST http://localhost:8000/predict \
  -F "file=@my_video.mp4"

Response:

{
  "label": "FAKE",
  "confidence": 0.82,
  "blink_rate": 0.3,
  "frame_scores": [0.75, 0.84, 0.87, 0.80],
  "attention_map_url": null
}

GET /health

{ "status": "ok", "model_loaded": true }

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Fresh Machine Setup

# 1. Clone repo and cd into it
# 2. Create Python 3.11 venv
pyenv local 3.11.11
python -m venv .venv311
source .venv311/bin/activate
pip install -r requirements.txt
pip install -r api/requirements.txt

# 3. Install frontend deps
cd frontend && npm install && cd ..

# 4. Start API + frontend (two terminals, as above)