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1dc2504 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | # 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
```bash
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
```bash
cd deepfake-detection
./scripts/start_frontend.sh
```
Open **http://localhost:5173** in your browser.
---
## 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
---
## 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`.
---
## API Reference
### `POST /predict`
Upload a video file (multipart form-data):
```bash
curl -X POST http://localhost:8000/predict \
-F "file=@my_video.mp4"
```
Response:
```json
{
"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`
```json
{ "status": "ok", "model_loaded": true }
```
---
## Fresh Machine Setup
```bash
# 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)
```
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