setup for hf spaces

Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY server/requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY server/ ./server/
+COPY training/exported_models/inference_model.sim.onnx ./model/inference_model.sim.onnx
+
+ENV MODEL_PATH=/app/model/inference_model.sim.onnx
+
+EXPOSE 7860
+
+CMD ["uvicorn", "server.main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -73,6 +73,34 @@ Response:
 
 Returns `{"status": "ok", "model_loaded": true}`.
 
+## Deployment
+
+The backend and frontend are deployed separately so the page loads instantly (static CDN) while the model runs server-side.
+
+### Backend — Hugging Face Spaces
+
+1. Create a new Space at [huggingface.co/new-space](https://huggingface.co/new-space) with **Docker** SDK
+2. Clone the Space repo and copy in the required files:
+   ```bash
+   git clone https://huggingface.co/spaces/YOUR_USER/YOUR_SPACE
+   cd YOUR_SPACE
+   cp /path/to/parking/Dockerfile .
+   cp -r /path/to/parking/server ./server
+   mkdir -p training/exported_models
+   cp /path/to/parking/training/exported_models/inference_model.sim.onnx ./training/exported_models/
+   git add . && git commit -m "Initial deploy" && git push
+   ```
+3. The Space will build the Docker image and start serving at `https://YOUR_USER-YOUR_SPACE.hf.space`
+
+### Frontend — Vercel
+
+1. Push the `frontend/` directory to a GitHub repo (or use the full project repo)
+2. Import the project at [vercel.com/new](https://vercel.com/new)
+3. Set **Root Directory** to `frontend`
+4. Add the environment variable:
+   - `VITE_API_URL` = `https://YOUR_USER-YOUR_SPACE.hf.space`
+5. Deploy — Vercel auto-detects Vite and builds it
+
 ## Model
 
 - Architecture: RF-DETR (medium)

frontend/src/api.ts

@@ -1,4 +1,4 @@
-const API_URL = 'http://localhost:8000';
+const API_URL = (import.meta.env.VITE_API_URL as string) || 'http://localhost:8000';
 
 export interface Detection {
   bbox: [number, number, number, number];

server/detect.py

@@ -1,6 +1,7 @@
 """ONNX inference for car detection in aerial images."""
 
 import base64
+import os
 from pathlib import Path
 
 import cv2
@@ -8,7 +9,8 @@ import numpy as np
 import onnxruntime as ort
 
 _PROJECT_ROOT = Path(__file__).resolve().parent.parent
-MODEL_PATH = _PROJECT_ROOT / "training" / "exported_models" / "inference_model.sim.onnx"
+_DEFAULT_MODEL_PATH = _PROJECT_ROOT / "training" / "exported_models" / "inference_model.sim.onnx"
+MODEL_PATH = Path(os.environ.get("MODEL_PATH", str(_DEFAULT_MODEL_PATH)))
 
 
 def load_model(model_path: Path = MODEL_PATH) -> ort.InferenceSession: