README.md

---
title: Traffic Sign Classification
emoji: 🐠
colorFrom: purple
colorTo: green
sdk: docker
pinned: false
---

# Traffic Sign Classifier Flask App

This project deploys a `traffic_classifier.h5` model as a Flask web app for Hugging Face Spaces with Docker.

## Features

- Welcome page based on the provided visual template direction
- Login and registration
- Protected traffic sign prediction page
- SQLite storage inside the container at `instance/traffic_signs.sqlite3`
- Per-user prediction history
- True/false feedback for every prediction
- Dashboard with total predictions, reviewed predictions, true/false counts, and feedback accuracy

## Run Locally

```bash
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python app.py
```

Open `http://localhost:7860`.

## Model

Place the trained model in the project root:

```text
traffic_classifier.h5
```

The app expects a 43-class traffic sign classifier using 30x30 RGB images, matching the common GTSRB class list.

## Hugging Face Space

This Space uses Docker and exposes port `7860`.

For production, set a strong secret:

```text
SECRET_KEY=your-secret-value
```

## Docker Deployment

### Prerequisites
- Docker installed on your system
- Docker Hub account (for pushing to registry)
- All project files including `traffic_classifier.h5`

### Building Docker Image

Build the Docker image locally:

```bash
docker build -t traffic-sign-classifier:latest .
```

### Running Docker Container Locally

Run the container on your local machine:

```bash
docker run -p 7860:7860 \
  -e SECRET_KEY=your-secret-key \
  -v $(pwd)/instance:/app/instance \
  traffic-sign-classifier:latest
```

Then access the application at `http://localhost:7860`.

### Pushing to Docker Hub

1. Tag the image:
```bash
docker tag traffic-sign-classifier:latest yourusername/traffic-sign-classifier:latest
```

2. Push to Docker Hub:
```bash
docker login
docker push yourusername/traffic-sign-classifier:latest
```

### Deploying to Hugging Face Spaces

1. Create a new Space on [Hugging Face Spaces](https://huggingface.co/spaces)
2. Select **Docker** as the SDK
3. In the Space settings, set environment variable:
   - `SECRET_KEY=your-production-secret`
4. Upload your project files including:
   - `Dockerfile`
   - `app.py`
   - `requirements.txt`
   - `traffic_classifier.h5`
   - `templates/` directory
   - `static/` directory

5. Hugging Face will automatically build and deploy the container
6. Your app will be accessible at `https://huggingface.co/spaces/YOUR-USERNAME/YOUR-SPACE-NAME`

### Docker Compose (Optional)

Create a `docker-compose.yml` for local development:

```yaml
version: '3.8'
services:
  traffic-classifier:
    build: .
    ports:
      - "7860:7860"
    environment:
      - SECRET_KEY=dev-secret-key
      - FLASK_ENV=development
    volumes:
      - ./instance:/app/instance
      - ./templates:/app/templates
      - ./static:/app/static
```

Run with:
```bash
docker-compose up
```

### Persistent Data

The SQLite database is stored in the `instance/` directory, which is mounted as a volume. This ensures data persists across container restarts.

### Health Check

To verify the container is running:

```bash
curl http://localhost:7860/
```

You should receive the welcome page HTML.

`