Smart Fan Control System Using Object Detection

A real-time object detection system that controls a virtual fan based on the presence of chair and minifan objects.

Features

• Real-time webcam object detection
• Image upload for detection
• Smart fan control logic
• Visual fan simulation
• Multiple camera support
• Confidence threshold adjustment

Local Development

1. Install dependencies:

pip install -r requirements.txt

2. Run the app:

streamlit run streamlit_app.py

Deployment Options

Option 1: Streamlit Community Cloud (Recommended)

1. Push your code to GitHub

   • Make sure your best.pt model file is in the Deploy/ folder
   • Commit and push all files

2. Deploy on Streamlit Cloud

   • Go to https://streamlit.io/cloud
   • Sign in with GitHub
   • Click "New app"
   • Select your repository
   • Set the app file path to: Deploy/streamlit_app.py
   • Click "Deploy"

Option 2: Render.com

1. Create a new Web Service
2. Connect your GitHub repository
3. Set build command:

   pip install -r Deploy/requirements.txt

4. Set start command:

   streamlit run Deploy/streamlit_app.py --server.port $PORT --server.address 0.0.0.0

Option 3: Railway

1. Connect your GitHub repository
2. Railway will auto-detect it's a Python app
3. Add environment variable:
   • PORT=8501

File Structure

Deploy/
├── streamlit_app.py      # Main Streamlit application
├── best.pt              # Your trained YOLO model
├── requirements.txt     # Python dependencies
├── .streamlit/
│   └── config.toml     # Streamlit configuration
└── README.md           # This file

Model Information

• Model: Custom YOLO model (best.pt)
• Classes: chair, minifan
• Input: Images or webcam feed
• Output: Object detection with bounding boxes and fan control

Troubleshooting

• Webcam not working: Check camera permissions and try different camera indices
• Model not loading: Ensure best.pt is in the correct location
• Deployment issues: Check that all files are committed to GitHub