---
title: AI Queue Management - Time in Zone Tracking
emoji: 🎯
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.4.0
app_file: app.py
pinned: false
license: mit
---

# AI Queue Management System - Time in Zone Tracking

An end-to-end AI-powered queue management solution that combines computer vision for real-time tracking with Large Language Models for business intelligence.

## 🚀 Features

- **Real-time Object Tracking**: YOLOv8 detection with ByteTrack tracking
- **Time-in-Zone Analytics**: Precise measurement of dwell time in defined zones using Roboflow Supervision
- **AI-Powered Insights**: LLM analysis of performance logs using Qwen-2.5-1.5B-Instruct
- **Comprehensive Error Handling**: Robust error handling throughout the application with graceful degradation
- **Multiple Input Formats**: Support for video, image, and YouTube URL processing
- **YouTube Integration**: Optional support for processing YouTube videos with real-time streaming
- **Import Error Handling**: Graceful handling of missing dependencies with informative error messages

## 📋 Use Cases

- **Retail Analytics**: Track customer movement and dwell time in product sections
- **Bank Branch Efficiency**: Monitor counter service times and optimize staffing
- **Airport Security**: Predict wait times and manage security lane staffing
- **Hospital ER**: Ensure patients are seen within target wait times
- **Smart Parking**: Monitor parking bay occupancy and turnover rates
- **Safety Monitoring**: Alert security if someone enters or lingers in restricted areas

## 🛠️ Technical Stack

- **Detection Model**: YOLOv8 (Ultralytics)
- **Tracking**: ByteTrack (Supervision)
- **Time Tracking**: Supervision TimeInZone
- **LLM**: Qwen-2.5-1.5B-Instruct
- **Framework**: Gradio

## 📦 Installation

### Local Installation

```bash
pip install -r requirements.txt
```

### Running Locally

```bash
python app.py
```

The application will be available at `http://localhost:7860`

## 🚀 Deployment on Hugging Face Spaces

### Step 1: Create a New Space

1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
2. Click "Create new Space"
3. Choose:
   - **SDK**: Gradio
   - **Hardware**: CPU Basic (free) or upgrade to GPU if needed
   - **Visibility**: Public or Private

### Step 2: Upload Files

Upload the following files to your Space:

- `app.py` - Main application file
- `queue_monitor.py` - Core tracking logic
- `llm_analyzer.py` - LLM analysis component
- `requirements.txt` - Python dependencies
- `README.md` - This file

### Step 3: Configure Environment (Optional)

The application uses a Hugging Face token for model access. You can configure it in two ways:

**Option 1: Environment Variable (Recommended for Spaces)**
1. Go to Space Settings
2. Add a **Secret** named `HF_TOKEN`
3. Paste your Hugging Face token (get it from [Settings](https://huggingface.co/settings/tokens))

**Option 2: Default Token**
The application includes a default token for testing. For production, use Option 1.

### Step 4: Deploy

The Space will automatically build and deploy. You can monitor the build logs in the Space interface.

## 📖 Usage

### Video Processing

1. Upload a video file (MP4, AVI, MOV)
2. Adjust confidence threshold (0.1-1.0)
3. Set maximum frames to process
4. Click "Process Video"
5. View processed frame and zone statistics

### YouTube Processing (Optional)

1. Enter a YouTube URL in the YouTube Processing tab
2. Choose between "Download & Process" (full video) or "Real-time Stream" (single frame)
3. Adjust confidence threshold
4. View processed results with zone tracking
5. **Note**: Requires `pytube` library. Install with: `pip install pytube`

### Image Processing

1. Upload an image (JPG, PNG)
2. Adjust confidence threshold
3. Click "Process Image"
4. View annotated image with zone tracking

### AI Log Analysis

1. Enter queue log data in JSON format (or use sample)
2. Click "Generate AI Insights"
3. Review AI-generated recommendations

## 📊 Log Data Format

The LLM expects logs in the following JSON format:

```json
{
  "date": "2026-01-24",
  "branch": "SBI Jabalpur",
  "avg_wait_time_sec": 420,
  "max_wait_time_sec": 980,
  "customers_served": 134,
  "counter_1_avg_service": 180,
  "counter_2_avg_service": 310,
  "peak_hour": "12:00-13:00",
  "queue_overflow_events": 5
}
```

## 🔧 Configuration

### Default Zone

The application uses a default rectangular zone. You can modify it in `app.py`:

```python
DEFAULT_ZONE = np.array([[100, 100], [1100, 100], [1100, 600], [100, 600]])
```

### Model Configuration

- **YOLO Model**: Defaults to `yolov8s.pt` (can be changed in `QueueMonitor.__init__`)
- **LLM Model**: Defaults to `Qwen/Qwen2.5-1.5B-Instruct` (can be changed in `LogAnalyzer.__init__`)

## ⚠️ Error Handling

The application includes comprehensive error handling for:

- Invalid video/image formats
- Model loading failures
- Zone configuration errors
- JSON parsing errors
- Processing exceptions
- Memory management
- Frame processing errors

## 📝 License

MIT License

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## 📧 Support

For issues and questions, please open an issue on the repository.