# AI Queue Management for CCTV and YOLO

## System Architecture
The system consists of three main components:
1.  **Vision Engine**: Uses YOLOv8 (via Ultralytics) and Roboflow Supervision to track people and calculate their "time in zone" (dwell time).
2.  **Log Analysis Engine**: Uses Qwen-2.5-1.5B-Instruct (via Hugging Face Transformers) to process structured logs and provide actionable insights.
3.  **User Interface**: A Gradio/Streamlit dashboard for real-time monitoring, log visualization, and AI-powered reporting.

## Expanded Use Cases
Beyond basic queue monitoring, the system can be applied to:
*   **Retail Heatmap & Dwell Time**: Identify which product sections attract the most customers and how long they stay.
*   **Bank Branch Efficiency**: Analyze service times at different counters (as seen in the provided log) to optimize staffing.
*   **Airport Security Checkpoints**: Predict wait times and alert staff to open new lanes before overflows occur.
*   **Hospital Emergency Rooms**: Monitor patient waiting areas to ensure timely triage and care.
*   **Smart Parking**: Track how long vehicles stay in specific zones to manage turnover and billing.
*   **Safety Monitoring**: Detect if individuals stay too long in restricted or hazardous zones.

## Log Data for LLM
The following structured data will be fed to the LLM for analysis:
*   **Branch/Location**: Context for the analysis.
*   **Throughput**: Total customers served.
*   **Wait Time Metrics**: Average and maximum wait times.
*   **Service Efficiency**: Average service time per counter.
*   **Peak Hours**: Identification of the busiest periods.
*   **Anomaly Events**: Queue overflow events or long wait time alerts.