Feat:AUTO Mode for YOLO Model Selection

### Describe the feature

Currently, PictoPy requires users to manually select the YOLO model size (nano, small, or medium). While this offers flexibility, it requires users to understand performance–accuracy trade-offs and system limitations.

**Introduce an AUTO mode that dynamically selects the most suitable YOLO model at runtime based on contextual information such as input characteristics and system resources.**

### Dynamic Decision Factors

**When AUTO mode is enabled, the system should evaluate multiple signals before choosing the model:**

- Image resolution (small images → lighter models)
- Estimated number of faces or objects
- User priority (speed vs accuracy)
- GPU availability and free memory
- Task type (face detection or object detection)

**This allows the system to adapt model choice per request instead of relying on a static configuration.**

### Multi-Stage Selection Strategy

**AUTO mode can follow a staged decision approach:**

- Pre-inference checks
Use lightweight models for small images or limited hardware resources.

- Fast probe detection
Run a quick detection pass using a lightweight model to estimate face/object count.

- Runtime fallback
Automatically downgrade the model if inference fails (e.g., GPU out-of-memory).

### User Benefits

- Smarter default behavior without manual tuning
- Improved performance and resource utilization
- Reduced inference failures on constrained systems
- Better overall user experience, especially for new users

### Backward Compatibility

- Existing manual model selection (nano, small, medium) remains unchanged
- AUTO mode is optional and opt-in
- No breaking changes to current workflows , raise an issue for gsoc style in github

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