data_models/image_context.py

"""
Image Context Model for EmotionMirror application.

This module defines the ImageContext class that represents
an image analysis context including metadata and results.
"""
from typing import Dict, Any, List, Optional
from datetime import datetime

class ImageContext:
    """
    Represents an image context for analysis including metadata and results.
    """
    
    def __init__(
        self, 
        image_id: str,
        timestamp: Optional[datetime] = None,
        metadata: Optional[Dict[str, Any]] = None,
        faces: Optional[List[Dict[str, Any]]] = None
    ):
        """
        Initialize an image context.
        
        Args:
            image_id: Unique identifier for the image
            timestamp: Time when the image was processed
            metadata: Image metadata like dimensions, format, etc.
            faces: List of detected faces and their analysis
        """
        self.image_id = image_id
        self.timestamp = timestamp or datetime.now()
        self.metadata = metadata or {}
        self.faces = faces or []
        
    def to_dict(self) -> Dict[str, Any]:
        """
        Convert the image context to a dictionary for storage or serialization.
        
        Returns:
            Dictionary representation of the image context
        """
        return {
            "image_id": self.image_id,
            "timestamp": self.timestamp.isoformat(),
            "metadata": self.metadata,
            "faces": self.faces
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'ImageContext':
        """
        Create an image context from a dictionary.
        
        Args:
            data: Dictionary with image context data
            
        Returns:
            An ImageContext instance
        """
        return cls(
            image_id=data.get("image_id", ""),
            timestamp=datetime.fromisoformat(data.get("timestamp", datetime.now().isoformat())),
            metadata=data.get("metadata", {}),
            faces=data.get("faces", [])
        )