import logging
from PIL import Image

logger = logging.getLogger(__name__)

class ContextualIntelligenceAgent:
    def __init__(self):
        # In a real scenario, this would involve an LLM call or a sophisticated rule engine
        pass

    def infer_context_tags(self, image_data: dict, initial_predictions: dict) -> list[str]:
        """Simulates an LLM inferring context tags based on image data and predictions."""
        context_tags = []
        
        # Boilerplate logic: infer tags based on simple cues
        if image_data.get("width", 0) > 1000 and image_data.get("height", 0) > 1000:
            context_tags.append("high_resolution")
        
        # Example based on initial broad prediction (e.g., if any model strongly predicts 'real')
        if any(v.get("Real Score", 0) > 0.9 for v in initial_predictions.values()):
            context_tags.append("potentially_natural_scene")

        # Mock external detection (e.g., from a simpler scene classification model or EXIF data)
        # For demonstration, we'll hardcode some possible tags here.
        # In a real system, you'd feed actual image features or metadata to an LLM.
        mock_tags = ["outdoor", "sunny"] # These could be returned by an actual LLM based on input
        for tag in mock_tags:
            if tag not in context_tags:
                context_tags.append(tag)

        return context_tags

class ForensicAnomalyDetectionAgent:
    def __init__(self):
        # In a real scenario, this would involve an LLM call to analyze textual descriptions
        pass

    def analyze_forensic_outputs(self, forensic_output_descriptions: list[str]) -> dict:
        """Simulates an LLM analyzing descriptions of forensic images for anomalies."""
        anomalies = {"summary": "No significant anomalies detected.", "details": []}

        # Boilerplate logic: look for keywords in descriptions
        for desc in forensic_output_descriptions:
            if "strong edges" in desc.lower() and "ela" in desc.lower():
                anomalies["summary"] = "Potential manipulation indicated by ELA."
                anomalies["details"].append("ELA: Unusually strong edges detected, suggesting image compositing.")
            if "unexpected patterns" in desc.lower() and "bit plane" in desc.lower():
                anomalies["summary"] = "Anomalies detected in bit plane data."
                anomalies["details"].append("Bit Plane: Irregular patterns found, possibly indicating hidden data or processing.")

        if len(anomalies["details"]) > 0:
            anomalies["summary"] = "Multiple anomalies detected across forensic outputs."
            
        return anomalies