agents/smart_agents.py

import logging
from PIL import Image
import smolagents
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 = ["foo", "bar"] # 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."""
        import random
        
        # 4 mock anomalies for demo purposes
        mock_anomalies = [
            {
                "summary": "ELA analysis reveals potential image manipulation",
                "details": ["ELA: Unusually strong compression artifacts detected", "ELA: Inconsistent noise patterns suggest compositing"]
            },
            {
                "summary": "Bit plane analysis shows irregular patterns",
                "details": ["Bit Plane: Unexpected data patterns in LSB", "Bit Plane: Hidden information detected in lower planes"]
            },
            {
                "summary": "Gradient analysis indicates artificial boundaries",
                "details": ["Gradient: Sharp discontinuities in color transitions", "Gradient: Unnatural edge patterns detected"]
            },
            {
                "summary": "Wavelet analysis reveals processing artifacts",
                "details": ["Wavelet: Unusual frequency distribution", "Wavelet: Compression artifacts inconsistent with natural images"]
            }
        ]
        
        # Randomly select one of the mock anomalies
        selected_anomaly = random.choice(mock_anomalies)
        
        return selected_anomaly