agents/smart_agents.py

import logging
import torch
import numpy as np
from PIL import Image # For image processing context
# import smolagents # Removed unused import

logger = logging.getLogger(__name__)

class ContextualIntelligenceAgent:
    def __init__(self):
        logger.info("Initializing ContextualIntelligenceAgent.")
        # This would be a more sophisticated model in a real scenario
        self.context_rules = {
            "high_resolution": {"min_width": 1920, "min_height": 1080, "tag": "high_resolution_image"},
            "low_resolution": {"max_width": 640, "max_height": 480, "tag": "low_resolution_image"},
            "grayscale": {"mode": "L", "tag": "grayscale_image"},
            "potentially_natural_scene": {"keywords": ["Real"], "threshold": 0.7, "tag": "potentially_natural_scene"},
            "potentially_ai_generated": {"keywords": ["AI", "Fake", "Deepfake"], "threshold": 0.7, "tag": "potentially_ai_generated"},
            "outdoor": {"model_tags": ["sunny", "sky", "trees"], "tag": "outdoor"},
            "indoor": {"model_tags": ["room", "furniture"], "tag": "indoor"},
            "sunny": {"rgb_avg_min": [200, 200, 100], "tag": "sunny"},
            "dark": {"rgb_avg_max": [50, 50, 50], "tag": "dark"},
        }

    def infer_context_tags(self, image_metadata: dict, model_predictions: dict) -> list[str]:
        logger.info("Inferring context tags from image metadata and model predictions.")
        detected_tags = []

        # Analyze image metadata
        width = image_metadata.get("width", 0)
        height = image_metadata.get("height", 0)
        mode = image_metadata.get("mode", "RGB")

        if width >= self.context_rules["high_resolution"]["min_width"] and \
           height >= self.context_rules["high_resolution"]["min_height"]:
            detected_tags.append(self.context_rules["high_resolution"]["tag"])
            logger.debug(f"Detected tag: {self.context_rules['high_resolution']['tag']}")

        if width <= self.context_rules["low_resolution"]["max_width"] and \
           height <= self.context_rules["low_resolution"]["max_height"]:
            detected_tags.append(self.context_rules["low_resolution"]["tag"])
            logger.debug(f"Detected tag: {self.context_rules['low_resolution']['tag']}")

        if mode == self.context_rules["grayscale"]["mode"]:
            detected_tags.append(self.context_rules["grayscale"]["tag"])
            logger.debug(f"Detected tag: {self.context_rules['grayscale']['tag']}")

        # Analyze model predictions for general context
        for model_id, prediction in model_predictions.items():
            label = prediction.get("Label")
            ai_score = prediction.get("AI Score", 0.0)
            real_score = prediction.get("Real Score", 0.0)

            if label and "potentially_natural_scene" not in detected_tags:
                for keyword in self.context_rules["potentially_natural_scene"]["keywords"]:
                    if keyword in label and real_score >= self.context_rules["potentially_natural_scene"]["threshold"]:
                        detected_tags.append(self.context_rules["potentially_natural_scene"]["tag"])
                        logger.debug(f"Detected tag: {self.context_rules['potentially_natural_scene']['tag']}")
                        break # Only add once
            
            if label and "potentially_ai_generated" not in detected_tags:
                for keyword in self.context_rules["potentially_ai_generated"]["keywords"]:
                    if keyword in label and ai_score >= self.context_rules["potentially_ai_generated"]["threshold"]:
                        detected_tags.append(self.context_rules["potentially_ai_generated"]["tag"])
                        logger.debug(f"Detected tag: {self.context_rules['potentially_ai_generated']['tag']}")
                        break # Only add once
        
        # Simulate simple scene detection based on general consensus if available
        # This is a very basic simulation; a real system would use a separate scene classification model
        if "potentially_natural_scene" in detected_tags and "potentially_ai_generated" not in detected_tags:
            # Simulate outdoor/sunny detection based on presence of a real image tag
            # In a real scenario, this would involve analyzing image features
            if real_score > 0.8: # Placeholder for actual image feature analysis
                detected_tags.append(self.context_rules["outdoor"]["tag"])
                detected_tags.append(self.context_rules["sunny"]["tag"])
                logger.debug(f"Simulated tags: {self.context_rules['outdoor']['tag']},{self.context_rules['sunny']['tag']}")
        
        logger.info(f"Inferred context tags: {detected_tags}")
        return detected_tags


class ForensicAnomalyDetectionAgent:
    def __init__(self):
        logger.info("Initializing ForensicAnomalyDetectionAgent.")
        self.anomaly_thresholds = {
            "ELA": {"min_anomalies": 3, "max_error_std": 20}, # Example thresholds
            "gradient": {"min_sharp_edges": 500},
            "minmax": {"min_local_deviation": 0.1}
        }

    def analyze_forensic_outputs(self, forensic_output_descriptions: list[str]) -> dict:
        logger.info("Analyzing forensic outputs for anomalies.")
        anomalies_detected = []
        summary_message = "No significant anomalies detected."

        # Example: Check for ELA anomalies (simplified)
        ela_anomalies = [desc for desc in forensic_output_descriptions if "ELA analysis" in desc and "enhanced contrast" in desc]
        if len(ela_anomalies) > self.anomaly_thresholds["ELA"]["min_anomalies"]:
            anomalies_detected.append("Multiple ELA passes indicate potential inconsistencies.")
            logger.warning("Detected multiple ELA passes indicating potential inconsistencies.")

        # Example: Check for gradient anomalies (simplified)
        gradient_anomalies = [desc for desc in forensic_output_descriptions if "Gradient processing" in desc]
        if len(gradient_anomalies) > 1 and "Highlights edges and transitions" in gradient_anomalies[0]:
            # This is a placeholder for actual image analysis, e.g., checking standard deviation of gradients
            anomalies_detected.append("Gradient analysis shows unusual edge patterns.")
            logger.warning("Detected unusual edge patterns from gradient analysis.")

        # Example: Check for MinMax anomalies (simplified)
        minmax_anomalies = [desc for desc in forensic_output_descriptions if "MinMax processing" in desc]
        if len(minmax_anomalies) > 1 and "Deviations in local pixel values" in minmax_anomalies[0]:
            # Placeholder for actual analysis of minmax output, e.g., deviation variance
            anomalies_detected.append("MinMax processing reveals subtle pixel deviations.")
            logger.warning("Detected subtle pixel deviations from MinMax processing.")
            
        if "Bit Plane extractor" in str(forensic_output_descriptions):
            anomalies_detected.append("Bit Plane extraction performed.")
            logger.info("Bit Plane extraction performed.")

        if anomalies_detected:
            summary_message = "Potential anomalies detected: " + "; ".join(anomalies_detected)
            logger.warning(f"Forensic anomaly detection summary: {summary_message}")
        else:
            logger.info(f"Forensic anomaly detection summary: {summary_message}")

        return {"anomalies": anomalies_detected, "summary": summary_message}