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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 |