LPX
refactor: reorganize agent structure by moving models to agents directory, update logging level, and enhance .gitignore for model files
c1d03da
| 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 |