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Template for creating new detector models.
Copy this file and implement the detect() method with your custom ML logic.
Then register it in app/services/detector/__init__.py
Example:
# Copy this file as app/services/detector/mydetector.py
# Modify the class and model_name
# Add to get_detector() in __init__.py
"""
import logging
import time
from typing import Dict, Any
from app.services.detector.base import BaseDetector
logger = logging.getLogger(__name__)
class MyDetector(BaseDetector):
"""
Template detector implementation.
Replace 'MyDetector' with your detector name.
"""
def __init__(self):
"""Initialize the detector."""
# Change 'mydetector' to your model name
super().__init__("mydetector")
async def detect(self, file_bytes: bytes) -> Dict[str, Any]:
"""
Detect if file is a deepfake.
Args:
file_bytes: The file contents as bytes
Returns:
Dictionary with:
- is_deepfake: Boolean
- confidence: Float between 0.0 and 1.0
- analysis_time: Float in seconds
"""
logger.info(f"Starting detection with {self.model_name}...")
start_time = time.time()
# ========================================
# TODO: Implement your ML model logic here
# ========================================
# Example:
# 1. Preprocess file_bytes if needed
# 2. Load your ML model
# 3. Run inference
# 4. Post-process results
# For now, return placeholder results
is_deepfake = True
confidence = 0.85
analysis_time = time.time() - start_time
result = {
"is_deepfake": is_deepfake,
"confidence": round(confidence, 3),
"analysis_time": round(analysis_time, 3),
}
logger.info(f"Detection completed. Result: {result}")
return result
# =====================================================
# REGISTRATION INSTRUCTIONS:
# =====================================================
#
# 1. Save this file as: app/services/detector/mydetector.py
#
# 2. Update app/services/detector/__init__.py:
#
# from app.services.detector.mydetector import MyDetector
#
# def get_detector(model_name: str = "mock") -> BaseDetector:
# detectors = {
# "mock": MockDetector,
# "mydetector": MyDetector, # ADD THIS LINE
# }
# # ... rest of function
#
# 3. Update .env.example:
#
# DEFAULT_DETECTOR_MODEL=mydetector
#
# 4. Test your detector:
#
# POST /analyze
# {
# "file_url": "https://example.com/video.mp4",
# "model": "mydetector"
# }
#
# =====================================================
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