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6c9d916 8e30b6a 6c9d916 8e30b6a 6c9d916 8e30b6a 6c9d916 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | import io
import logging
import time
from typing import Dict, Any
from PIL import Image
from transformers import pipeline
logger = logging.getLogger(__name__)
_image_classifier = None
def _load_model():
global _image_classifier
if _image_classifier is None:
logger.info("Loading capcheck/ai-image-detection model...")
_image_classifier = pipeline(
"image-classification",
model="capcheck/ai-image-detection",
device=-1
)
logger.info("Image detector model loaded successfully")
return _image_classifier
async def analyze_image(image_bytes: bytes) -> Dict[str, Any]:
start_time = time.time()
logger.info(f"Starting image analysis, size: {len(image_bytes)} bytes")
try:
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
except Exception as e:
logger.error(f"Failed to parse image bytes: {str(e)}")
raise ValueError("Invalid image format or corrupted bytes") from e
classifier = _load_model()
result = classifier(image)
label = result[0]["label"]
score = result[0]["score"]
is_deepfake = label.lower() == "fake"
confidence = score
analysis_time = time.time() - start_time
response = {
"is_deepfake": is_deepfake,
"confidence": round(confidence, 3),
"analysis_time": round(analysis_time, 3),
}
logger.info(f"Image analysis completed. Result: {response}")
return response |