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"""
AI detection using HuggingFace fakespot-ai/roberta-base-ai-text-detection-v1
Fast, accurate ML model. Returns AI similarity score (0-1).
"""
import logging
import os
from huggingface_hub import InferenceClient
from app.config import HF_TOKEN
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("ai_detector")
# Initialize HF client
try:
hf_client = InferenceClient(api_key=HF_TOKEN)
logger.info("✓ HuggingFace client initialized")
except Exception as e:
logger.error(f"❌ HF_TOKEN not available: {e}")
hf_client = None
def detect_ai_similarity(text: str) -> float:
"""
Detect AI-generated text using HuggingFace model.
Fast, accurate ML-based detection.
Returns:
float: 0.0-1.0 where 1.0 = definitely AI, 0.0 = definitely human
"""
try:
logger.info("Starting AI detection (HuggingFace model)...")
if not text or len(text) < 20:
logger.warning("Text too short for detection")
return 0.0
if hf_client is None:
logger.error("HuggingFace client not initialized")
return 0.0
# Truncate to avoid token limits
text = text[:2000]
logger.info("Sending request to HuggingFace...")
result = hf_client.text_classification(
text,
model="fakespot-ai/roberta-base-ai-text-detection-v1",
)
logger.info(f"API response: {result}")
# Extract top result
if not result or len(result) == 0:
logger.error("No result from API")
return 0.0
top_result = result[0]
label = top_result.get('label', '').upper()
confidence = top_result.get('score', 0.5)
logger.info(f"Classification: {label} (confidence: {confidence:.4f})")
# Map to 0-1 score
if label == 'AI':
ai_score = confidence
elif label == 'HUMAN':
ai_score = 1.0 - confidence
else:
ai_score = 0.5
ai_score = min(max(ai_score, 0.0), 1.0) # Clamp to 0-1
logger.info(f"✅ AI detection complete: {ai_score:.3f}")
return round(ai_score, 3)
except Exception as e:
logger.error(f"❌ AI detection error: {str(e)}", exc_info=True)
return 0.0
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