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# models/trust_score.py
from .model_loader import load_model
from .logging_config import logger
def generate_trust_score(text, image_analysis, pdf_analysis):
try:
classifier = load_model("zero-shot-classification", "typeform/mobilebert-uncased-mnli")
aspects = [
"complete information provided",
"verified location",
"consistent data",
"authentic documents",
"authentic images",
"reasonable pricing",
"verified ownership",
"proper documentation"
]
result = classifier(text[:1000], aspects, multi_label=True)
# Much stricter weights with higher emphasis on critical aspects
weights = {
"complete information provided": 0.25,
"verified location": 0.20,
"consistent data": 0.15,
"authentic documents": 0.15,
"authentic images": 0.10,
"reasonable pricing": 0.05,
"verified ownership": 0.05,
"proper documentation": 0.05
}
score = 0
reasoning_parts = []
# Much stricter scoring for each aspect
for label, confidence in zip(result['labels'], result['scores']):
adjusted_confidence = confidence
# Stricter document verification
if label == "authentic documents":
if not pdf_analysis or len(pdf_analysis) == 0:
adjusted_confidence = 0.0
else:
doc_scores = [p.get('verification_score', 0) for p in pdf_analysis]
adjusted_confidence = sum(doc_scores) / max(1, len(doc_scores))
# Heavily penalize if any document has low verification score
if any(score < 0.7 for score in doc_scores):
adjusted_confidence *= 0.4
# Additional penalty for missing documents
if len(doc_scores) < 2:
adjusted_confidence *= 0.5
# Stricter image verification
elif label == "authentic images":
if not image_analysis or len(image_analysis) == 0:
adjusted_confidence = 0.0
else:
img_scores = [i.get('authenticity_score', 0) for i in image_analysis]
adjusted_confidence = sum(img_scores) / max(1, len(img_scores))
# Heavily penalize if any image has low authenticity score
if any(score < 0.8 for score in img_scores):
adjusted_confidence *= 0.4
# Additional penalty for AI-generated images
if any(i.get('is_ai_generated', False) for i in image_analysis):
adjusted_confidence *= 0.5
# Additional penalty for non-property related images
if any(not i.get('is_property_related', False) for i in image_analysis):
adjusted_confidence *= 0.6
# Stricter consistency check
elif label == "consistent data":
# Check for inconsistencies in the data
if "inconsistent" in text.lower() or "suspicious" in text.lower():
adjusted_confidence *= 0.3
# Check for impossible values
if "impossible" in text.lower() or "invalid" in text.lower():
adjusted_confidence *= 0.2
# Check for missing critical information
if "missing" in text.lower() or "not provided" in text.lower():
adjusted_confidence *= 0.4
# Stricter completeness check
elif label == "complete information provided":
# Check for missing critical information
if len(text) < 300 or "not provided" in text.lower() or "missing" in text.lower():
adjusted_confidence *= 0.4
# Check for vague or generic descriptions
if "generic" in text.lower() or "vague" in text.lower():
adjusted_confidence *= 0.5
# Check for suspiciously short descriptions
if len(text) < 150:
adjusted_confidence *= 0.3
score += adjusted_confidence * weights.get(label, 0.1)
reasoning_parts.append(f"{label} ({adjusted_confidence:.0%})")
# Apply additional penalties for suspicious patterns
if "suspicious" in text.lower() or "fraudulent" in text.lower():
score *= 0.5
# Apply penalties for suspiciously low values
if "suspiciously low" in text.lower() or "unusually small" in text.lower():
score *= 0.6
# Apply penalties for inconsistencies
if "inconsistent" in text.lower() or "mismatch" in text.lower():
score *= 0.6
# Apply penalties for missing critical information
if "missing critical" in text.lower() or "incomplete" in text.lower():
score *= 0.7
# Ensure score is between 0 and 100
score = min(100, max(0, int(score * 100)))
reasoning = f"Based on: {', '.join(reasoning_parts)}"
return score, reasoning
except Exception as e:
logger.error(f"Error generating trust score: {str(e)}")
return 20, "Could not assess trust."
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