b392bc3570
Create libs/ai-services/ with FastAPI app providing: - POST /avm/predict — XGBoost-backed property price prediction (heuristic fallback) - POST /avm/extract-features — Vietnamese NLP feature extraction from listing text - POST /moderation/check — content moderation with rule-based flagging - GET /health — health check endpoint Includes Dockerfile (Python 3.12), docker-compose integration, Pydantic models, and 9 passing tests covering all endpoints. Co-Authored-By: Paperclip <noreply@paperclip.ing>
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
import re
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from app.models.moderation import ModerationFlag, ModerationRequest, ModerationResponse
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# Blocklist categories with patterns and severity
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_RULES: list[dict] = [
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{
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"category": "contact_info",
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"severity": "medium",
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"patterns": [
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re.compile(r"0\d{9,10}"), # Vietnamese phone numbers
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re.compile(r"\b[\w.+-]+@[\w-]+\.[\w.]+\b"), # Email
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re.compile(r"(?:zalo|viber|telegram|whatsapp)\s*[:\-]?\s*\d+", re.IGNORECASE),
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],
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"reason": "Contact information detected — may bypass platform messaging",
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},
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{
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"category": "spam",
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"severity": "low",
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"patterns": [
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re.compile(r"(.)\1{5,}"), # Repeated characters
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re.compile(r"(!!!|\.\.\.){3,}"), # Excessive punctuation
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re.compile(r"(?:click|nhấn|bấm)\s+(?:here|vào đây|link)", re.IGNORECASE),
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],
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"reason": "Spam-like content pattern",
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},
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{
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"category": "profanity",
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"severity": "high",
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"patterns": [
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re.compile(
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r"\b(?:lừa đảo|scam|fake|giả mạo)\b",
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re.IGNORECASE,
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),
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],
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"reason": "Potentially harmful or fraudulent language",
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},
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{
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"category": "prohibited_content",
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"severity": "high",
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"patterns": [
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re.compile(
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r"\b(?:đất rừng phòng hộ|đất quốc phòng|đất tranh chấp)\b",
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re.IGNORECASE,
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),
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],
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"reason": "Listing references prohibited property types",
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},
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]
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class ModerationService:
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def check(self, req: ModerationRequest) -> ModerationResponse:
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flags: list[ModerationFlag] = []
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text = req.text
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for rule in _RULES:
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for pattern in rule["patterns"]:
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for match in pattern.finditer(text):
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flags.append(
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ModerationFlag(
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category=rule["category"],
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severity=rule["severity"],
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matched_text=match.group(),
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reason=rule["reason"],
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)
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)
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if not flags:
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return ModerationResponse(
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is_flagged=False,
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score=0.0,
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flags=[],
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cleaned_text=text,
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)
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# Compute aggregate score
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severity_weights = {"low": 0.2, "medium": 0.5, "high": 0.9}
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max_score = max(severity_weights.get(f.severity, 0.5) for f in flags)
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avg_score = sum(severity_weights.get(f.severity, 0.5) for f in flags) / len(flags)
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score = round(min(1.0, max_score * 0.7 + avg_score * 0.3), 3)
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# Redact flagged content
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cleaned = text
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for flag in flags:
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cleaned = cleaned.replace(flag.matched_text, "[REDACTED]")
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return ModerationResponse(
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is_flagged=True,
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score=score,
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flags=flags,
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cleaned_text=cleaned,
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)
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moderation_service = ModerationService()
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