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>
49 lines
2.0 KiB
Python
49 lines
2.0 KiB
Python
from pydantic import BaseModel, Field
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class AVMPredictRequest(BaseModel):
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area: float = Field(..., gt=0, description="Property area in m²")
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district: str = Field(..., min_length=1, description="District name")
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city: str = Field(..., min_length=1, description="City name")
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property_type: str = Field(..., description="e.g. apartment, house, land")
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bedrooms: int = Field(0, ge=0)
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bathrooms: int = Field(0, ge=0)
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floors: int = Field(0, ge=0)
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frontage: float = Field(0.0, ge=0, description="Frontage width in meters")
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road_width: float = Field(0.0, ge=0, description="Adjacent road width in meters")
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year_built: int | None = Field(None, description="Year the property was built")
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has_legal_paper: bool = Field(True, description="Whether property has sổ đỏ/sổ hồng")
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class AVMPredictResponse(BaseModel):
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estimated_price_vnd: float = Field(..., description="Estimated price in VND")
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confidence: float = Field(..., ge=0, le=1, description="Prediction confidence score")
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price_per_m2: float = Field(..., description="Price per m² in VND")
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price_range_low: float = Field(..., description="Lower bound estimate in VND")
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price_range_high: float = Field(..., description="Upper bound estimate in VND")
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class FeatureExtractRequest(BaseModel):
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text: str = Field(..., min_length=1, description="Vietnamese property listing text")
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class ExtractedFeatures(BaseModel):
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area: float | None = None
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district: str | None = None
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city: str | None = None
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property_type: str | None = None
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bedrooms: int | None = None
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bathrooms: int | None = None
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floors: int | None = None
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frontage: float | None = None
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road_width: float | None = None
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price_mentioned: float | None = None
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has_legal_paper: bool | None = None
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address_raw: str | None = None
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class FeatureExtractResponse(BaseModel):
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features: ExtractedFeatures
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tokens: list[str] = Field(default_factory=list, description="Tokenized words")
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entities: list[dict] = Field(default_factory=list, description="Named entities found")
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