feat(avm): end-to-end AVM v2 schema + POST /analytics/valuation endpoint
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Closes the last gap from the tec-2725 branch: the valuation form's v2
extended-features section and POST endpoint can now submit real
predictions through to the Python ensemble model.
Backend
- New DTO apps/api/src/modules/analytics/presentation/dto/predict-valuation.dto.ts
with all v1 fields + 8 v2 fields (useV2 toggle, distanceToHospital/Park/
Mall in km, floodZoneRisk enum NONE|LOW|MEDIUM|HIGH, hasElevator/
Parking/Pool booleans).
- New CQRS handler apps/api/src/modules/analytics/application/queries/
predict-valuation/ that routes to AVM_SERVICE.estimateValue() with the
full request body.
- Extend AVMParams (domain) with the same v2 fields + inline v1 fields
(district, city, bedrooms, bathrooms, floors, frontage, roadWidth,
hasLegalPaper, projectId, imageUrl, description, deepAnalysis).
- HttpAVMService.estimateViaAi now branches on `useV2`: v2 calls the new
aiClient.predictV2() → POST /avm/v2/predict on the Python service,
mapping floodZoneRisk enum → 0..1 float and computing
building_age_years from yearBuilt. v1 path gets all the inline
descriptors wired through so non-propertyId calls no longer lose
context.
- AiServiceClient gets AiPredictV2Request / AiPredictV2Response types
mirroring libs/ai-services/app/models/avm_v2.py::AVMv2PredictRequest
(which already accepts all 7 numeric/boolean v2 fields — no Python
change needed).
- Register PredictValuationHandler in AnalyticsModule.
- New route POST /analytics/valuation on AnalyticsController:
JwtAuthGuard + QuotaGuard + EndpointRateLimitGuard (10/min),
@RequireQuota('analytics_queries'), full Swagger doc. Total endpoint
count 179 → 180.
Frontend
- Extend ValuationRequest with useV2, 3 distance-km fields,
floodZoneRisk, hasElevator/Parking/Pool + export FloodZoneRisk type
and FLOOD_RISK_OPTIONS.
- valuationApi.predict() body mapping now includes v2 fields and renames
'areaM2' → 'area' to match the backend DTO contract.
- valuationFormSchema gains matching optional Zod fields + exports
FLOOD_RISK_OPTIONS for the form.
- valuation-form.tsx gets:
* Image upload hardening: MIME+size validation (JPG/PNG ≤5MB) before
preview, role="progressbar" + aria-labels on the progress bar,
role="alert" + data-testid="image-upload-error" on errors. Matches
the upload-progress part of the task/tec-2725 commit 4ee0129 that
was previously parked as blocked.
* New Sparkles-branded "Mô hình v2 (Ensemble)" toggle alongside the
existing Bot-branded "Phân tích chuyên sâu" toggle.
* Collapsible "Đặc trưng mở rộng (AVM v2)" section with distance
inputs, flood-risk select, and three amenity checkboxes.
* handleFormSubmit passes all v2 fields through to onSubmit.
Python service unchanged — AVMv2PredictRequest already has every field
we send (distance_to_hospital_km, flood_zone_risk as float,
has_elevator/parking/pool, etc.).
Typecheck clean for the valuation surface. Pre-existing errors in
metadata.spec.ts and transfer-wizard-client.tsx are unrelated and left
for a follow-up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -23,6 +23,76 @@ export interface AiPredictResponse {
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price_range_high: number;
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}
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/**
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* AVM v2 request — extended feature set for residential ensemble.
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* Matches `AVMv2PredictRequest` in libs/ai-services/app/models/avm_v2.py.
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*/
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export interface AiPredictV2Request {
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district: string;
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city: string;
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property_type: string;
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area_m2: number;
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distance_to_cbd_km?: number;
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distance_to_metro_km?: number;
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distance_to_school_km?: number;
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distance_to_hospital_km?: number;
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distance_to_park_km?: number;
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distance_to_mall_km?: number;
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flood_zone_risk?: number;
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neighborhood_score?: number;
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rooms?: number;
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floor_level?: number;
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total_floors?: number;
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direction?: string;
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floor_ratio?: number;
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building_age_years?: number;
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has_elevator?: boolean;
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has_parking?: boolean;
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has_pool?: boolean;
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has_legal_paper?: boolean;
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developer_reputation?: number;
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avg_price_district_3m_vnd_m2?: number;
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listing_density?: number;
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absorption_rate?: number;
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dom_avg?: number;
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price_momentum_30d?: number;
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yoy_change?: number;
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renovation_score?: number;
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view_quality?: number;
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interior_quality?: number;
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noise_level?: number;
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natural_light?: number;
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month?: number;
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quarter?: number;
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is_year_end?: boolean;
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}
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export interface AiPredictV2FeatureImportance {
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feature: string;
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importance: number;
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}
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export interface AiPredictV2Comparable {
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district: string;
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property_type: string;
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area_m2: number;
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price_vnd: number;
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price_per_m2_vnd: number;
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similarity_score: number;
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}
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export interface AiPredictV2Response {
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estimated_price_vnd: number;
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confidence: number;
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price_per_m2_vnd: number;
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price_range_low_vnd: number;
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price_range_high_vnd: number;
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drivers?: AiPredictV2FeatureImportance[];
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comparables?: AiPredictV2Comparable[];
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model_version?: string;
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ensemble_method?: string;
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}
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export interface AiIndustrialPredictRequest {
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province: string;
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region: string;
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@@ -124,6 +194,7 @@ export const AI_SERVICE_CLIENT = Symbol('AI_SERVICE_CLIENT');
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export interface IAiServiceClient {
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predict(req: AiPredictRequest): Promise<AiPredictResponse>;
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predictV2(req: AiPredictV2Request): Promise<AiPredictV2Response>;
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predictIndustrial(req: AiIndustrialPredictRequest): Promise<AiIndustrialPredictResponse>;
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moderate(req: AiModerationRequest): Promise<AiModerationResponse>;
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scoreNeighborhood(req: AiNeighborhoodScoreRequest): Promise<AiNeighborhoodScoreResponse>;
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@@ -146,6 +217,10 @@ export class AiServiceClient implements IAiServiceClient {
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return this.post<AiPredictResponse>('/avm/predict', req);
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}
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async predictV2(req: AiPredictV2Request): Promise<AiPredictV2Response> {
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return this.post<AiPredictV2Response>('/avm/v2/predict', req);
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}
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async predictIndustrial(req: AiIndustrialPredictRequest): Promise<AiIndustrialPredictResponse> {
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return this.post<AiIndustrialPredictResponse>('/avm/industrial/predict', req);
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}
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@@ -12,7 +12,16 @@ import {
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AI_SERVICE_CLIENT,
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type IAiServiceClient,
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type AiPredictRequest,
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type AiPredictV2Request,
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} from './ai-service.client';
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/** Map string risk buckets to the 0..1 float the Python service expects. */
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const FLOOD_RISK_TO_SCORE: Record<string, number> = {
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NONE: 0,
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LOW: 0.33,
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MEDIUM: 0.66,
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HIGH: 1,
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};
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import { PrismaAVMService } from './prisma-avm.service';
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/** Max concurrency for batch AI calls to avoid overloading the Python service. */
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@@ -83,18 +92,22 @@ export class HttpAVMService implements IAVMService {
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? await this.getPropertyDetails(params.propertyId)
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: null;
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if (params.useV2) {
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return this.estimateViaAiV2(params, propertyData);
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}
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const request: AiPredictRequest = {
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area: params.areaM2 ?? propertyData?.areaM2 ?? 0,
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district: propertyData?.district ?? '',
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city: propertyData?.city ?? '',
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district: params.district ?? propertyData?.district ?? '',
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city: params.city ?? propertyData?.city ?? '',
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property_type: (params.propertyType ?? propertyData?.propertyType ?? 'house').toLowerCase(),
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bedrooms: propertyData?.bedrooms ?? 0,
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bathrooms: propertyData?.bathrooms ?? 0,
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floors: propertyData?.floors ?? 0,
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frontage: 0,
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road_width: 0,
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bedrooms: params.bedrooms ?? propertyData?.bedrooms ?? 0,
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bathrooms: params.bathrooms ?? propertyData?.bathrooms ?? 0,
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floors: params.floors ?? propertyData?.floors ?? 0,
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frontage: params.frontage ?? 0,
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road_width: params.roadWidth ?? 0,
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year_built: params.yearBuilt ?? propertyData?.yearBuilt,
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has_legal_paper: propertyData?.hasLegalPaper ?? true,
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has_legal_paper: params.hasLegalPaper ?? propertyData?.hasLegalPaper ?? true,
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};
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const aiResult = await this.aiClient.predict(request);
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@@ -118,6 +131,55 @@ export class HttpAVMService implements IAVMService {
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};
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}
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private async estimateViaAiV2(
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params: AVMParams,
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propertyData: Awaited<ReturnType<HttpAVMService['getPropertyDetails']>>,
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): Promise<ValuationResult> {
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const yearBuilt = params.yearBuilt ?? propertyData?.yearBuilt ?? null;
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const now = new Date();
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const v2Request: AiPredictV2Request = {
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district: params.district ?? propertyData?.district ?? '',
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city: params.city ?? propertyData?.city ?? '',
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property_type: (params.propertyType ?? propertyData?.propertyType ?? 'house').toLowerCase(),
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area_m2: params.areaM2 ?? propertyData?.areaM2 ?? 0,
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distance_to_hospital_km: params.distanceToHospitalKm,
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distance_to_park_km: params.distanceToParkKm,
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distance_to_mall_km: params.distanceToMallKm,
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flood_zone_risk:
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params.floodZoneRisk != null ? FLOOD_RISK_TO_SCORE[params.floodZoneRisk] ?? 0 : undefined,
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rooms: params.bedrooms ?? propertyData?.bedrooms,
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total_floors: params.floors ?? propertyData?.floors,
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building_age_years: yearBuilt != null ? Math.max(0, now.getFullYear() - yearBuilt) : undefined,
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has_elevator: params.hasElevator,
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has_parking: params.hasParking,
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has_pool: params.hasPool,
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has_legal_paper: params.hasLegalPaper ?? propertyData?.hasLegalPaper ?? true,
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month: now.getMonth() + 1,
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quarter: Math.floor(now.getMonth() / 3) + 1,
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is_year_end: now.getMonth() >= 9,
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};
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const aiResult = await this.aiClient.predictV2(v2Request);
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let comparables: Comparable[] = [];
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try {
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if (params.propertyId) {
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comparables = await this.fallback.getComparables(params.propertyId, 2000);
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}
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} catch {
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// Supplementary — don't fail
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}
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return {
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estimatedPrice: Math.round(aiResult.estimated_price_vnd).toString(),
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confidence: aiResult.confidence,
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pricePerM2: Math.round(aiResult.price_per_m2_vnd),
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comparables,
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modelVersion: aiResult.model_version ?? 'ai-service-v2',
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};
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}
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private async getPropertyDetails(propertyId: string) {
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const row = await this.prisma.property.findUnique({
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where: { id: propertyId },
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