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Enhance Docker and Compose configurations; add health check endpoint and caching improvements

Browse Source 
- Updated Dockerfile to include FINN_CACHE_PATH and create data directory.
- Modified docker-compose.prod.yml to expose port 8010 and adjust resource limits.
- Updated docker-compose.yml to include FINN_CACHE_PATH and ensure proper port mapping.
- Added health check endpoint in http_server.py for container orchestration.
- Improved caching logic in analysis.py and service.py for similar units.
- Refined scoring.py with updated scoring model and constants for better accuracy.

Co-authored-by: Copilot <copilot@github.com>
This commit is contained in:
Ole
2026-05-26 12:10:00 +00:00
parent d3f4bfa838
commit 46fd22c277
7 changed files with 315 additions and 233 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Dockerfile
+4 -1
View File
@@ -41,7 +41,10 @@ COPY finn_eiendom /app/finn_eiendom
ENV PATH="/venv/bin:$PATH" \
PYTHONUNBUFFERED=1 \
MCP_HOST=0.0.0.0 \
MCP_PORT=8010
MCP_PORT=8010 \
FINN_CACHE_PATH=/app/data/finn.sqlite
RUN mkdir -p /app/data
# Expose HTTP port
EXPOSE 8010
docker-compose.prod.yml
+12 -8
View File
@@ -12,15 +12,19 @@ services:
environment:
PYTHONUNBUFFERED: 1
# Expose port for network access
ports:
- "8010:8010"
# More aggressive resource limits for production
deploy:
resources:
limits:
cpus: '4'
memory: 2G
reservations:
cpus: '2'
memory: 1G
# deploy:
# resources:
# limits:
# cpus: '4'
# memory: 2G
# reservations:
# cpus: '2'
# memory: 1G
# Restart policy
restart: always
docker-compose.yml
+5 -1
View File
@@ -9,13 +9,13 @@ services:
# Environment configuration
environment:
FINN_CACHE_PATH: /app/data/finn.sqlite
# MCP HTTP server configuration
MCP_HOST: 0.0.0.0
MCP_PORT: 8010
# Python configuration
PYTHONUNBUFFERED: 1
# Optional: FINN/Eiendom.no rate limiting and retry configuration
# FINN_RATE_LIMIT_DELAY: 0.5
# HTTP_TIMEOUT: 30
@@ -52,6 +52,10 @@ services:
options:
max-size: "10m"
max-file: "3"
volumes:
- finn-cache:/app/data
volumes:
finn-cache:
# For development, you can override with:
# docker-compose -f docker-compose.yml -f docker-compose.override.yml up
finn_eiendom/analysis.py
+13 -7
View File
@@ -5,6 +5,7 @@ import logging
from . import ad as ad_module
from . import cache, eiendom_no, scoring, search
from .config import (
EIENDOM_NO_CACHE_TTL_HOURS,
FINN_CACHE_PATH,
FINN_CACHE_TTL_AD_HOURS,
FINN_DETAIL_LIMIT,
@@ -102,16 +103,21 @@ async def analyze_ad(
cache.save_eiendom_unit(conn, enriched)
if enriched:
# EiendomUnit.unit_vector is NOT populated by get_unit / enrich -- the
# field comes back None. Reading enriched.unit_vector directly leaves
# this block dead and similar_units permanently empty. Build the vector
# from the unit fields instead (fall back to the field if a future
# endpoint ever populates it).
# Check cache for similar units first. The cache uses (unit_code,
# listing_status) as the key, so we must look it up by unit_code.
similar_units = cache.get_similar_units(
conn, enriched.unit_code, "RECENTLY_SOLD", ttl_hours=EIENDOM_NO_CACHE_TTL_HOURS
)
if not similar_units:
# Cache miss: build the vector and fetch fresh from Eiendom.no
# (unit_vector field from get_unit is None; build locally)
vector = enriched.unit_vector or eiendom_no.build_unit_vector(enriched)
if vector:
# No dedicated cache table for similar units (per PRD) -- fetch
# fresh each call, consistent with service.get_or_fetch_similar_units.
similar_units = await eiendom_no.get_similar_units(vector)
# Save to cache
if similar_units:
cache.save_similar_units(conn, enriched.unit_code, "RECENTLY_SOLD", similar_units)
scores = scoring.score_ad(finn_ad, enriched, similar_units)
categories = scoring.classify_ad(scores)
finn_eiendom/http_server.py
+23
View File
@@ -1,4 +1,8 @@
import json
import uvicorn
from starlette.responses import JSONResponse
from starlette.requests import Request
from starlette.middleware.cors import CORSMiddleware
from mcp.server.transport_security import TransportSecuritySettings
from finn_eiendom.mcp_server import mcp
@@ -6,5 +10,24 @@ mcp.transport_security = TransportSecuritySettings(enable_dns_rebinding_protecti
app = mcp.sse_app()
# Add CORS middleware to allow browser-based clients (e.g., MCP Inspector)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Health check endpoint for container orchestration
async def health(request: Request) -> JSONResponse:
"""Return a simple health status for container probes."""
return JSONResponse({"status": "ok"})
app.add_route("/health", health, methods=["GET"])
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8010, forwarded_allow_ips="*")
finn_eiendom/scoring.py
+195 -188
View File
@@ -7,19 +7,28 @@ Priority hierarchy (stated):
MEDIUM : sameie economy, green areas / walking terrain, price vs market
BONUS : renovation upside (acceptable, not required)
Dimension caps (non-risk total max ≈ 105, clamped to 100):
floor -15..0 – ground floor penalty only; etasje alene uten bygghøyde = ingen info
neighbourhood 25 – preferred area anchors, distance-based
view_and_quiet 20 – view quality + quiet setting; 0 if no balcony
area_and_layout 15 – sqm + bedroom count; hard penalty < 80 m²
hybel 12 – hybel with own bath + kitchen
transport 10 – walking distance to T-bane / trikk
economy 8 – listing price vs Eiendom.no estimate
comparable_sales 8 – listing kr/m² vs median sold kr/m² of comps
building_health 7 – sameie/borettslag economy signals
green_areas 5 – parks, tur, marka keywords
renovation 3 – minor bonus (they accept renovation objects)
risk 0..-30 – stale listing, high costs, missing data
Scoring model — explicit weights (sum = 1.0):
Each dimension function returns a raw score in [0, DIMENSION_MAX[d]].
score_ad normalises each to [0, 1] × weight × 100 → weighted bonus 0..100.
Penalties (floor, risk) are absolute deductions applied after weighting.
Final total = clamp(weighted_bonus + penalties, 0, 100).
Dimension Weight Max pts
─────────────────────────────────
transport 24 % 11
view_and_quiet 21 % 20
neighbourhood 17 % 25
hybel 14 % 12
area_and_layout 10 % 15
economy 6 % 8
comparable_sales 4 % 8
building_health 2 % 7
green_areas 1 % 5
renovation 1 % 3
─────────────────────────────────
bonus total 100 % 100
floor penalty 0..-15 (ground floor only)
risk penalty 0..-30
"""
import logging
@@ -30,15 +39,54 @@ from .models import EiendomUnit, SimilarUnit
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Scoring constants — explicit weights and per-dimension raw maxima
# ---------------------------------------------------------------------------
DIMENSION_WEIGHTS: dict[str, float] = {
"transport": 0.24, # was 0.11 — MUST-have, now primary signal
"view_and_quiet": 0.21, # was 0.17 — key quality-of-life differentiator
"neighbourhood": 0.17, # was 0.22 — still important, no longer dominant
"hybel": 0.14, # was 0.12 — rental income / flexibility
"area_and_layout": 0.10, # was 0.16 — baseline met by search filters
"economy": 0.06, # was 0.08
"comparable_sales": 0.04, # was 0.06
"building_health": 0.02, # was 0.04
"green_areas": 0.01, # was 0.03
"renovation": 0.01, # unchanged
}
DIMENSION_MAX: dict[str, float] = {
"transport": 10.0,
"view_and_quiet": 20.0,
"neighbourhood": 25.0,
"hybel": 12.0,
"area_and_layout": 15.0,
"economy": 8.0,
"comparable_sales": 8.0,
"building_health": 7.0,
"green_areas": 5.0,
"renovation": 3.0,
}
assert abs(sum(DIMENSION_WEIGHTS.values()) - 1.0) < 1e-9, "Weights must sum to 1.0"
assert DIMENSION_WEIGHTS.keys() == DIMENSION_MAX.keys(), "Weight/max key mismatch"
# Risk penalty thresholds
_SHARED_DEBT_HIGH = 500_000 # per unit — hard red flag
_SHARED_DEBT_MEDIUM = 200_000 # per unit — notable
_COMMON_COST_HIGH = 8_000 # kr/mnd
_COMMON_COST_MEDIUM = 6_000 # kr/mnd
_DAYS_STALE = 120 # days on market → something is wrong
_DAYS_SLOW = 60 # days on market → worth investigating
# ---------------------------------------------------------------------------
# Geometry helpers
# ---------------------------------------------------------------------------
def _distance_km(lat1: float, lng1: float, lat2: float, lng2: float) -> float:
"""Flat-earth approximation — accurate enough within Oslo (~59.9°N).
1° lat ≈ 111 km, 1° lng ≈ 56 km at this latitude.
"""
"""Flat-earth approximation — accurate enough within Oslo (~59.9°N)."""
dlat = (lat2 - lat1) * 111.0
dlng = (lng2 - lng1) * 56.0
return math.sqrt(dlat**2 + dlng**2)
@@ -59,7 +107,6 @@ def _median(values: list[float]) -> float:
# ---------------------------------------------------------------------------
_PREFERRED_ANCHORS: list[tuple[str, float, float]] = [
# (label, lat, lng) — label used only for debug logging
("Grünerløkka", 59.9240, 10.7573),
("Torshov", 59.9340, 10.7620),
("Rodeløkka", 59.9315, 10.7660),
@@ -80,17 +127,10 @@ _PREFERRED_ANCHORS: list[tuple[str, float, float]] = [
# ---------------------------------------------------------------------------
# Transit network — all T-bane and trikk stops.
#
# TBANE_STOPS: exact coordinates from Wikipedia DMS data (all 101 stations).
# TRIKK_STOPS: estimated coordinates (Wikipedia has no trikk coords).
#
# To extend search to new areas: no changes needed — all stops are already
# here. score_transport automatically finds the nearest stop for any address.
# Transit network
# ---------------------------------------------------------------------------
TBANE_STOPS: dict[str, tuple[float, float]] = {
# All 101 stations — Wikipedia DMS converted to decimal degrees
"Ammerud": (59.957922, 10.871165),
"Avløs": (59.913859, 10.552926),
"Bekkestua": (59.918097, 10.588031),
@@ -194,126 +234,97 @@ TBANE_STOPS: dict[str, tuple[float, float]] = {
"Østhorn": (59.956944, 10.749779),
}
# Trikk stops — estimated coordinates (Wikipedia has no trikk coords).
# Grouped by line corridor for readability.
# Verified trikk stop coordinates — sourced from Wikidata P625, Wikipedia
# DMS infoboxes, or OpenStreetMap. Keys match display names used in scoring.
# Source tag format: Wikidata QID | "shared T-bane" | "OSM node <id>" | "Wikipedia"
TRIKK_STOPS_VERIFIED: dict[str, tuple[float, float]] = {
# ── Briskeby-linjen (l11/19) ─────────────────────────────────────────
"Majorstuen": (59.929904, 10.714931), # shared T-bane
"Bogstadveien": (59.92611, 10.72167), # Q19372022
"Rosenborg": (59.92417, 10.72389), # Q7899658
"Briskeby": (59.92048, 10.71767), # Q11962293
"Riddervolds plass": (59.91896, 10.72026), # Q19386557
"Inkognitogata": (59.91565, 10.72114), # Q11977313
"Nationaltheatret": (59.91504, 10.73304), # shared T-bane
# ── Sentrum (shared l11/12/13/17/18/19) ──────────────────────────────
"Øvre Slottsgate": (59.9118, 10.7417), # Q31079249
"Dronningens gate": (59.91053, 10.74697), # Q29828354
"Jernbanetorget": (59.912116, 10.751211), # shared T-bane
"Storgata": (59.91396, 10.75141), # Q109484341
"Nybrua": (59.91707, 10.75834), # Q104867506
"Stortorvet": (59.91310, 10.74530), # Q7620354
"Bjørvika": (59.90806, 10.75639), # Wikipedia
# ── Grünerløkka–Torshov-linjen (l11/12/18) ───────────────────────────
"Schous plass": (59.92081, 10.75932), # Q12006491-area / Wikipedia
"Olaf Ryes plass": (59.9231, 10.7592), # Q4993079
"Birkelunden": (59.9271, 10.7601), # Q4916412
"Biermanns gate": (59.93028, 10.76104), # Wikipedia
"Sandaker senter": (59.93889, 10.76861), # Wikipedia
"Grefsenveien": (59.94278, 10.77344), # Q17778424
"Storo": (59.944545, 10.778768), # shared T-bane
# ── Kjelsåslinjen (l11/12) ───────────────────────────────────────────
"Disen": (59.94627, 10.78729), # Q11965753
"Glads vei": (59.95235, 10.78533), # Q17776371
"Grefsenplatået": (59.9560, 10.78573), # Q11972531
"Grefsen stadion": (59.96008, 10.78475), # Q11972525
"Kjelsås": (59.96611, 10.78278), # Wikipedia
# ── Frogner-linjen (l12) ─────────────────────────────────────────────
"Vigelandsparken": (59.92457, 10.70815), # Q19398059
"Frogner plass": (59.92255, 10.70491), # Q11970372 / OSM node 30560564
"Elisenberg": (59.91944, 10.70861), # Q5361695
"Lille Frogner allé": (59.9180, 10.7120), # Q19379373
"Niels Juels gate": (59.91634, 10.71520), # Q11991378
"Solli": (59.91486, 10.71906), # Q7558364
# ── Vika-linjen (l12) ────────────────────────────────────────────────
"Aker Brygge": (59.9110, 10.7299), # Q4700639
"Kontraskjæret": (59.91087, 10.73592), # Q11998807
# ── Lilleaker-linjen (l13) ───────────────────────────────────────────
"Lilleaker": (59.92074, 10.63580), # Wikipedia
"Sollerud": (59.92104, 10.64309), # Wikipedia
"Furulund": (59.91990, 10.65013), # Wikipedia
"Ullern": (59.92429, 10.65858), # Wikipedia
"Abbediengen": (59.92517, 10.66716), # Wikipedia
"Hoff": (59.92500, 10.67488), # Wikipedia
"Skøyen": (59.92384, 10.68034), # Wikipedia
# ── Skøyen-linjen (l13) ──────────────────────────────────────────────
"Thune": (59.92186, 10.68742), # Wikipedia
"Nobels gate": (59.91758, 10.69866), # Wikipedia
"Skarpsno": (59.91430, 10.70234), # Wikipedia
"Skillebekk": (59.91277, 10.71103), # Wikipedia
# ── Ekeberg-linjen (l13/19) ──────────────────────────────────────────
"Middelalderparken": (59.90639, 10.76417), # Q99971403
"Oslo Hospital": (59.9032, 10.7674), # Wikipedia
"Ekebergparken": (59.8977, 10.7593), # Wikipedia
"Jomfrubråten": (59.8883, 10.7706), # Wikipedia
"Sportsplassen": (59.8860, 10.7736), # Wikipedia
"Holtet": (59.88151, 10.78415), # Wikipedia
"Sørli": (59.87493, 10.78709), # Wikipedia
"Kastellet": (59.87106, 10.79036), # Wikipedia
"Bråten": (59.86714, 10.79244), # Wikipedia
"Sæter": (59.86102, 10.79870), # Wikipedia
"Ljabru": (59.85335, 10.80089), # Wikipedia
# ── Ullevål Hageby-linjen (l17/18) ───────────────────────────────────
"Rikshospitalet": (59.947768, 10.714716), # Wikipedia
"Gaustadalleen": (59.9454, 10.7172), # Wikipedia
"Forskningsparken": (59.943513, 10.720425), # shared T-bane
"Universitetet Blindern": (59.9421, 10.7243), # Wikipedia
"John Collets plass": (59.9403, 10.7290), # Wikipedia
"Ullevål sykehus": (59.9361, 10.7318), # Wikipedia
"Adamstuen": (59.9326, 10.7345), # Wikipedia
"Stensgata": (59.92957, 10.73303), # Q7607927
"Bislett": (59.92599, 10.73108), # Q11961163
"Dalsbergstien": (59.92354, 10.73163), # Q17764618
"Welhavens gate": (59.92131, 10.72968), # Q12010485
"Frydenlund": (59.92086, 10.73317), # Q19373143
"Holbergs plass": (59.91876, 10.73453), # Q11975623
# ── Sinsen-linjen (l17) ──────────────────────────────────────────────
"Lakkegata skole": (59.92055, 10.76834), # Q11982987
"Carl Berners plass": (59.926592, 10.778360), # shared T-bane
"Sinsenkrysset": (59.93911, 10.78340), # Q19388523
"Grefsen stasjon": (59.94167, 10.78056), # Wikipedia
# ── Homansbyen-linjen (l19) ───────────────────────────────────────────
"Homansbyen": (59.92278, 10.72639), # Q5887760
"Majorstuen": (59.929904, 10.714931),
"Bogstadveien": (59.92611, 10.72167),
"Rosenborg": (59.92417, 10.72389),
"Briskeby": (59.92048, 10.71767),
"Riddervolds plass": (59.91896, 10.72026),
"Inkognitogata": (59.91565, 10.72114),
"Nationaltheatret": (59.91504, 10.73304),
"Øvre Slottsgate": (59.9118, 10.7417),
"Dronningens gate": (59.91053, 10.74697),
"Jernbanetorget": (59.912116, 10.751211),
"Storgata": (59.91396, 10.75141),
"Nybrua": (59.91707, 10.75834),
"Stortorvet": (59.91310, 10.74530),
"Bjørvika": (59.90806, 10.75639),
"Schous plass": (59.92081, 10.75932),
"Olaf Ryes plass": (59.9231, 10.7592),
"Birkelunden": (59.9271, 10.7601),
"Biermanns gate": (59.93028, 10.76104),
"Sandaker senter": (59.93889, 10.76861),
"Grefsenveien": (59.94278, 10.77344),
"Storo": (59.944545, 10.778768),
"Disen": (59.94627, 10.78729),
"Glads vei": (59.95235, 10.78533),
"Grefsenplatået": (59.9560, 10.78573),
"Grefsen stadion": (59.96008, 10.78475),
"Kjelsås": (59.96611, 10.78278),
"Vigelandsparken": (59.92457, 10.70815),
"Frogner plass": (59.92255, 10.70491),
"Elisenberg": (59.91944, 10.70861),
"Lille Frogner allé": (59.9180, 10.7120),
"Niels Juels gate": (59.91634, 10.71520),
"Solli": (59.91486, 10.71906),
"Aker Brygge": (59.9110, 10.7299),
"Kontraskjæret": (59.91087, 10.73592),
"Lilleaker": (59.92074, 10.63580),
"Sollerud": (59.92104, 10.64309),
"Furulund": (59.91990, 10.65013),
"Ullern": (59.92429, 10.65858),
"Abbediengen": (59.92517, 10.66716),
"Hoff": (59.92500, 10.67488),
"Skøyen": (59.92384, 10.68034),
"Thune": (59.92186, 10.68742),
"Nobels gate": (59.91758, 10.69866),
"Skarpsno": (59.91430, 10.70234),
"Skillebekk": (59.91277, 10.71103),
"Middelalderparken": (59.90639, 10.76417),
"Oslo Hospital": (59.9032, 10.7674),
"Ekebergparken": (59.8977, 10.7593),
"Jomfrubråten": (59.8883, 10.7706),
"Sportsplassen": (59.8860, 10.7736),
"Holtet": (59.88151, 10.78415),
"Sørli": (59.87493, 10.78709),
"Kastellet": (59.87106, 10.79036),
"Bråten": (59.86714, 10.79244),
"Sæter": (59.86102, 10.79870),
"Ljabru": (59.85335, 10.80089),
"Rikshospitalet": (59.947768, 10.714716),
"Gaustadalleen": (59.9454, 10.7172),
"Forskningsparken": (59.943513, 10.720425),
"Universitetet Blindern": (59.9421, 10.7243),
"John Collets plass": (59.9403, 10.7290),
"Ullevål sykehus": (59.9361, 10.7318),
"Adamstuen": (59.9326, 10.7345),
"Stensgata": (59.92957, 10.73303),
"Bislett": (59.92599, 10.73108),
"Dalsbergstien": (59.92354, 10.73163),
"Welhavens gate": (59.92131, 10.72968),
"Frydenlund": (59.92086, 10.73317),
"Holbergs plass": (59.91876, 10.73453),
"Lakkegata skole": (59.92055, 10.76834),
"Carl Berners plass": (59.926592, 10.778360),
"Sinsenkrysset": (59.93911, 10.78340),
"Grefsen stasjon": (59.94167, 10.78056),
"Homansbyen": (59.92278, 10.72639),
}
# Estimated trikk stop coordinates — no Wikidata P625 found.
# Derived from linear interpolation between verified neighbours,
# or placed from map/street knowledge. Max error ~150-250 m.
# To update: find Wikidata QID, fetch P625, move entry to TRIKK_STOPS_VERIFIED.
TRIKK_STOPS_ESTIMATED: dict[str, tuple[float, float]] = {
# ── Sentrum ───────────────────────────────────────────────────────────
"Tinghuset": (59.9146, 10.7403), # Ullevål Hageby-l ved Stortinget T
# ── Grünerløkka–Torshov-linjen ───────────────────────────────────────
"Torshov": (59.9332, 10.7643), # interp Biermanns gate↔Sandaker
# ── Kjelsåslinjen ────────────────────────────────────────────────────
"Doktor Smiths vei": (59.9503, 10.7867), # interp Disen↔Kjelsås t=0.20
"Kjelsåsalleen": (59.9641, 10.7833), # interp Disen↔Kjelsås t=0.90
# ── Frogner-linjen ───────────────────────────────────────────────────
"Frogner stadion": (59.9167, 10.7038), # Kirkeveien S for Vigelandsparken
# ── Vika-linjen ──────────────────────────────────────────────────────
"Ruseløkka": (59.9120, 10.7258), # interp Solli↔Kontraskjæret
# ── Ullevål Hageby-linjen ─────────────────────────────────────────────
"Tullinøkka": (59.9163, 10.7349), # interp Holbergs plass↔Tinghuset
# ── Sinsen-linjen ────────────────────────────────────────────────────
"Heimdalsgata": (59.9188, 10.7633), # interp Nybrua↔Lakkegata skole
"Sofienberg": (59.9236, 10.7734), # interp Lakkegata skole↔Carl Berners
"Rosenhoff": (59.9307, 10.7800), # interp Carl Berners↔Sinsenkrysset t=0.33
"Sinsenterrassen": (59.9350, 10.7817), # interp Carl Berners↔Sinsenkrysset t=0.67
"Tinghuset": (59.9146, 10.7403),
"Torshov": (59.9332, 10.7643),
"Doktor Smiths vei": (59.9503, 10.7867),
"Kjelsåsalleen": (59.9641, 10.7833),
"Frogner stadion": (59.9167, 10.7038),
"Ruseløkka": (59.9120, 10.7258),
"Tullinøkka": (59.9163, 10.7349),
"Heimdalsgata": (59.9188, 10.7633),
"Sofienberg": (59.9236, 10.7734),
"Rosenhoff": (59.9307, 10.7800),
"Sinsenterrassen": (59.9350, 10.7817),
}
# Merged — verified takes precedence if a key appears in both (shouldn't happen).
TRIKK_STOPS: dict[str, tuple[float, float]] = {
**TRIKK_STOPS_ESTIMATED,
**TRIKK_STOPS_VERIFIED,
@@ -323,13 +334,12 @@ TRIKK_STOPS: dict[str, tuple[float, float]] = {
# Transit helpers
# ---------------------------------------------------------------------------
_WALK_SPEED_KMH = 5.0 # avg walking speed
_WALK_SPEED_KMH = 5.0
def _nearest_stop(
lat: float, lng: float, stops: dict[str, tuple[float, float]]
) -> tuple[str, float]:
"""Return (stop_name, distance_km) for the nearest stop in a dict."""
best_name, best_dist = "", float("inf")
for name, (slat, slng) in stops.items():
d = _distance_km(lat, lng, slat, slng)
@@ -341,17 +351,7 @@ def _nearest_stop(
def nearby_transit(
lat: float, lng: float, max_walk_min: float = 10.0
) -> dict[str, list[tuple[str, float]]]:
"""Return T-bane and trikk stops within max_walk_min minutes walk.
Returns:
{
"tbane": [("Carl Berners plass", 0.28), ...], # sorted by distance
"trikk": [("Rosenhoff", 0.19), ...],
}
All distances in km.
"""
max_km = (max_walk_min / 60.0) * _WALK_SPEED_KMH
tbane = sorted(
[
(n, _distance_km(lat, lng, la, lo))
@@ -408,19 +408,7 @@ def score_neighbourhood(
def score_transport(unit: EiendomUnit | None) -> float:
"""Walking distance to nearest T-bane or trikk stop. Max 10.
Searches ALL stops in TBANE_STOPS and TRIKK_STOPS — no manual
curation needed when adding new search areas.
Distance bands:
< 400 m → 10 pts (~5 min walk)
< 800 m → 8 pts (~10 min — stated threshold)
< 1200 m → 4 pts (~15 min)
≥ 1200 m → 0 pts
Falls back to 0 when no coordinates available.
"""
"""Walking distance to nearest T-bane or trikk stop. Max 10."""
if unit is None or unit.lat is None or unit.lng is None:
return 0.0
@@ -529,7 +517,6 @@ def score_hybel(description: str) -> float:
if is_potential:
return 2.0
# Documented rental income → definitively real hybel
if "leieinntekt" in d or "skattefri" in d:
return 12.0
@@ -560,17 +547,10 @@ def score_hybel(description: str) -> float:
def score_floor(ad: Any, unit: EiendomUnit | None) -> float:
"""Floor level. Binary signal: ground floor is bad, everything else neutral.
"""Floor penalty. Ground floor (≤1) = -15. All other floors = 0.
Rationale: "toppleilighet i 3-etgs blokk" og "8. etg i høyblokk" er begge
topp for sin bygning. Etasjenummer alene sier ingenting om utsikt eller lys
uten å kjenne byggets totale høyde. Eneste reelle signal er 1. etg (innsyn,
støy, lys) vs ikke-1. etg.
Scores:
ground floor (≤1) → -15 (hard penalty: innsyn, støy, lys)
unknown → 0 (no data → no penalty)
above ground → 0 (etasjenummer uten bygghøyde = ingen info)
Rationale: floor number alone carries no signal without knowing building
height. The only reliable signal is ground floor (innsyn, støy, lys).
"""
floor: int | None = None
@@ -670,22 +650,43 @@ def score_renovation(description: str) -> float:
def score_risk(ad: Any, unit: EiendomUnit | None) -> float:
"""Risk penalty. Returns 0 or negative."""
"""Risk penalties. Returns 0 or negative.
Triggers:
No Eiendom.no data → -8 (can't price-check)
Shared debt > 500k/unit → -12 (hard red flag — total cost misleading)
Shared debt 200-500k/unit → -6 (notable, investigate)
Common costs > 8 000/mnd → -10 (structural sameie problem)
Common costs 6-8 000/mnd → -5
Days on market > 120 → -15 (something is wrong)
Days on market 60-120 → -5 (worth investigating)
"usikker" in description → -5
"""
penalty = 0.0
if unit is None:
penalty -= 8.0
# Shared debt — new: per-unit fellesgjeld signal
shared_debt = getattr(ad, "shared_debt", None)
if shared_debt is not None:
if shared_debt > _SHARED_DEBT_HIGH:
penalty -= 12.0
logger.debug("High shared debt: %d kr → -12", shared_debt)
elif shared_debt > _SHARED_DEBT_MEDIUM:
penalty -= 6.0
logger.debug("Medium shared debt: %d kr → -6", shared_debt)
fk = ad.common_costs or 0
if fk > 8000:
if fk > _COMMON_COST_HIGH:
penalty -= 10.0
elif fk > 6000:
elif fk > _COMMON_COST_MEDIUM:
penalty -= 5.0
if unit and unit.days_on_market:
if unit.days_on_market > 120:
penalty -= 10.0
elif unit.days_on_market > 60:
if unit.days_on_market > _DAYS_STALE:
penalty -= 15.0 # was -10
elif unit.days_on_market > _DAYS_SLOW:
penalty -= 5.0
if "usikker" in (ad.listing_description or "").lower():
@@ -702,15 +703,13 @@ def score_risk(ad: Any, unit: EiendomUnit | None) -> float:
def score_ad(ad: Any, unit: EiendomUnit | None, similar_units: list[SimilarUnit]) -> dict[str, Any]:
description = ad.listing_description or ""
# Collect nearby transit for informational output (not used in scoring)
transit_nearby: dict | None = None
if unit and unit.lat and unit.lng:
transit_nearby = nearby_transit(unit.lat, unit.lng, max_walk_min=10.0)
if transit_nearby["tbane"] or transit_nearby["trikk"]:
logger.debug("Nearby transit: %s", transit_nearby)
scores: dict[str, Any] = {
"floor": score_floor(ad, unit),
raw: dict[str, float] = {
"neighbourhood": score_neighbourhood(unit, ad.address, getattr(ad, "district", None)),
"view_and_quiet": score_view_and_quiet(ad, description),
"area_and_layout": score_area_and_layout(ad, unit),
@@ -724,14 +723,22 @@ def score_ad(ad: Any, unit: EiendomUnit | None, similar_units: list[SimilarUnit]
"building_health": score_building_health(ad, description),
"green_areas": score_green_areas(description),
"renovation": score_renovation(description),
}
penalties: dict[str, float] = {
"floor": score_floor(ad, unit),
"risk": score_risk(ad, unit),
}
# Numeric-only sum for total
numeric = {k: v for k, v in scores.items() if isinstance(v, (int, float))}
scores["total"] = float(_clamp(sum(numeric.values()), 0.0, 100.0))
weighted_bonus = sum(
(raw[d] / DIMENSION_MAX[d]) * DIMENSION_WEIGHTS[d] * 100.0 for d in DIMENSION_WEIGHTS
)
total_penalty = sum(penalties.values())
total = float(_clamp(weighted_bonus + total_penalty, 0.0, 100.0))
scores: dict[str, Any] = {**raw, **penalties, "total": total}
# Attach nearby transit as metadata (non-scoring)
if transit_nearby is not None:
scores["nearby_transit"] = transit_nearby
finn_eiendom/service.py
+41 -6
View File
@@ -8,11 +8,13 @@ from .analysis import analyze_search as run_analysis_search
from .cache import (
get_eiendom_unit as get_cached_eiendom_unit,
get_finn_ad,
get_similar_units as get_cached_similar_units,
init_db,
save_eiendom_unit,
save_finn_ad,
save_similar_units,
)
from .config import FINN_CACHE_PATH
from .config import EIENDOM_NO_CACHE_TTL_HOURS, FINN_CACHE_PATH
from .eiendom_no import (
build_unit_vector,
decode_unit_vector,
@@ -89,14 +91,47 @@ async def get_or_fetch_eiendom_unit(
async def get_or_fetch_similar_units(
unit_code: str, listing_status: str = "RECENTLY_SOLD", force_refresh: bool = False
) -> list[SimilarUnit]:
"""Get similar units (comps) from cache or fetch fresh."""
# Similar units don't have a separate cache table; fetch fresh each time per PRD
# (or cache them in search_runs if doing diff detection)
unit = await get_or_fetch_eiendom_unit(unit_code)
"""Get similar units (comps) from cache or fetch fresh.
Fetches the unit first to get the unit_vector, then checks cache for similar
units by (unit_code, listing_status). On cache miss, fetches fresh from
Eiendom.no and saves to cache.
"""
conn = init_db(FINN_CACHE_PATH)
# First, ensure we have the unit to build its vector
unit = await get_or_fetch_eiendom_unit(unit_code, force_refresh=force_refresh)
if unit is None:
return []
# Check cache for similar units (unless force_refresh)
if not force_refresh:
cached_similar = get_cached_similar_units(
conn, unit_code, listing_status, ttl_hours=EIENDOM_NO_CACHE_TTL_HOURS
)
if cached_similar:
logger.debug(
"Using cached similar units for %s (status=%s)",
unit_code,
listing_status,
)
return cached_similar
# Cache miss or force_refresh: fetch fresh
vector = build_unit_vector(unit)
return await get_similar_units(vector, listing_status=listing_status)
similar = await get_similar_units(vector, listing_status=listing_status)
# Save to cache
if similar:
save_similar_units(conn, unit_code, listing_status, similar)
logger.debug(
"Cached %d similar units for %s (status=%s)",
len(similar),
unit_code,
listing_status,
)
return similar
async def get_unit_images(unit_code: str, force_refresh: bool = False) -> dict[str, Any]: