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323fee6 5b27429 323fee6 5b27429 323fee6 5b27429 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | """FastAPI service exposing the inference pipelines + catalog / FX management.
Endpoints:
GET /health service + model state
GET /catalogs list known parts-cost catalogs
POST /catalogs/{catalog_id}/activate flip the active symlink
GET /fx current cached USD->INR rate
POST /fx/refresh fetch a fresh rate
POST /estimate run Variant A / B / both on an upload
Pipelines are instantiated at startup (one model load) and reused per request.
"""
from __future__ import annotations
import io
from contextlib import asynccontextmanager
from datetime import datetime, timezone
from pathlib import Path
from typing import Optional
from fastapi import FastAPI, File, Form, HTTPException, UploadFile
from fastapi.responses import Response
from ccdp.api.schemas import (
CatalogEntry,
Currency,
FxResponse,
HealthResponse,
ModelChoice,
)
from ccdp.costing import activate as activate_catalog
from ccdp.costing import fx as fxmod
from ccdp.costing import list_catalogs, load_active
from ccdp.identification.car_identifier import IdentificationResult, infer_segment
from ccdp.infer.variant_a import VariantAPipeline
from ccdp.preprocess import preprocess
from ccdp.utils import pick_device
from ccdp.viz import annotate_prediction
# ---------------------------------------------------------------------------
# Lifespan — load pipelines once
# ---------------------------------------------------------------------------
_state: dict = {}
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Load pipelines at boot; never per request."""
_state["device"] = str(pick_device())
_state["variant_a"] = None
_state["variant_b"] = None
try:
_state["variant_a"] = VariantAPipeline()
print(f"[api] Variant A pipeline loaded on {_state['device']}")
except Exception as e: # noqa: BLE001
print(f"[api] Variant A unavailable: {e}")
try:
from ccdp.infer.variant_b import VariantBPipeline
_state["variant_b"] = VariantBPipeline()
print(f"[api] Variant B pipeline loaded on {_state['device']}")
except Exception as e: # noqa: BLE001
print(f"[api] Variant B unavailable: {e}")
yield
_state.clear()
app = FastAPI(
title="ccdp",
description="Car Crash Fix Amount Predictor — damage recognition + cost estimation.",
version="0.1.0",
lifespan=lifespan,
)
# ---------------------------------------------------------------------------
# Health
# ---------------------------------------------------------------------------
@app.get("/health", response_model=HealthResponse)
def health() -> HealthResponse:
"""Service liveness + which models are loaded + catalog / FX state."""
try:
active = load_active()
catalog_id = active.catalog_id
except FileNotFoundError:
catalog_id = None
fx_rate: Optional[float] = None
fx_age_hours: Optional[float] = None
try:
fr = fxmod.get_rate("USD", "INR", allow_stale=True)
fx_rate = fr.rate
fetched = datetime.fromisoformat(fr.fetched_at)
fx_age_hours = (datetime.now(timezone.utc) - fetched).total_seconds() / 3600
except Exception: # noqa: BLE001
pass
return HealthResponse(
status="ok",
active_catalog=catalog_id,
fx_rate=fx_rate,
fx_age_hours=fx_age_hours,
models={
"variant_a": "loaded" if _state.get("variant_a") else None,
"variant_b": "loaded" if _state.get("variant_b") else None,
},
device=_state.get("device", "unknown"),
)
# ---------------------------------------------------------------------------
# Catalogs
# ---------------------------------------------------------------------------
@app.get("/catalogs", response_model=list[CatalogEntry])
def catalogs() -> list[CatalogEntry]:
rows = list_catalogs()
return [CatalogEntry(
catalog_id=r["catalog_id"],
created_at=r.get("created_at"),
currency=r.get("currency"),
is_active=r["is_active"],
) for r in rows]
@app.post("/catalogs/{catalog_id}/activate")
def catalog_activate(catalog_id: str) -> dict:
try:
activate_catalog(catalog_id)
return {"activated": catalog_id}
except FileNotFoundError as e:
raise HTTPException(status_code=404, detail=str(e))
# ---------------------------------------------------------------------------
# FX
# ---------------------------------------------------------------------------
@app.get("/fx", response_model=FxResponse)
def fx_show() -> FxResponse:
try:
fr = fxmod.get_rate("USD", "INR")
except RuntimeError as e:
raise HTTPException(status_code=503, detail=str(e))
return FxResponse(base=fr.base, target=fr.target, rate=fr.rate,
source=fr.source, fetched_at=fr.fetched_at)
@app.post("/fx/refresh", response_model=FxResponse)
def fx_refresh() -> FxResponse:
fr = fxmod.refresh_rate("USD", "INR")
return FxResponse(base=fr.base, target=fr.target, rate=fr.rate,
source=fr.source, fetched_at=fr.fetched_at)
# ---------------------------------------------------------------------------
# Estimate (the main endpoint)
# ---------------------------------------------------------------------------
def _build_identification(make, model_name, year, body_type) -> Optional[IdentificationResult]:
if not make:
return None
return IdentificationResult(
image_path=Path(""),
make=make.lower(),
model=(model_name.lower() if model_name else None),
year=year,
body_type=body_type or "unknown",
segment=infer_segment(make),
confidence=1.0,
source="user",
)
@app.post("/estimate")
async def estimate(
image: UploadFile = File(..., description="JPEG or PNG car damage image"),
model: ModelChoice = Form("both"),
currency: Currency = Form("USD"),
make: Optional[str] = Form(None),
model_name: Optional[str] = Form(None),
year: Optional[int] = Form(None),
body_type: Optional[str] = Form("unknown"),
refresh_fx: bool = Form(False),
) -> dict:
"""Run the chosen variant(s) on an uploaded image and return a structured response."""
raw = await image.read()
if not raw:
raise HTTPException(status_code=400, detail="Empty upload")
try:
pil_image, preprocessing_meta = preprocess(raw)
except Exception as e: # noqa: BLE001
raise HTTPException(status_code=400, detail=f"Could not decode image: {e}")
if refresh_fx:
try:
fxmod.refresh_rate("USD", "INR")
except Exception as e: # noqa: BLE001
print(f"[api] FX refresh failed (continuing): {e}")
metadata = _build_identification(make, model_name, year, body_type)
response: dict = {
"preprocessing": preprocessing_meta,
"active_catalog": load_active().catalog_id,
}
if model in ("resnet", "both"):
pipe = _state.get("variant_a")
if pipe is None:
raise HTTPException(status_code=503, detail="Variant A model not loaded")
response["variant_a"] = pipe.predict(
pil_image, metadata=metadata, currency=currency,
).to_dict()
if model in ("yolov8", "both"):
pipe = _state.get("variant_b")
if pipe is None:
if model == "yolov8":
raise HTTPException(status_code=503, detail="Variant B model not loaded")
# 'both' is best-effort — silently skip B if unavailable
else:
response["variant_b"] = pipe.predict(
pil_image, metadata=metadata, currency=currency,
).to_dict()
return response
# ---------------------------------------------------------------------------
# Annotated image — same flow as /estimate but returns a PNG with boxes drawn
# ---------------------------------------------------------------------------
@app.post("/estimate/annotated",
responses={200: {"content": {"image/png": {}}}, 503: {}, 400: {}})
async def estimate_annotated(
image: UploadFile = File(..., description="JPEG or PNG car damage image"),
) -> Response:
"""Return the uploaded image with YOLOv8 damage boxes overlaid as PNG.
Always runs Variant B because that's the only variant producing boxes.
"""
raw = await image.read()
if not raw:
raise HTTPException(status_code=400, detail="Empty upload")
try:
pil_image, _ = preprocess(raw)
except Exception as e: # noqa: BLE001
raise HTTPException(status_code=400, detail=f"Could not decode image: {e}")
pipe = _state.get("variant_b")
if pipe is None:
raise HTTPException(status_code=503, detail="Variant B model not loaded")
pred = pipe.predict(pil_image, metadata=None, currency="USD")
annotated = annotate_prediction(pil_image, pred)
buf = io.BytesIO()
annotated.save(buf, format="PNG")
return Response(content=buf.getvalue(), media_type="image/png")
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