fix(): fix e2e ml tests, handle no entries for vacancies

src/template_project/adapters/data_gateways/tables.py

@@ -22,7 +22,7 @@ from sqlalchemy.orm import registry
 
 from template_project.application.access_token.entity import AccessToken
 from template_project.application.auth_identity.entity import AuthIdentity, AuthMethod
-from template_project.application.common.enums import EducationGrade, ExperienceType, ExperienceType
+from template_project.application.common.enums import EducationGrade, ExperienceType
 from template_project.application.notification_device.entity import NotificationDevice
 from template_project.application.resume.entity import (
     Resume,

src/template_project/adapters/ml_api_gateway.py

@@ -57,6 +57,7 @@ class MlApiGateway:
             timeout=100,
         )
 
+        response.raise_for_status()
         response_json = response.json()
         return GenerateResumePredictionResponse(
             salary_from=Decimal(str(response_json["salary_from"])),

src/template_project/application/resume/interactors/predict_model.py

@@ -46,7 +46,7 @@ class PredictModelInteractor:
 
     def _predict_salary(self, vacancies: list[VacancyInput], resume_skills: list[str]) -> tuple[Decimal, Decimal]:
         if not vacancies:
-            return Decimal(50000), Decimal(80000)
+            return Decimal(0), Decimal(0)
 
         vacancy_weights: list[float] = []
         for vacancy in vacancies:
@@ -56,7 +56,7 @@ class PredictModelInteractor:
 
         total_weight = sum(vacancy_weights)
         if total_weight == 0:
-            return Decimal(50000), Decimal(80000)
+            return Decimal(0), Decimal(0)
 
         weighted_from_sum = Decimal(0)
         weighted_to_sum = Decimal(0)
@@ -143,6 +143,9 @@ class PredictModelInteractor:
             if skill in candidate_skills
         }
 
+        if not candidate_skills:
+            return []
+
         frequencies = [skill_frequencies[skill] for skill in candidate_skills]
         avg_salaries = [float(skill_avg_salaries[skill]) for skill in candidate_skills]
 

src/template_project/ml/entry_point.py

@@ -3,6 +3,8 @@ import asyncio
 import logging
 import os
 import sys
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
 from pathlib import Path
 from typing import Final
 
@@ -11,6 +13,7 @@ from dishka import AsyncContainer
 from dishka.integrations.fastapi import setup_dishka
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
+from sentence_transformers import SentenceTransformer
 
 from template_project.ml.configuration import load_configuration
 from template_project.ml.ioc.make import make_ioc
@@ -37,10 +40,18 @@ LOG_CONFIG: Final = {
 }
 
 
+@asynccontextmanager
+async def lifespan(app: FastAPI) -> AsyncIterator[None]:
+    await app.state.dishka_container.get(SentenceTransformer)
+    yield
+    await app.state.dishka_container.close()
+
+
 def make_asgi_application(
     ioc: AsyncContainer,
 ) -> FastAPI:
     app = FastAPI(
+        lifespan=lifespan,
         docs_url="/docs",
         title="ML Service",
         description="ML Service API",
@@ -81,10 +92,7 @@ async def _main(
         access_log=configuration.server.access_log,
     )
     server = uvicorn.Server(config)
-    try:
-        await server.serve()
-    finally:
-        await ioc.close()
+    await server.serve()
 
 
 def main() -> None:

src/template_project/ml/routes/predict.py

@@ -42,7 +42,7 @@ class PredictSalaryRequestModel(BaseModel):
     key_skills: list[str] = Field(
         description="List of key skills from resume", examples=[["Python", "FastAPI", "PostgreSQL"]]
     )
-    vacancies: list[VacancyInputModel] = Field(description="List of relevant vacancies", examples=[[]])
+    vacancies: list[VacancyInputModel] = Field(description="List of relevant vacancies", examples=[[]], min_length=0)
 
     model_config = {
         "json_schema_extra": {