feat(): prediction pipeline

src/template_project/adapters/ml_api_gateway.py

@@ -36,28 +36,29 @@ class MlApiGateway:
         self,
         resume_id: ResumeId,
         key_skills: list[str],
-        suituble_vacancies: Sequence[SuitableVacancyDs],
+        suitable_vacancies: Sequence[SuitableVacancyDs],
     ) -> GenerateResumePredictionResponse:
         response = await self._client.post(
-            "/predict_salary",
+            "/predict",
             json={
-                "resume_id": resume_id,
+                "resume_id": str(resume_id),
                 "key_skills": key_skills,
                 "vacancies": [
                     {
-                        "vacancy_id": suituble_vacancy.vacancy_id,
-                        "from_salary": suituble_vacancy.from_salary,
-                        "to_salary": suituble_vacancy.to_salary,
-                        "key_skills": suituble_vacancy.key_skills,
-                        "resume_similarity": suituble_vacancy.resume_similarity,
-                    } for suituble_vacancy in suituble_vacancies
+                        "vacancy_id": str(suitable_vacancy.vacancy_id),
+                        "from_salary": str(suitable_vacancy.from_salary),
+                        "to_salary": str(suitable_vacancy.to_salary),
+                        "key_skills": suitable_vacancy.key_skills,
+                        "resume_similarity": suitable_vacancy.resume_similarity,
+                    }
+                    for suitable_vacancy in suitable_vacancies
                 ],
             },
         )
 
         response_json = response.json()
         return GenerateResumePredictionResponse(
-            salary_from=response_json["salary_from"],
-            salary_to=response_json["salary_to"],
+            salary_from=Decimal(str(response_json["salary_from"])),
+            salary_to=Decimal(str(response_json["salary_to"])),
             recommended_skills=response_json["recommended_skills"],
         )