fix(): adaptix is a common dependency due to it being used in domain

2025-11-22 14:26:39 +03:00
parent 00af067f4d
commit 71389d5d82
8 changed files with 181 additions and 4 deletions
@@ -0,0 +1,123 @@
+from decimal import Decimal
+
+from dishka import FromDishka
+from dishka.integrations.fastapi import DishkaRoute
+from fastapi import APIRouter, status
+from pydantic import BaseModel, Field
+
+from template_project.application.resume.entity import ResumeId
+from template_project.ml.interactors.predict_salary import (
+    PredictSalaryInteractor,
+    PredictSalaryRequest,
+    PredictSalaryResponse,
+    VacancyInput,
+)
+
+router = APIRouter(route_class=DishkaRoute, tags=["Prediction"])
+
+
+class VacancyInputModel(BaseModel):
+    vacancy_id: str = Field(description="Vacancy ID", examples=["vacancy_123"])
+    from_salary: Decimal = Field(description="Minimum salary", examples=[Decimal(100000)])
+    to_salary: Decimal = Field(description="Maximum salary", examples=[Decimal(150000)])
+    key_skills: list[str] = Field(description="List of key skills", examples=[["Python", "FastAPI", "PostgreSQL"]])
+    resume_similarity: float = Field(
+        ge=0.0, le=1.0, description="Resume similarity score (0.0 to 1.0)", examples=[0.85]
+    )
+
+    model_config = {
+        "json_schema_extra": {
+            "example": {
+                "vacancy_id": "vacancy_123",
+                "from_salary": "100000",
+                "to_salary": "150000",
+                "key_skills": ["Python", "FastAPI", "PostgreSQL"],
+                "resume_similarity": 0.85,
+            }
+        }
+    }
+
+
+class PredictSalaryRequestModel(BaseModel):
+    resume_id: ResumeId = Field(description="Resume ID", examples=["01234567-89ab-cdef-0123-456789abcdef"])
+    key_skills: list[str] = Field(
+        min_length=1, description="List of key skills from resume", examples=[["Python", "FastAPI", "PostgreSQL"]]
+    )
+    vacancies: list[VacancyInputModel] = Field(
+        min_length=1, description="List of relevant vacancies", examples=[[]]
+    )
+
+    model_config = {
+        "json_schema_extra": {
+            "example": {
+                "resume_id": "01234567-89ab-cdef-0123-456789abcdef",
+                "key_skills": ["Python", "FastAPI", "PostgreSQL"],
+                "vacancies": [
+                    {
+                        "vacancy_id": "vacancy_123",
+                        "from_salary": "100000",
+                        "to_salary": "150000",
+                        "key_skills": ["Python", "FastAPI", "PostgreSQL", "Docker"],
+                        "resume_similarity": 0.85,
+                    }
+                ],
+            }
+        }
+    }
+
+
+class PredictSalaryResponseModel(BaseModel):
+    salary_from: Decimal = Field(description="Minimum predicted salary", examples=[Decimal(100000)])
+    salary_to: Decimal = Field(description="Maximum predicted salary", examples=[Decimal(150000)])
+    recommended_skills: list[str] = Field(
+        description="Top 3 recommended skills", examples=[["Kubernetes", "Redis", "Docker"]]
+    )
+
+    model_config = {
+        "json_schema_extra": {
+            "example": {
+                "salary_from": "100000",
+                "salary_to": "150000",
+                "recommended_skills": ["Kubernetes", "Redis", "Docker"],
+            }
+        }
+    }
+
+
+@router.post(
+    "/predict_salary",
+    summary="Predict salary",
+    description="Predict salary range and recommend skills based on resume and relevant vacancies",
+    responses={
+        200: {"description": "Salary prediction generated successfully", "model": PredictSalaryResponseModel},
+    },
+)
+async def predict_salary(
+    request: PredictSalaryRequestModel,
+    interactor: FromDishka[PredictSalaryInteractor],
+) -> PredictSalaryResponseModel:
+    vacancy_inputs = [
+        VacancyInput(
+            vacancy_id=vacancy.vacancy_id,
+            from_salary=vacancy.from_salary,
+            to_salary=vacancy.to_salary,
+            key_skills=vacancy.key_skills,
+            resume_similarity=vacancy.resume_similarity,
+        )
+        for vacancy in request.vacancies
+    ]
+
+    predict_request = PredictSalaryRequest(
+        resume_id=request.resume_id,
+        key_skills=request.key_skills,
+        vacancies=vacancy_inputs,
+    )
+
+    response = await interactor.execute(predict_request)
+
+    return PredictSalaryResponseModel(
+        salary_from=response.salary_from,
+        salary_to=response.salary_to,
+        recommended_skills=response.recommended_skills,
+    )
+