Devtest solution. #77

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AdailtonHolanda wants to merge 1 commit into Citric-Sheep:master from AdailtonHolanda:devtest_solution

app/__init__.py

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app/__pycache__/__init__.cpython-311.pyc

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app/__pycache__/main.cpython-311.pyc

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app/database/__init__.py

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app/database/__pycache__/elevators.cpython-311.pyc

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app/database/elevators.py

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+    from sqlalchemy import create_engine
+    from sqlalchemy.orm import sessionmaker, DeclarativeBase
+    DATABASE_URL = "sqlite:///./elevators.db"
+    engine = create_engine(
+        DATABASE_URL,
+        connect_args={"check_same_thread": False},
+    )
+    SessionLocal = sessionmaker(bind=engine, expire_on_commit=False)
+    class Base(DeclarativeBase):
+        pass

app/main.py

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+    import logging
+    from fastapi import FastAPI
+    from dotenv import load_dotenv
+    from app.database.elevators import Base, engine
+    from app.routers.elevators import router as main_router
+    load_dotenv()
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger(__name__)
+    app = FastAPI(title="Prediction_Engine")
+    @app.on_event("startup")
+    def on_startup():
+        """
+        Function executed once when the FastAPI app starts.
+        It ensures that all database tables defined in the SQLAlchemy models are created.
+        """
+        Base.metadata.create_all(bind=engine)
+        logger.info("SQLite schema ready")
+    app.include_router(main_router)

app/models/__init__.py

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app/models/__pycache__/elevators.cpython-311.pyc

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app/models/elevators.py

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+    from datetime import datetime
+    from sqlalchemy import Column, Integer, DateTime, Boolean
+    from app.database.elevators import Base
+    class Demand(Base):
+        __tablename__ = "demands"
+        id = Column(Integer, primary_key=True, index=True)
+        floor = Column(Integer, nullable=False)
+        timestamp = Column(DateTime, default=datetime.utcnow)  # ← OK aqui
+    class ElevatorState(Base):
+        __tablename__ = "elevator_states"
+        id = Column(Integer, primary_key=True, index=True)
+        timestamp = Column(DateTime, default=datetime.utcnow, index=True)
+        floor = Column(Integer, nullable=False)
+        vacant = Column(Boolean, nullable=False)

app/routers/__init__.py

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app/routers/__pycache__/elevators.cpython-311.pyc

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app/routers/elevators.py

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+    from fastapi import APIRouter, Depends, HTTPException
+    from sqlalchemy.orm import Session
+    from app.models.elevators import Demand, ElevatorState
+    from app.schemas.elevators import DemandOut, DemandCreate, StateOut, StateCreate
+    from app.database.elevators import SessionLocal
+    router = APIRouter(prefix="/api", tags=["elevators"])
+    def get_db():
+        db = SessionLocal()
+        try:
+            yield db
+            db.commit()
+        except Exception:
+            db.rollback()
+            raise
+        finally:
+            db.close()
+    # POST /demands
+    @router.post("/demands", response_model=DemandOut)
+    def create_demand(demand: DemandCreate, db: Session = Depends(get_db)):
+        """
+        Registers a new elevator demand when a user calls the elevator from a specific floor.
+        """
+        rec = Demand(floor=demand.floor)
+        db.add(rec)
+        db.flush()
+        db.refresh(rec)
+        return rec
+    # POST /state
+    @router.post("/state", response_model=StateOut)
+    def create_state(state: StateCreate, db: Session = Depends(get_db)):
+        """
+        Records the elevator’s current state, including its current floor and whether it is vacant.
+        """
+        rec = ElevatorState(floor=state.floor, vacant=state.vacant)
+        db.add(rec)
+        db.flush()
+        db.refresh(rec)
+        return rec
+    # GET /analytics
+    @router.get("/analytics")
+    def analyze_positioning(db: Session = Depends(get_db), limit: int = 50, threshold: int = 3):
+        """
+        Analyzes elevator positioning efficiency by comparing resting positions with upcoming demands.
+        """
+        states = (
+            db.query(ElevatorState)
+            .order_by(ElevatorState.timestamp.asc())
+            .limit(limit)
+            .all()
+        )
+        if not states:
+            raise HTTPException(status_code=404, detail="No elevator states found.")
+        distances = []
+        for state in states:
+            demand = (
+                db.query(Demand)
+                .filter(Demand.timestamp > state.timestamp)
+                .order_by(Demand.timestamp.asc())
+                .first()
+            )
+            if demand:
+                distance = abs(demand.floor - state.floor)
+                distances.append(distance)
+        if not distances:
+            raise HTTPException(status_code=404, detail="Not enough state → demand pairs found.")
+        avg_distance = sum(distances) / len(distances)
+        return {
+            "average_distance": round(avg_distance, 2),
+            "threshold": threshold,
+            "mispositioned": avg_distance > threshold,
+            "message": "Elevator is frequently poorly positioned." if avg_distance > threshold else "Positioning is within acceptable range."
+        }

app/schemas/__init__.py

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app/schemas/__pycache__/elevators.cpython-311.pyc

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app/schemas/elevators.py

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+    from datetime import datetime
+    from pydantic import BaseModel, Field
+    class DemandCreate(BaseModel):
+        floor: int = Field(..., ge=0, description="Andar onde o usuário chamou o elevador")
+    class DemandOut(DemandCreate):
+        id: int
+        timestamp: datetime
+        model_config = {
+            "from_attributes": True  # substitui orm_mode no Pydantic v2
+        }
+    class StateCreate(BaseModel):
+        floor: int
+        vacant: bool
+    class StateOut(StateCreate):
+        id: int
+        timestamp: datetime
+        model_config = {
+            "from_attributes": True
+        }

chatgpt/app_tests.py

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chatgpt/db.sql

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chatgpt/main.py

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chatgpt/requirements.txt

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elevators.db

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explanation.md

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+    ### Problem Understanding
+    The goal was to build a minimal backend system to record elevator demands and resting states, so that historical data can be used later by an ML model to predict the optimal resting floor.
+    ### Design Decisions
+. Database Modeling
+    I created two tables:
+    Demand: stores the floor where the elevator was called and the timestamp.
+    ElevatorState: stores the elevator’s floor, timestamp, and whether it's vacant.
+    This separation allows:
+    Compare demand vs. elevator location.
+    Simulate a real scenario with frequent calls and elevator resting positions.
+    Alternative: I considered using a single table, but separating concerns made analysis easier and aligned with normalized schema practices.
+. API Endpoints
+    POST /api/demands: register a new demand.
+    POST /api/state: register the elevator’s current floor and availability.
+    GET /api/analytics: analyze the average distance between each recorded elevator state and the subsequent demand. This helps evaluate whether the elevator is frequently poorly positioned when idle.
+    This gives ML practitioners useful data such as:
+    Time series of elevator calls.
+    Reactions or delays from elevator states.
+    Or initiate a model retraining process to improve floor prediction accuracy based on updated behavior patterns.
+. Business Rules Added
+    In GET /analytics, we check if the elevator is often resting too far from the next demand, using a configurable threshold.
+    This acts as an early signal of poor optimization.
+. Testing
+    Covered endpoints with pytest and TestClient.
+    Verified status codes and response content.
+    Ensures system stability before plugging in any ML logic.
+    ### Tech Stack
+    FastAPI + SQLite.
+    SQLAlchemy for ORM.
+    Pytest for testing.

readme.md

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     # Dev Test
+    ## RUN APP
+    Instruction to run application
+    ### Create the virtual environment
+    python -m venv test_dev
+    ### Activate the virtual environment
+    source test_dev/bin/activate (Linux)
+    ### Install packages
+    pip install -r requirements.txt
+    ### Start the API server
+    uvicorn app.main:app --reload
+    ### Run tests
+    pytest tests/test_endpoints.py
+    ### Explanation
+    The file explanation.md has the explanation about the solution.
     ## Elevators
     When an elevator is empty and not moving this is known as it's resting floor.
     The ideal resting floor to be positioned on depends on the likely next floor that the elevator will be called from.
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requirements.txt

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+    fastapi==0.115.14
+    uvicorn==0.35.0
+    SQLAlchemy==2.0.41
+    pydantic==2.11.7
+    python-dotenv==1.1.1
+    pytest==8.4.1
+    httpx==0.28.1

tests/__init__.py

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tests/__pycache__/endpoints.cpython-311-pytest-7.4.0.pyc

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tests/test_endpoints.py

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+    from fastapi.testclient import TestClient
+    from app.main import app
+    client = TestClient(app)
+    def test_create_demand():
+        resp = client.post("/api/demands", json={"floor": 3})
+        assert resp.status_code == 200
+        payload = resp.json()
+        assert payload["floor"] == 3
+        assert "id" in payload
+        assert "timestamp" in payload
+    def test_create_state():
+        resp = client.post("/api/state", json={"floor": 2, "vacant": True})
+        assert resp.status_code == 200
+        payload = resp.json()
+        assert payload["floor"] == 2
+        assert payload["vacant"] is True
+        assert "timestamp" in payload
+    def test_analyze_positioning():
+        # Criar um state antigo
+        client.post("/api/state", json={"floor": 1, "vacant": True})
+        # Criar uma demanda posterior
+        client.post("/api/demands", json={"floor": 5})
+        # Chamar a rota de análise
+        resp = client.get("/api/analytics", params={"limit": 10, "threshold": 2})
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "average_distance" in data
+        assert "mispositioned" in data
+        assert isinstance(data["average_distance"], float)
+        assert isinstance(data["mispositioned"], bool)

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