Merge pull request #1 from Axiomatic-AI/try-excercise

Add some fixes after trying the excercise

backend/app/dtos/house_create_request.py

@@ -21,3 +21,14 @@ class HouseCreateRequest(BaseModel):
         description="Description of the house",
         examples=["A beautiful 3-bedroom house"],
     )
+    square_feet: float = Field(
+        ...,
+        description="Square footage of the house",
+        examples=[1500.00],
+    )
+    bedrooms: int = Field(
+        ..., description="Number of bedrooms in the house", examples=[3]
+    )
+    bathrooms: float = Field(
+        ..., description="Number of bathrooms in the house", examples=[2.5]
+    )

backend/app/models/house.py

@@ -11,3 +11,6 @@ class House(SQLModel, table=True):
     price: float = Field()
     description: str = Field()
     owner_user_id: UUID = Field(foreign_key="owner.user_id")
+    square_feet: float = Field()
+    bedrooms: int = Field()
+    bathrooms: float = Field()

backend/app/repositories/house_repository.py

@@ -1,9 +1,9 @@
-from typing import Annotated
+from typing import Annotated, Literal
 from uuid import UUID
 
 from fastapi import Depends
 from sqlalchemy.ext.asyncio.session import AsyncSession
-from sqlmodel import select
+from sqlmodel import asc, desc, select
 
 from ..models.house import House
 from ..providers.db_provider import get_session
@@ -13,8 +13,19 @@ class HouseRepository:
     def __init__(self, session: Annotated[AsyncSession, Depends(get_session)]) -> None:
         self.session = session
 
-    async def get_all(self, limit: int = 100, offset: int = 0) -> list[House]:
+    async def get_all(
-        statement = select(House).offset(offset).limit(limit)
+        self,
+        limit: int = 100,
+        offset: int = 0,
+        order_by: Literal["PRICE"] = "PRICE",
+        sort_order: Literal["ASC", "DESC"] = "DESC",
+    ) -> list[House]:
+        sorter = desc if sort_order == "DESC" else asc
+
+        statement = (
+            select(House).offset(offset).limit(limit).order_by(sorter(House.price))
+        )
+
         result = await self.session.execute(statement)
         return result.scalars().all()
 

backend/app/routers/houses.py

@@ -1,4 +1,4 @@
-from typing import Annotated
+from typing import Annotated, Literal
 
 from fastapi import APIRouter, Depends
 
@@ -35,6 +35,9 @@ async def create_house(
         country=body.country,
         price=body.price,
         description=body.description,
+        square_feet=body.square_feet,
+        bedrooms=body.bedrooms,
+        bathrooms=body.bathrooms,
     )
 
     await house_repository.save(house)
@@ -45,10 +48,14 @@ async def create_house(
 @router.get("")
 async def get_all_houses(
     house_repository: Annotated[HouseRepository, Depends()],
+    order_by: Literal["PRICE"] = "PRICE",
+    sort_order: Literal["ASC", "DESC"] = "DESC",
     limit: int = 100,
     offset: int = 0,
 ) -> HousesListResponse:
-    all_houses = await house_repository.get_all(offset=offset, limit=limit)
+    all_houses = await house_repository.get_all(
+        offset=offset, limit=limit, order_by=order_by, sort_order=sort_order
+    )
 
     house_responses = [
         HouseResponse(
@@ -61,6 +68,5 @@ async def get_all_houses(
         )
         for house in all_houses
     ]
-    print(house_responses)
 
     return HousesListResponse(houses=house_responses)

backend/app/services/house_price_predictor.py

@@ -1,45 +1,16 @@
-import random
-from dataclasses import dataclass
-from typing import List
-
-
-@dataclass
-class Prediction:
-    predicted_price: float
-    confidence_score: float
-    similar_listings: List[float]
-
-
 class HousePricePredictor:
     """
     Mock ML model that predicts house prices.
     In a real scenario, this would load a trained model.
     """
 
-    def __init__(self):
+    async def predict(
-        # Mock initialization - in reality would load model weights
-        pass
-
-    def predict(
         self, square_feet: float, bedrooms: int, bathrooms: float
-    ) -> Prediction:
+    ) -> float:
-        """
-        Mock prediction method that returns:
-        - predicted price
-        - confidence score
-        - similar listing prices
-        """
-        # Mock prediction logic
         base_price = square_feet * 200
         bedroom_value = bedrooms * 25000
         bathroom_value = bathrooms * 15000
 
         predicted_price = base_price + bedroom_value + bathroom_value
 
-        # Add some randomness to make it interesting
+        return predicted_price
-        confidence_score = random.uniform(0.8, 0.99)
-        similar_listings = [
-            predicted_price * random.uniform(0.9, 1.1) for _ in range(3)
-        ]
-
-        return Prediction(predicted_price, confidence_score, similar_listings)

backend/app/services/investor_predictor.py

@@ -1,5 +1,7 @@
 import random
 
+from ..models import User
+
 
 class InvestorPredictor:
     def is_investor(user: User) -> bool: