initial commit

backend/app/core/config.py

@@ -0,0 +1,10 @@
+from pydantic_settings import BaseSettings
+
+class Settings(BaseSettings):
+    API_V1_STR: str = "/api/v1"
+    PROJECT_NAME: str = "House Price Predictor"
+    
+    class Config:
+        case_sensitive = True
+
+settings = Settings() 

backend/app/main.py

@@ -0,0 +1,21 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from app.routers import predictions
+
+app = FastAPI(
+    title="Housing Price Predictor API",
+    description="API for predicting housing prices",
+    version="1.0.0"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include routers
+app.include_router(predictions.router, prefix="/api/v1") 

backend/app/models/prediction.py

@@ -0,0 +1,21 @@
+from pydantic import BaseModel, Field
+from typing import List
+
+class HousePredictionInput(BaseModel):
+    square_feet: float = Field(..., gt=0, description="Square footage of the house")
+    bedrooms: int = Field(..., ge=1, description="Number of bedrooms")
+    bathrooms: float = Field(..., gt=0, description="Number of bathrooms")
+    
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "square_feet": 2000,
+                "bedrooms": 3,
+                "bathrooms": 2.5
+            }
+        }
+
+class HousePredictionOutput(BaseModel):
+    predicted_price: float
+    confidence_score: float
+    similar_listings: List[float] 

backend/app/routers/predictions.py

@@ -0,0 +1,27 @@
+from fastapi import APIRouter, HTTPException
+from app.models.prediction import HousePredictionInput, HousePredictionOutput
+from app.services.ml_model import HousePricePredictor
+
+router = APIRouter()
+model = HousePricePredictor()
+
+@router.post("/predict", 
+    response_model=HousePredictionOutput,
+    summary="Predict house price",
+    description="Predicts the price of a house based on its features"
+)
+async def predict_price(input_data: HousePredictionInput) -> HousePredictionOutput:
+    try:
+        predicted_price, confidence_score, similar_listings = model.predict(
+            input_data.square_feet,
+            input_data.bedrooms,
+            input_data.bathrooms
+        )
+        
+        return HousePredictionOutput(
+            predicted_price=predicted_price,
+            confidence_score=confidence_score,
+            similar_listings=similar_listings
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e)) 

backend/app/services/ml_model.py

@@ -0,0 +1,35 @@
+import random
+from typing import List, Tuple
+
+class HousePricePredictor:
+    """
+    Mock ML model that predicts house prices.
+    In a real scenario, this would load a trained model.
+    """
+    
+    def __init__(self):
+        # Mock initialization - in reality would load model weights
+        pass
+    
+    def predict(self, square_feet: float, bedrooms: int, bathrooms: float) -> Tuple[float, float, List[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
+        confidence_score = random.uniform(0.8, 0.99)
+        similar_listings = [
+            predicted_price * random.uniform(0.9, 1.1) 
+            for _ in range(3)
+        ]
+        
+        return predicted_price, confidence_score, similar_listings 

backend/requirements.txt

@@ -0,0 +1,3 @@
+fastapi
+pydantic
+uvicorn