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File size: 1,592 Bytes
fc41845 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import os
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
import joblib
import pandas as pd
import uvicorn
app = FastAPI(title="Morocco Real Estate API")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Load Model
MODEL_PATH = "morocco_re_model_pro.joblib"
model = None
if os.path.exists(MODEL_PATH):
model = joblib.load(MODEL_PATH)
class PropertyFeatures(BaseModel):
City: str
Neighborhood: str
Type: str
Surface: float
Rooms: int
Bedrooms: int
Standing: str
Residency: str
Orientation: str
View: str
Condition: str
Floor: int
Lift: int
Pool: int
Garden: int
Parking_Spots: int
Proximity_Tram: int
Proximity_University: int
Proximity_Mosque: int
@app.post("/predict")
async def predict_price(features: PropertyFeatures):
if model is None:
raise HTTPException(status_code=500, detail="Model not loaded")
input_data = pd.DataFrame([features.dict()])
prediction = model.predict(input_data)[0]
return {"estimated_price": round(prediction, 2), "currency": "MAD"}
# Serve Frontend
if os.path.exists("static"):
app.mount("/", StaticFiles(directory="static", html=True), name="static")
if __name__ == "__main__":
# Hugging Face Spaces usually uses port 7860
port = int(os.environ.get("PORT", 8000))
uvicorn.run(app, host="0.0.0.0", port=port)
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