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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import pandas as pd
import numpy as np
app = FastAPI(title="Airbnb Price Prediction in Copenhagen")
# Load model and preprocessing objects
model_xgb = joblib.load('model_xgb.joblib')
scaler = joblib.load('scaler.joblib')
ohe = joblib.load('ohe.joblib')
class RoomFeatures(BaseModel):
neighbourhood_cleansed: str
room_type: str
instant_bookable: bool
accommodates: int
bedrooms: int
beds: int
minimum_nights_avg_ntm: int
@app.post("/predict")
async def predict_price(features: RoomFeatures):
try:
# Prepare categorical features
cat_features = pd.DataFrame({
'neighbourhood_cleansed': [features.neighbourhood_cleansed],
'room_type': [features.room_type]
})
cat_encoded = pd.DataFrame(
ohe.transform(cat_features).todense(),
columns=ohe.get_feature_names_out(['neighbourhood_cleansed', 'room_type'])
)
# Prepare numerical features
num_features = pd.DataFrame({
'instant_bookable': [int(features.instant_bookable)],
'accommodates': [features.accommodates],
'bedrooms': [features.bedrooms],
'beds': [features.beds],
'minimum_nights_avg_ntm': [features.minimum_nights_avg_ntm]
})
num_scaled = pd.DataFrame(scaler.transform(num_features), columns=num_features.columns)
# Combine features
combined_features = pd.concat([num_scaled, cat_encoded], axis=1)
# Make prediction
predicted_price = model_xgb.predict(combined_features)[0]
# Calculate price range
lower_range = max(0, round(predicted_price - 350))
upper_range = round(predicted_price + 350)
return {
"predicted_price": round(predicted_price),
"suggested_price_range": {
"lower": lower_range,
"upper": upper_range
}
}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
@app.get("/")
async def root():
return {"message": "Welcome to the Airbnb Price Prediction API for Copenhagen"}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |