expense_model / inference.py
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import pickle
import numpy as np
from fastapi import FastAPI
from pydantic import BaseModel
# Load the model (ensure this path matches the location of your model)
with open("expense_model.pkl", "rb") as model_file:
model = pickle.load(model_file)
# Initialize the FastAPI app
app = FastAPI()
class ForecastRequest(BaseModel):
month: float # The input feature (number of months)
class ForecastResponse(BaseModel):
predicted_expense: float
@app.post("/predict", response_model=ForecastResponse)
async def predict_expense(request: ForecastRequest):
# Predict the expense for the given month
predicted_expense = model.predict(np.array([[request.month]]))[0]
return ForecastResponse(predicted_expense=predicted_expense)