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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)
+