inference.py

import pickle
import pandas as pd
import json

def load_model():
    try:
        with open("model/expense_forecaster_model.pkl", "rb") as f:
            model = pickle.load(f)
        return model
    except Exception as e:
        print(f"Error loading model: {e}")
        return None

def predict(data):
    model = load_model()
    if model is None:
        return {"error": "Model loading failed"}

    try:
        # Ensure data is a dictionary
        if not isinstance(data, dict):
            return {"error": "Input data must be a dictionary"}

        df = pd.DataFrame([data])
        prediction = model.predict(df)
        return prediction.tolist()

    except Exception as e:
        return {"error": f"Prediction error: {e}"}

if __name__ == "__main__":
    example_input = {"income": 5000, "previous_expenses": 3000, "month": 12}
    prediction = predict(example_input)
    print(f"Prediction: {prediction}")