Update inference.py

inference.py

@@ -1,18 +1,18 @@
-import sklearn  # Explicit import
-   import pandas as pd
-   import pickle
-   from datetime import datetime
+import pickle
+import pandas as pd
 
-   class ExpenseForecaster:
-       def __init__(self, model_path="model/expense_forecaster_model.pkl"):
-           with open(model_path, "rb") as model_file:
-               self.model = pickle.load(model_file)
-           self.min_date = pd.to_datetime("2024-01-01")  # Reference date
+def load_model():
+    with open("model/expense_forecaster_model.pkl", "rb") as f:
+        model = pickle.load(f)
+    return model
 
-       def __call__(self, input_date_str):
-           input_date = pd.to_datetime(input_date_str)
-           numerical_date = (input_date - self.min_date) / pd.Timedelta(days=30)
-           prediction = self.model.predict(pd.DataFrame({"ds": [numerical_date]}))
-           return prediction[0].tolist()  # Return as a list
+def predict(data):
+    model = load_model()
+    df = pd.DataFrame([data])
+    prediction = model.predict(df)
+    return prediction.tolist()
 
-   model = ExpenseForecaster() # Instantiate the model
+if __name__ == "__main__":
+    example_input = {"income": 5000, "previous_expenses": 3000, "month": 12} #example data, change this to match your feature names.
+    prediction = predict(example_input)
+    print(f"Prediction: {prediction}")