Update inference.py

inference.py

@@ -1,21 +1,21 @@
-import pandas as pd
-import pickle
-from datetime import datetime
-
-def predict_expense(input_date_str):
-    """Predicts the expense for the given date."""
-
-    # Load the model
-    with open("expense_forecaster_model.pkl", "rb") as model_file:
-        model = pickle.load(model_file)
-
-    # Preprocess input (convert date to numerical feature)
-    input_date = pd.to_datetime(input_date_str)
-    # Use the minimum date from your training data (2024-01-01) as a reference point
-    min_date = pd.to_datetime("2024-01-01")
-    numerical_date = (input_date - min_date) / pd.Timedelta(days=30)
-
-    # Make prediction
-    prediction = model.predict(pd.DataFrame({"ds": [numerical_date]}))
-
+import pandas as pd
+import pickle
+from datetime import datetime
+
+def predict_expense(input_date_str):
+    """Predicts the expense for the given date."""
+
+    # Load the model
+    with open("model/expense_forecaster_model.pkl", "rb") as model_file:
+        model = pickle.load(model_file)
+
+    # Preprocess input (convert date to numerical feature)
+    input_date = pd.to_datetime(input_date_str)
+    # Use the minimum date from your training data (2024-01-01) as a reference point
+    min_date = pd.to_datetime("2024-01-01")
+    numerical_date = (input_date - min_date) / pd.Timedelta(days=30)
+
+    # Make prediction
+    prediction = model.predict(pd.DataFrame({"ds": [numerical_date]}))
+
     return prediction[0]