update

glucose_app.py

@@ -210,7 +210,7 @@ if data_option == "Upload files":
         show_tabs = True
         
 elif data_option == "Sample A":
-    combined_data_path = '../data/processed/samples/sample_A.csv'
+    combined_data_path = 'data/processed/samples/sample_A.csv'
     combined_data = pd.read_csv(combined_data_path)
     st.session_state.combined_data = combined_data
     st.session_state.data_processed = True
@@ -218,7 +218,7 @@ elif data_option == "Sample A":
     show_tabs = True
 
 elif data_option == "Sample B":
-    combined_data_path = '../data/processed/samples/sample_B.csv'
+    combined_data_path = 'data/processed/samples/sample_B.csv'
     combined_data = pd.read_csv(combined_data_path)
     st.session_state.combined_data = combined_data
     st.session_state.data_processed = True
@@ -245,7 +245,7 @@ if show_tabs:
                     # Call naive model prediction functions
                     column_specs = get_column_specs()
                     prepared_data = prepare_data(combined_data, column_specs["timestamp_column"])
-                    train_file = '../data/processed/train_dataset.csv'
+                    train_file = 'data/processed/train_dataset.csv'
                     train_data = pd.read_csv(train_file)
                     train_data = prepare_data(train_data, column_specs["timestamp_column"])
                     predictions = zeroshot_eval(
@@ -315,7 +315,7 @@ if show_tabs:
             if combined_data is not None:
                 X_test, y_test = format_dataset(combined_data, CONTEXT_LENGTH, PREDICTION_LENGTH)
 
-                model_output_path = "../models/xgb_model.pkl"
+                model_output_path = "models/xgb_model.pkl"
                 xgb_model = joblib.load(model_output_path)
 
                 y_test_pred = xgb_model.predict(X_test)
@@ -377,7 +377,7 @@ if show_tabs:
                 column_specs = get_column_specs()
                 prepared_data = prepare_data(combined_data, column_specs["timestamp_column"])
                 
-                train_file = '../data/processed/train_dataset.csv'
+                train_file = 'data/processed/train_dataset.csv'
                 train_data = pd.read_csv(train_file)
                 train_data = prepare_data(train_data, column_specs["timestamp_column"])
                 predictions = zeroshot_eval(