Update app.py

app.py

@@ -1,10 +1,17 @@
-import gradio as gr
+import os
 import joblib
 import numpy as np
-import os
+import gradio as gr
+from pathlib import Path
+import sys
 
-# Get the current file directory
-current_dir = os.path.dirname(os.path.abspath(__file__))
+# Get the current directory of the script or fallback to the current working directory
+if hasattr(sys, 'frozen'):
+    current_dir = Path(sys.executable).parent
+elif '__file__' in globals():
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+else:
+    current_dir = Path().absolute()
 
 # Load the trained model from the same directory
 model_path = os.path.join(current_dir, "trained_model.joblib")
@@ -45,10 +52,13 @@ input_labels = ["CSC101_total", "CSC201_total", "CSC203_total", "CSC205_total",
                 "CNE304_total", "CSC301_total", "CNE302_total", "CSC309_total", "CSC302_total", 
                 "CSC303_total", "CNE308_total"]
 
-inputs = [gr.inputs.Number(label=label) for label in input_labels]
-output = gr.outputs.Textbox(label="Predicted Department")
+# Create a list of number inputs corresponding to the input labels
+inputs = [gr.Number(label=label) for label in input_labels]
+
+# Define the output as a text box that will show the predicted department
+output = gr.Textbox(label="Predicted Department")
 
-# Create the Gradio app
+# Create the Gradio app interface
 app = gr.Interface(fn=predict_department, inputs=inputs, outputs=output, title="Department Predictor")
 
 # Launch the app