Update app.py

app.py

@@ -3,7 +3,7 @@ import gradio as gr
 import numpy as np
 
 # Load the saved Random Forest model
-model = joblib.load('best_model.pkl')
+model = joblib.load('random_forest_model.pkl')
 
 # Define the feature names (as per your dataset)
 feature_names = [
@@ -17,9 +17,25 @@ feature_names = [
 ]
 
 # Define the prediction function
-def predict(*args):
+def predict(
+    marital_status, application_mode, application_order, course,
+    attendance, previous_qualification, nationality,
+    mother_qualification, mother_occupation, displaced,
+    special_needs, debtor, tuition_fees,
+    gender, scholarship_holder, curricular_units_credited,
+    curricular_units_without_evaluations, unemployment_rate,
+    inflation_rate, gdp
+):
     # Create a numpy array from the input features
-    input_data = np.array(args).reshape(1, -1)
+    input_data = np.array([
+        marital_status, application_mode, application_order, course,
+        attendance, previous_qualification, nationality,
+        mother_qualification, mother_occupation, displaced,
+        special_needs, debtor, tuition_fees,
+        gender, scholarship_holder, curricular_units_credited,
+        curricular_units_without_evaluations, unemployment_rate,
+        inflation_rate, gdp
+    ]).reshape(1, -1)
 
     # Make a prediction
     prediction = model.predict(input_data)[0]
@@ -35,7 +51,29 @@ def predict(*args):
         return "Unknown"
 
 # Create a Gradio interface
-inputs = [gr.inputs.Number(label=name) for name in feature_names]
+inputs = [
+    gr.inputs.Number(label="Marital status"),
+    gr.inputs.Number(label="Application mode"),
+    gr.inputs.Number(label="Application order"),
+    gr.inputs.Number(label="Course"),
+    gr.inputs.Number(label="Daytime/evening attendance"),
+    gr.inputs.Number(label="Previous qualification"),
+    gr.inputs.Number(label="Nacionality"),
+    gr.inputs.Number(label="Mother's qualification"),
+    gr.inputs.Number(label="Mother's occupation"),
+    gr.inputs.Number(label="Displaced"),
+    gr.inputs.Number(label="Educational special needs"),
+    gr.inputs.Number(label="Debtor"),
+    gr.inputs.Number(label="Tuition fees up to date"),
+    gr.inputs.Number(label="Gender"),
+    gr.inputs.Number(label="Scholarship holder"),
+    gr.inputs.Number(label="Curricular units 1st sem (credited)"),
+    gr.inputs.Number(label="Curricular units 1st sem (without evaluations)"),
+    gr.inputs.Number(label="Unemployment rate"),
+    gr.inputs.Number(label="Inflation rate"),
+    gr.inputs.Number(label="GDP"),
+]
+
 outputs = gr.outputs.Textbox(label="Prediction")
 
 # Launch the Gradio app