Create app.py

app.py

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+import joblib
+import gradio as gr
+import numpy as np
+
+# Load the saved Random Forest model
+model = joblib.load('random_forest_model.pkl')
+
+# Define the feature names (as per your dataset)
+feature_names = [
+    'Marital status', 'Application mode', 'Application order', 'Course',
+    'Daytime/evening attendance', 'Previous qualification', 'Nacionality',
+    "Mother's qualification", "Mother's occupation", 'Displaced',
+    'Educational special needs', 'Debtor', 'Tuition fees up to date',
+    'Gender', 'Scholarship holder', 'Curricular units 1st sem (credited)',
+    'Curricular units 1st sem (without evaluations)', 'Unemployment rate',
+    'Inflation rate', 'GDP'
+]
+
+# Define the prediction function
+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([
+        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]
+
+    # Map the prediction to the corresponding label
+    if prediction == 0:
+        return "Dropout"
+    elif prediction == 1:
+        return "Graduate"
+    elif prediction == 2:
+        return "Enrolled"
+    else:
+        return "Unknown"
+
+# Create a Gradio interface
+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
+gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title="Student Dropout Prediction").launch()