Create app.py

app.py

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+import gradio as gr
+import joblib
+
+# Load your trained model
+model = joblib.load("path_to_your_model.pkl")
+
+# Define prediction function
+def predict_performance(Gender, AttendanceRate, StudyHoursPerWeek, PreviousGrade, ExtracurricularActivities, ParentalSupport):
+    # Encoding Gender (Male/Female)
+    Gender_Male = 1 if Gender == 'Male' else 0
+    Gender_Female = 1 if Gender == 'Female' else 0
+    
+    # One-hot encode ParentalSupport (Low/Medium/High)
+    ParentalSupport_Low = 1 if ParentalSupport == 'Low' else 0
+    ParentalSupport_Medium = 1 if ParentalSupport == 'Medium' else 0
+    ParentalSupport_High = 1 if ParentalSupport == 'High' else 0
+    
+    # ExtracurricularActivities is now numeric (0-3)
+    # No transformation needed, it's a numeric input already
+    
+    # Prepare input array
+    input_data = [
+        [Gender_Male, Gender_Female, AttendanceRate, StudyHoursPerWeek, PreviousGrade, ExtracurricularActivities, 
+         ParentalSupport_Low, ParentalSupport_Medium, ParentalSupport_High]
+    ]
+    
+    # Predict the student's performance
+    prediction = model.predict(input_data)
+    return prediction[0]
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_performance,
+    inputs=[
+        gr.Dropdown(choices=['Male', 'Female'], label='Gender'),
+        gr.Number(label='Attendance Rate (%)'),
+        gr.Number(label='Study Hours Per Week'),
+        gr.Number(label='Previous Grade'),
+        gr.Slider(0, 3, step=1, label='Number of Extracurricular Activities'),  # Updated: Numeric slider (0-3)
+        gr.Dropdown(choices=['Low', 'Medium', 'High'], label='Parental Support')
+    ],
+    outputs="text"
+)
+
+# Launch the interface
+interface.launch()