Create app.py

app.py

@@ -0,0 +1,85 @@
+import gradio as gr
+import pickle
+import pandas as pd
+
+# Load model
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Define prediction function
+def predict_jamb(study_hours, attendance, teacher_quality, distance,
+                 school_type, school_location, extra_tutorials, learning_materials,
+                 parent_involvement, it_knowledge, age, gender, socioeconomic_status,
+                 parent_education, assignments_completed):
+
+    # Create dataframe from inputs
+    input_data = pd.DataFrame({
+        "Study_Hours_Per_Week": [study_hours],
+        "Attendance_Rate": [attendance],
+        "Teacher_Quality": [teacher_quality],
+        "Distance_To_School": [distance],
+        "School_Type": [school_type],
+        "School_Location": [school_location],
+        "Extra_Tutorials": [extra_tutorials],
+        "Access_To_Learning_Materials": [learning_materials],
+        "Parent_Involvement": [parent_involvement],
+        "IT_Knowledge": [it_knowledge],
+        "Age": [age],
+        "Gender": [gender],
+        "Socioeconomic_Status": [socioeconomic_status],
+        "Parent_Education_Level": [parent_education],
+        "Assignments_Completed": [assignments_completed]
+    })
+
+    prediction = model.predict(input_data)[0]
+
+    if prediction == 0:
+        return "📉 Below Average"
+    elif prediction == 1:
+        return "⚖️ Average"
+    else:
+        return "✅ Pass"
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎓 JAMB Score Prediction App")
+    gr.Markdown("Predict whether a student's JAMB score will be **Below Average**, **Average**, or **Pass**.")
+
+    with gr.Row():
+        study_hours = gr.Slider(0, 40, value=10, label="Study Hours per Week")
+        attendance = gr.Slider(0, 100, value=80, label="Attendance Rate (%)")
+        teacher_quality = gr.Dropdown([1,2,3,4,5], label="Teacher Quality")
+        distance = gr.Number(value=5, label="Distance to School (km)")
+
+    with gr.Row():
+        school_type = gr.Radio(["Public", "Private"], label="School Type")
+        school_location = gr.Radio(["Urban", "Rural"], label="School Location")
+        extra_tutorials = gr.Radio(["Yes", "No"], label="Extra Tutorials")
+        learning_materials = gr.Radio(["Yes", "No"], label="Access to Learning Materials")
+
+    with gr.Row():
+        parent_involvement = gr.Dropdown(["Low", "Medium", "High"], label="Parent Involvement")
+        it_knowledge = gr.Dropdown(["Low", "Medium", "High"], label="IT Knowledge")
+        age = gr.Slider(10, 30, value=18, label="Age")
+        gender = gr.Radio(["Male", "Female"], label="Gender")
+
+    with gr.Row():
+        socioeconomic_status = gr.Dropdown(["Low", "Medium", "High"], label="Socioeconomic Status")
+        parent_education = gr.Dropdown(["None", "Primary", "Secondary", "Tertiary"], label="Parent Education")
+        assignments_completed = gr.Slider(0, 10, value=2, label="Assignments Completed")
+
+    output = gr.Textbox(label="Prediction")
+
+    submit_btn = gr.Button("Predict")
+    submit_btn.click(
+        predict_jamb,
+        inputs=[study_hours, attendance, teacher_quality, distance,
+                school_type, school_location, extra_tutorials, learning_materials,
+                parent_involvement, it_knowledge, age, gender, socioeconomic_status,
+                parent_education, assignments_completed],
+        outputs=output
+    )
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()