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| import gradio as gr | |
| import pickle | |
| with open(f"./model/model.pkl", "rb") as f: | |
| model = pickle.load(f) | |
| def get_yes_no_question(yes_no): | |
| yes_no_mapping = { | |
| "No" : 0, | |
| "Yes" : 1 | |
| } | |
| return yes_no_mapping.get(yes_no, -1) | |
| def get_gender(gender): | |
| gender_mapping = { | |
| "Female" : 0, | |
| "Male" : 1 | |
| } | |
| return gender_mapping.get(gender, -1) | |
| status = { | |
| 0 : "Dropout", | |
| 1 : "Graduate" | |
| } | |
| def predict_status(_sem_enrolled, _scholarship_holder, _sem_approved, _sem_credited, | |
| _tuition_fees, _sem_evaluations, _gender, _debt): | |
| scholarship_holder = get_yes_no_question(_scholarship_holder) | |
| tuition_fees = get_yes_no_question(_tuition_fees) | |
| gender = get_gender(_gender) | |
| debt = get_yes_no_question(_debt) | |
| data = [[debt, _sem_approved, _sem_evaluations, _sem_credited, | |
| _sem_enrolled, scholarship_holder, | |
| tuition_fees, gender]] | |
| prediction = model.predict(data)[0] | |
| prediction_proba = model.predict_proba(data)[0][prediction] * 100 | |
| if prediction == 0: | |
| return f"The student might {status[prediction]}, model confidence is {prediction_proba:.2f}%" | |
| if prediction == 1: | |
| return f"The student should {status[prediction]}, model confidence is {prediction_proba:.2f}%" | |
| with gr.Blocks(title="Student Status Prediction") as demo: | |
| gr.Markdown(""" | |
| # 🎒 Student Status Prediction | |
| # Dicoding - Solving Educational Institution Problem | |
| ## Made by : Muhammad Hafizh Dzaki | |
| ## Gihub Repo : [Here](https://github.com/hfzdzakii/Dicoding-SolvingEducationIntsituteProblem) | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("### Input Variables") | |
| sem_approved = gr.Number(label="Sum of 2nd Semester Curricular Units Approved:", | |
| value=0, minimum=0, maximum=24) | |
| sem_evaluations = gr.Number(label="Sum of 2nd Semester Curricular Units Evalutions:", | |
| value=0, minimum=0) | |
| sem_credited = gr.Number(label="Sum of 2nd Semester Curricular Units Credited:", | |
| value=0, minimum=0, maximum=24) | |
| sem_enrolled = gr.Number(label="Sum of 2nd Semester Curricular Units Enrolled:", | |
| value=0, minimum=0, maximum=24) | |
| debt = gr.Radio(label="Having Debt?", | |
| choices=["No", "Yes"], value="No") | |
| scholarship_holder = gr.Radio(label="Scholarship Holder?", | |
| choices=["No", "Yes"], value="No") | |
| tuition_fees = gr.Radio(label="Tuition Fees Payed?", | |
| choices=["No", "Yes"], value="No") | |
| gender = gr.Radio(label="Gender:", | |
| choices=["Male", "Female"], value="Male") | |
| with gr.Column(): | |
| gr.Markdown("""### Example Data | |
| Choose one from list below to fill input immediately! | |
| """) | |
| gr.Examples( | |
| examples=[ | |
| [6, "No", 5, 0, "Yes", 13, "Female", "Yes"], | |
| [5, "No", 0, 0, "Yes", 0, "Male", "No"], | |
| [7, "No", 6, 2, "Yes", 10, "Female", "Yes"], | |
| [5, "Yes", 3, 0, "No", 9, "Female", "No"], | |
| [6, "Yes", 6, 0, "No", 6, 'Female', "Yes"], | |
| [6, "Yes", 6, 2, "No", 6, "Female", "No"] | |
| ], | |
| inputs=[sem_enrolled, scholarship_holder, sem_approved, | |
| sem_credited, tuition_fees, sem_evaluations, gender, | |
| debt] | |
| ) | |
| gr.Markdown("### Predict and Result") | |
| predict_button = gr.Button("Predict", variant="primary") | |
| prediction = gr.Textbox(label="Prediction", interactive=False) | |
| predict_button.click( | |
| fn=predict_status, | |
| inputs=[sem_enrolled, scholarship_holder, | |
| sem_approved, | |
| sem_credited, tuition_fees, | |
| sem_evaluations, gender, debt], | |
| outputs=prediction, | |
| ) | |
| demo.launch() |