| import gradio as gr |
| import pandas as pd |
| import joblib |
|
|
| |
| model = joblib.load("student_gpa_model.pkl") |
| columns = joblib.load("columns.pkl") |
|
|
| def predict_gpa( |
| Age, |
| Gender, |
| Ethnicity, |
| ParentalEducation, |
| StudyTimeWeekly, |
| Absences, |
| Tutoring, |
| ParentalSupport, |
| Extracurricular, |
| Sports, |
| Music, |
| Volunteering |
| ): |
| data = { |
| "Age": Age, |
| "Gender": Gender, |
| "Ethnicity": Ethnicity, |
| "ParentalEducation": ParentalEducation, |
| "StudyTimeWeekly": StudyTimeWeekly, |
| "Absences": Absences, |
| "Tutoring": Tutoring, |
| "ParentalSupport": ParentalSupport, |
| "Extracurricular": Extracurricular, |
| "Sports": Sports, |
| "Music": Music, |
| "Volunteering": Volunteering, |
| } |
|
|
| df = pd.DataFrame([data]) |
| df = pd.get_dummies(df) |
| df = df.reindex(columns=columns, fill_value=0) |
|
|
| prediction = model.predict(df)[0] |
| return round(float(prediction), 2) |
|
|
| app = gr.Interface( |
| fn=predict_gpa, |
| inputs=[ |
| gr.Number(label="Age"), |
| gr.Dropdown(["Male", "Female"], label="Gender"), |
| gr.Dropdown( |
| ["Group A", "Group B", "Group C", "Group D", "Group E"], |
| label="Ethnicity" |
| ), |
| gr.Dropdown( |
| ["High School", "Associate", "Bachelor", "Master"], |
| label="Parental Education" |
| ), |
| gr.Number(label="Weekly Study Time (hours)"), |
| gr.Number(label="Absences"), |
| gr.Dropdown(["Yes", "No"], label="Tutoring"), |
| gr.Dropdown(["Low", "Medium", "High"], label="Parental Support"), |
| gr.Dropdown(["Yes", "No"], label="Extracurricular"), |
| gr.Dropdown(["Yes", "No"], label="Sports"), |
| gr.Dropdown(["Yes", "No"], label="Music"), |
| gr.Dropdown(["Yes", "No"], label="Volunteering"), |
| ], |
| outputs=gr.Number(label="Predicted GPA"), |
| title="Student GPA Predictor", |
| description="ML model to predict student GPA based on academic & lifestyle factors" |
| ) |
|
|
| if __name__ == "__main__": |
| app.launch(server_name="0.0.0.0", server_port=7860) |
|
|
