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