Kla007/Student-performance-analysis

1import gradio as gr2import joblib3import numpy as np4import pandas as pd5 6# Load the model and unique brand values7model = joblib.load('model.joblib')8# Assuming unique_values contains the unique values for each categorical variable9unique_values = joblib.load('unique_values.joblib')10 11# Define the prediction function12def predict(age, gender, ethnicity, parental_education, study_time_weekly, absences, tutoring, parental_support, extracurricular, sports, music, volunteering):13    # Convert inputs to appropriate types (if necessary)14    # ... (if any inputs need conversion)15 16    # Prepare the input array for prediction17    input_data = pd.DataFrame({18        'Age': [age],19        'Gender': [gender],20        'Ethnicity': [ethnicity],21        'Parental Education': [parental_education],22        'Study Time Weekly': [study_time_weekly],23        'Absences': [absences],24        'Tutoring': [tutoring],25        'Parental Support': [parental_support],26        'Extracurricular': [extracurricular],27        'Sports': [sports],28        'Music': [music],29        'Volunteering': [volunteering]30    })31 32    # Perform the prediction33    prediction = model.predict(input_data)34 35    return prediction[0]36 37# Create the Gradio interface38interface = gr.Interface(39    fn=predict,40    inputs=[41        gr.Textbox(label="Age"),42        gr.Textbox(label="Gender"),43        gr.Textbox(label="Ethnicity"),44        gr.Textbox(label="Parental Education"),45        gr.Textbox(label="Study Time Weekly"),46        gr.Textbox(label="Absences"),47        gr.Textbox(label="Tutoring"),48        gr.Textbox(label="Parental Support"),49        gr.Textbox(label="Extracurricular"),50        gr.Textbox(label="Sports"),51        gr.Textbox(label="Music"),52        gr.Textbox(label="Volunteering")53    ],54    outputs="text",55    title="Predictor",56    description="Enter the relevant information to predict the target value."57)58 59# Launch the app60interface.launch()