| import streamlit as st
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| import pandas as pd
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| import numpy as np
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| import joblib
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|
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|
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| model = joblib.load('student_performance_model.h5')
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|
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| def predict_marks(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced):
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| input_data = np.array([[Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced]])
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| prediction = model.predict(input_data)
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| prediction = round(float(prediction), 2)
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|
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| if prediction >= 100:
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| prediction = 100
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|
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| return prediction
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| def main():
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| st.title("Student Performance Prediction")
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| name = st.text_input("Enter your name:--")
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| Hours_Studied = st.number_input("Enter Number of hours you daily study:--",max_value=12,min_value=0,value=0)
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| Previous_Scores = st.number_input("Enter your previous scores:--",max_value=100.0,min_value=0.0,value=0.0)
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| Extracurricular_Activities = st.number_input("Enter the number of extracurricular activities you participate in:--",max_value=10,min_value=0,value=0)
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| Sleep_Hours= st.number_input("Enter the number of hours you sleep daily:--",max_value=12,min_value=0,value=0)
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| Sample_Question_Papers_Practiced= st.number_input("Enter the number of sample question papers you practice:--",max_value=100,min_value=0,value=0)
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|
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| st.sidebar.title("Prediction")
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| st.sidebar.write(f"Hey, {name}")
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|
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| if st.button("Result"):
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| prediction = predict_marks(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced)
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| if prediction > 90:
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| st.success(f"Your predicted grade is A you are on a correct path with the estimated score of {prediction}.")
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| st.balloons()
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| elif prediction > 35:
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| st.warning(f"Your predicted grade is B you need to impove your estimated score is {prediction}.")
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| else:
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| st.error(f"Work hard your estimated score is {prediction}.")
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|
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| if __name__ == "__main__":
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| main()
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|
