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# Hours Studied Previous Scores Extracurricular Activities Sleep Hours Sample Question Papers Practiced Performance Index\
import numpy as np
import joblib
import streamlit as st
# Load the trained model
model = joblib.load("student_performance_model.h5")
def predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question):
"predict the student marks based on the input data"
input_data = np.array([[Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question]])
prediction= model.predict(input_data)
return round(float(prediction),2)
def main():
st.title("Student Marks Predictor")
#Input data
name = st.text_input("Enter your name")
Hours_studied = st.number_input("Enter the number of Hours you Studied", min_value=0.0,max_value=20.0,value=0.0)
Previous_Score = st.number_input("Enter your Previous exam Score", min_value=0,max_value=100,value=0)
Extracurriculum_Activivities = st.number_input("Enter the number extracurriculum activities you have done",min_value=0,max_value=10,value=0)
Sleep_Hours = st.number_input("Enter the number of hours you slept",min_value=0.0,max_value=12.0,value=0.0)
Sample_Question = st.number_input("Enter the number of Sample Question you have practiced",min_value=0,max_value=50,value=0)
# predict
st.sidebar.write(f"# hi {name}")
st.sidebar.write("##### i am a helpful students marks predictor here to assist you in predicting your marks")
if st.button("Predict"):
prediction = predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question)
# Display the predictions
if prediction >=90:
st.success(f"{name} You have a high chances of passing with the the exceptional marks of {prediction} marks keep it up")
elif prediction >=35:
st.success(f"{name} You have chances of Passing with {prediction} marks try to get 90+")
else:
st.error(f"{name} You have a very high chances of failing")
if __name__=="__main__":
main()
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