app.py

import streamlit as st
import numpy as np
import pickle
import streamlit.components.v1 as components
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
# Load the pickled model
def load_model():
    return pickle.load(open('Student_Marks_LinearRegresssionnew.pkl', 'rb')) #change

# Function for model prediction
def model_prediction(model, features):
    predicted = str(model.predict(features)[0])
    return predicted

def transform(text):
    text = le.fit_transform(text)
    return text[0]

def app_design():
    # Add input fields for High, Open, and Low values
    image = '37.png' #change
    st.image(image, use_column_width=True)
    
    st.subheader("Enter the following values:") #change
    
    school= st.number_input("school")
    school = le.fit_transform([school])
    sex= st.number_input("sex")
    sex = le.fit_transform([sex])
    age= st.number_input("age")
    age = le.fit_transform([age])
    famsize= st.number_input("famsize")
    famsize = le.fit_transform([famsize])
    Pstatus= st.number_input("Pstatus")
    Pstatus = le.fit_transform([Pstatus])
    Medu= st.number_input("Medu")
    Fedu= st.number_input("Fedu")
    Mjob= st.number_input("Mjob")
    Mjob = le.fit_transform(Mjob)
    Fjob= st.number_input("Fjob")
    Fjob = le.fit_transform([Fjob])
    reason = st.number_input("reason")
    reason = le.fit_transform([reason])
    guardian= st.number_input("guardian")
    guardian = le.fit_transform([guardian])
    traveltime= st.number_input("traveltime")
    studytime= st.number_input("studytime")
    failures= st.number_input("failures")
    schoolsup= st.number_input("schoolsup")
    schoolsup = le.fit_transform([schoolsup])
    famsup= st.number_input("famsup")
    famsup = le.fit_transform([famsup])
    paid= st.number_input("paid")
    paid = le.fit_transform([paid])
    activities= st.number_input("activities")
    activities = le.fit_transform([activities])
    nursery= st.number_input("nursery")
    nursery = le.fit_transform([nursery])
    higher= st.number_input("higher")
    higher = le.fit_transform([higher])
    internet= st.number_input("internet")
    internet = le.fit_transform([internet])
    famrel = le.fit_transform([famrel])
    freetime= st.number_input("freetime")
    goout= st.number_input("goout")
    Dalc= st.number_input("Dalc")
    Walc= st.number_input("Walc")
    health= st.number_input("health")
    absences= st.number_input("absences")
    G1= st.number_input("G1")
    G2= st.number_input("G2")
    
    
 # Create a feature list from the user inputs
    features = [[school,sex,age,famsize,Pstatus,Medu,Fedu,Mjob,Fjob,reason,guardian,traveltime,studytime,failures,schoolsup,famsup,paid,activities,nursery,higher,internet,famrel,freetime,goout,Dalc,Walc,health,absences,G1,G2]]
    
    # Load the model
    model = load_model()
    
    # Make a prediction when the user clicks the "Predict" button
    if st.button('Predict Grade'):
        predicted_value = model_prediction(model, features)
      
        st.success(f"Student Grade is: {predicted_value}")       


def about_hidevs():

        components.html("""
        <div>
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        <p class="subtitle">💡 Join us now, and turbocharge your career!</p>
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        <a class="link" href="https://www.instagram.com/hidevs_community/" target="__blank">Instagram</a>
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      </div>
        """,
                    height=600)

def main():

        # Set the app title and add your website name and logo
        st.set_page_config(
        page_title="Student Gade Prediction",
        page_icon=":chart_with_upwards_trend:",
        )
    
        st.title("Welcome to our Student Grade Prediction App!")
    
        app_design()    
        st.header("About HiDevs Community")
        about_hidevs()

if __name__ == '__main__':
        main()