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('Income_classification_ra.pkl', 'rb'))

# 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 = '18.png'
    st.image(image, use_column_width=True)
    
    st.subheader("Enter the following values:")

    Age = st.number_input("Age")
    Workclass = st.text_input("Workclass")
    Workclass = transform([Workclass])
    Final_weight = st.number_input("Final_weight")
    Education = st.text_input("Education")
    Education=transform([Education])
    Education_Num = st.number_input("Education_Num")
    Marital_status = st.text_input("Marital_status")
    Marital_status=transform([Marital_status])
    Occupation = st.text_input("Occupation")
    Occupation=transform([Occupation])
    Relationship = st.text_input("Relationship")
    Relationship=transform([Relationship])
    Race = st.text_input("Race")
    Race=transform([Race])
    Sex = st.selectbox("Sex",('Male','Female'))
    if Sex == 'Male':
        Sex = 1
    else:
        Sex = 0
    Capital_gain = st.number_input("Capital_gain")
    Capital_loss = st.number_input("Capital_loss")
    Hours_per_week = st.number_input("Hours_per_week")
    Native_country = st.text_input("Native_country")
    Native_country=transform([Native_country])
    
 
    # Create a feature list from the user inputs
    features = [[Age, Workclass, Final_weight, Education, Education_Num, Marital_status, Occupation,Relationship,Race,Sex,Capital_gain,Capital_loss,Hours_per_week,Native_country]]
    
    # Load the model
    model = load_model()
    
    # Make a prediction when the user clicks the "Predict" button
    if st.button('Predict Income'):
        predicted_value = model_prediction(model, features)
        if predicted_value=='1':
           predicted_value='greater then 50000'
        else:
           predicted_value='less then or equal to 50000'
        st.success(f"The Income is: {predicted_value}")       


def about_hidevs():

        components.html("""
        <div>
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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="Income Classification",
        page_icon=":chart_with_upwards_trend:",
        )
    
        st.title("Welcome to our Income Classification App!")
    
        app_design()    
        st.header("About HiDevs Community")
        about_hidevs()

if __name__ == '__main__':
        main()