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('Employee_Turnover_prognosis_RandomForest.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 = '14.png'
    st.image(image, use_column_width=True)
    
    st.subheader("Enter the following values:")

    
    Stag = st.number_input("Stag")
    Gender = st.selectbox('Gender',('Male','Female'))
    if Gender == 'Male':
          Gender = 1
    elif Gender == 'Female':
          Gender = 0      
    Age = st.number_input("Age")
    Industry = st.text_input("Industry")
    Industry=transform([Industry])
    Profession = st.text_input("Profession")
    Profession=transform([Profession])
    Traffic = st.text_input("Traffic")
    Traffic=transform([Traffic])
    Coach = st.selectbox('Coach',('Yes','No'))
    if Coach == 'Yes':
          Coach = 1
    elif Coach == 'No':
          Coach = 0 
    Head_gender = st.selectbox("Head Gender",('Male','Female'))
    if Head_gender == 'Male':
          Head_gender = 0
    elif Head_gender == 'Female':
          Head_gender = 1  
    Greywage = st.text_input("Greywage")
    Greywage=transform([Greywage])
    Way = st.text_input("Way")
    Way=transform([Way])
    Extraversion = st.number_input("Extraversion")
    Independ = st.number_input("Independ")
    Selfcontrol = st.number_input("Self-control")
    Anxiety = st.number_input("Anxiety")
    Novator = st.number_input("Novator")
    
 
    # Create a feature list from the user inputs
    features = [[Stag,Gender,Age,Industry,Profession,Traffic,Coach,Head_gender,Greywage,Way,Extraversion,Independ,Selfcontrol,Anxiety,Novator]]
    
    # Load the model
    model = load_model()
    
    # Make a prediction when the user clicks the "Predict" button
    if st.button('Predict Turnover'):
        predicted_value = model_prediction(model, features)
        st.success(f"The Employee Turnover 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="Employee Turnover Prediction",
        page_icon=":chart_with_upwards_trend:",
        )
    
        st.title("Welcome to our Employee Turnover Prediction App!")
    
        app_design()    
        st.header("About HiDevs Community")
        about_hidevs()

if __name__ == '__main__':
        main()