import streamlit as st
import pandas as pd
import joblib

st.header('FTDS Model Deployment')
st.write("""
Created by FTDS Curriculum Team

Use the sidebar to select input features.
""")



df = pd.read_csv('https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/campus.csv')

gender = st.selectbox('Gender', df['gender'].unique())
ssc = st.number_input('Secondary School Points', value=67.00)
hsc = st.number_input('High School Points', 0.0, value=91.0)
hsc_s = st.selectbox('High School Spec', df['hsc_s'].unique())
degree_p = st.number_input('Degree Points', 0.0, value=58.0)
degree_t = st.selectbox('Degree Spec', df['degree_t'].unique())
workex = st.selectbox('Work Experience?', df['workex'].unique())
etest_p = st.number_input('Etest Points', 0.0, value=78.00)
spec = st.selectbox('Specialization', df['specialisation'].unique())
mba_p = st.number_input('MBA Points', 0.0, value=54.55)

data = {
    'gender': gender,
    'ssc_p': ssc,
    'hsc_p': hsc,
    'hsc_s': hsc_s,
    'degree_p': degree_p,
    'degree_t': degree_t,
    'workex': workex,
    'etest_p': etest_p,
    'specialisation':spec,
    'mba_p': mba_p
}
input = pd.DataFrame(data, index=[0])

st.subheader('User Input')
st.write(input)

load_model = joblib.load("my_model.pkl")

if st.button('Predict'):
    prediction = load_model.predict(input)

    if prediction == 1:
        prediction = 'Placed'
    else:
        prediction = 'Not Placed'

    st.write('Based on user input, the placement model predicted: ')
    st.write(prediction)