import streamlit as st
import pandas as pd
import joblib
import sklearn
import transformers

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

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

@st.cache
def fetch_data():
    df = pd.read_csv('https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/campus.csv')
    return df

df = fetch_data()
st.write(df)

st.sidebar.header('User Input Features')

def user_input():
    gender = st.sidebar.selectbox('Gender', df['gender'].unique())
    ssc = st.sidebar.number_input('Secondary School Points', value=67.00)
    hsc = st.sidebar.number_input('High School Points', 0.0, value=91.0)
    hsc_s = st.sidebar.selectbox('High School Spec', df['hsc_s'].unique())
    degree_p = st.sidebar.number_input('Degree Points', 0.0, value=58.0)
    degree_t = st.sidebar.selectbox('Degree Spec', df['degree_t'].unique())
    workex = st.sidebar.selectbox('Work Experience?', df['workex'].unique())
    etest_p = st.sidebar.number_input('Etest Points', 0.0, value=78.00)
    spec = st.sidebar.selectbox('Specialization', df['specialisation'].unique())
    mba_p = st.sidebar.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
    }
    features = pd.DataFrame(data, index=[0])
    return features


input = user_input()

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

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

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)