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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. | |
""") | |
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("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) |