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import streamlit as st |
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import pandas as pd |
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import joblib |
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st.header('FTDS Model Deployment') |
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st.write(""" |
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Created by FTDS Curriculum Team |
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Use the sidebar to select input features. |
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""") |
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df = pd.read_csv('https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/campus.csv') |
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gender = st.selectbox('Gender', df['gender'].unique()) |
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ssc = st.number_input('Secondary School Points', value=67.00) |
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hsc = st.number_input('High School Points', 0.0, value=91.0) |
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hsc_s = st.selectbox('High School Spec', df['hsc_s'].unique()) |
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degree_p = st.number_input('Degree Points', 0.0, value=58.0) |
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degree_t = st.selectbox('Degree Spec', df['degree_t'].unique()) |
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workex = st.selectbox('Work Experience?', df['workex'].unique()) |
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etest_p = st.number_input('Etest Points', 0.0, value=78.00) |
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spec = st.selectbox('Specialization', df['specialisation'].unique()) |
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mba_p = st.number_input('MBA Points', 0.0, value=54.55) |
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data = { |
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'gender': gender, |
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'ssc_p': ssc, |
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'hsc_p': hsc, |
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'hsc_s': hsc_s, |
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'degree_p': degree_p, |
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'degree_t': degree_t, |
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'workex': workex, |
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'etest_p': etest_p, |
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'specialisation':spec, |
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'mba_p': mba_p |
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} |
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input = pd.DataFrame(data, index=[0]) |
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st.subheader('User Input') |
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st.write(input) |
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load_model = joblib.load("my_model.pkl") |
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if st.button('Predict'): |
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prediction = load_model.predict(input) |
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if prediction == 1: |
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prediction = 'Placed' |
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else: |
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prediction = 'Not Placed' |
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st.write('Based on user input, the placement model predicted: ') |
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st.write(prediction) |