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app.py

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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.
+""")
+
+@st.cache
+
+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)

my_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:044c3279b2bda005ba401e82d9a8b90b9b677f1d5585406c45f962399e36e892
+size 6115