commit

app.py

@@ -1,4 +1,65 @@
 import streamlit as st
+import pandas as pd
+import joblib
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+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")
+
+if st.button("Predict"):
+    prediction = load_model.predict(input)
+
+    if prediction == 1:
+        prediction = 'Placed'
+    else:
+        prediction = 'Not Placed'
+
+    st.subheader('Based on user input, the placement model predicted: ')
+    st.header(prediction)