Update app.py

app.py

@@ -1,65 +1,66 @@
-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
-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: ')
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+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("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)