Upload app.py

app.py

@@ -0,0 +1,75 @@
+import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+import pandas as pd
+import numpy as np
+import pickle
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+st.set_page_config(page_title = 'Customer Churn Prediction',
+                  initial_sidebar_state = "expanded",
+                  menu_items = {
+                      'About' : 'Milestone 1 Customer Churn Predicton '
+                  })
+
+image = Image.open('indihome.jpg')
+
+# load model
+class columnDropperTransformer():
+    def __init__(self, columns):
+        self.columns = columns
+
+    def transform(self, X, y=None):
+        return X.drop(self.columns, axis=1)
+
+    def fit(self, X, y=None):
+        return self
+
+pickles = open('preprocessings.pkl', 'rb')
+preprocessing = pickle.load(pickles)
+saved_model=load_model('Model.h5')
+
+def predict(inputs):
+    df = pd.DataFrame(inputs, index=[0])
+    df = preprocessing.transform(df)
+    y_pred = saved_model.predict(df)
+    y_pred = np.where(y_pred < 0.5, 0, 1).squeeze()
+    print(y_pred)
+    return y_pred.item()
+
+columns = ['SeniorCitizen', 'Partner', 'tenure', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 
+'DeviceProtection', 'TechSupport', 'Contract', 'MonthlyCharges', 'TotalCharges']
+label = ['Not Churn', 'Churn']
+
+st.title("Customer Churn Prediction")
+st.image(image)
+
+
+SeniorCitizen = st.selectbox("Senior Citizen", ['Yes', 'No'])
+Partner = st.selectbox("Marriage Status", ['Married', 'Not Married'])
+tenure = st.slider("Tenure Length", min_value=0.0, max_value=72.0, value=24.0, step=1.0, help='Tenure Length Default 24 Months')
+MultipleLines = st.selectbox("Multiple Lines", ['Yes', 'No'])
+InternetService = st.selectbox("Which internet service do you use?", ['DSL', 'Fiber optic', 'No'])
+OnlineSecurity = st.selectbox("Do you have online security?", ['No', 'Yes', 'No internet service'])
+OnlineBackup = st.selectbox("Do you have online backup?", ['No', 'Yes', 'No internet service'])
+DeviceProtection = st.selectbox("Do you have device protection?", ['No', 'Yes', 'No internet service'])
+TechSupport = st.selectbox("Do you have Tech Support?", ['No', 'Yes', 'No internet service'])
+Contract = st.selectbox("Which contract do you use?", ['Month-to-month', 'One year', 'Two year'])
+MonthlyCharges = st.number_input("Monthly Charges", min_value=19.0, max_value=119.0, value=75.0, step=0.1, help='Customers Monthly Charges Default is $75')
+TotalCharges = st.number_input("Total Charges", min_value=19.0, max_value=8685.0, value=500.0, step=0.1, help='Customers Total Charges Default is $500')
+
+#inference
+new_data = [SeniorCitizen, Partner, tenure, 
+MultipleLines, InternetService, OnlineSecurity, OnlineBackup, 
+DeviceProtection, TechSupport, 
+Contract, MonthlyCharges, TotalCharges]
+new_data = pd.DataFrame([new_data], columns = columns)
+new_data = preprocessing.transform(new_data).tolist()
+res = saved_model.predict(new_data)
+
+res = 0 if res < 0.5 else 1
+
+press = st.button('Predict')
+if press:
+   st.title(label[res])