Create app.py

app.py

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+import streamlit as st
+import hopsworks
+import plotly.graph_objs as go
+import plotly.express as px
+import joblib
+import math
+import pandas as pd
+import os
+
+st.title('🔮 Customer Churn Prediction')
+
+st.write(36 * "-")
+st.header('\n📡 Connecting to Hopsworks Feature Store...')
+
+
+def header(text):
+    st.write(36 * "-")
+    st.write('#### ' + text)
+
+project = hopsworks.login(project = "annikaij", api_key_value=os.environ['HOPSWORKS_API_KEY'])
+fs = project.get_feature_store()
+
+header('🪄 Retrieving Feature View...')
+
+feature_view = fs.get_feature_view(
+    name="churn_feature_view",
+    version=1
+    )
+
+st.text('Done ✅')
+header('⚙️ Reading DataFrames from Feature View...')
+
+@st.cache_data()
+def retrive_data(feature_view=feature_view):
+    feature_view.init_batch_scoring(1)
+    batch_data = feature_view.get_batch_data()
+    batch_data.drop('customerid', axis=1, inplace=True)
+    df_all = feature_view.query.read()
+    df_all.drop('churn', axis=1, inplace=True)
+    return batch_data, df_all
+
+
+batch_data, df_all = retrive_data()
+
+st.dataframe(df_all.head())
+st.text(f'Shape: {df_all.shape}')
+header('🔮 Model Retrieving...')
+
+
+@st.cache_data()
+def get_model(project=project):
+    mr = project.get_model_registry()
+    model = mr.get_model("churnmodel", version=1)
+    model_dir = model.download()
+    return joblib.load(model_dir + "/churnmodel.pkl")
+
+
+model = get_model()
+st.write(model)
+
+
+def transform_preds(predictions):
+    return ['Churn' if pred == 1 else 'Not Churn' for pred in predictions]
+
+
+header('📝 Batch Data Prediction...')
+
+st.dataframe(batch_data.head())
+
+predictions = model.predict(batch_data)
+predictions = transform_preds(predictions)
+
+df_all['Churn'] = predictions
+
+result_table = df_all[['customerid', 'Churn']]
+
+st.text(f'👩🏻‍⚖️ Predictions for 5 rows:\n {predictions[:5]}')
+header('💳 Prediction by Customer Id...')
+
+with st.form(key="Selecting Customer ID"):
+    option = st.selectbox(
+             'Select a Custimer ID to return a predict.',
+             (result_table.customerid.values[:15])
+          )
+    submit_button = st.form_submit_button(label='Submit')
+
+if submit_button:   
+    result = result_table[result_table.customerid == option]['Churn'].values
+
+    st.text(f'👮🏻‍♂️ Customer ID: {option}')
+    st.text(f'👩🏻‍⚖️ Prediction: {result}')
+
+header('👨🏻‍🎨 Prediction Visualizing...')
+
+
+feature_names = batch_data.columns
+
+feature_importance = pd.DataFrame(feature_names, columns=["feature"])
+feature_importance["importance"] = model.feature_importances_
+feature_importance = feature_importance.sort_values(by=["importance"], ascending=False)
+
+fig_importance = px.bar(
+    feature_importance,
+    x='feature',
+    y='importance',
+    title='Feature Importance Plot'
+     )
+
+fig_importance.update_xaxes(tickangle=23)
+fig_importance.update_xaxes(title="Feature")
+fig_importance.update_yaxes(title="Importance")
+fig_importance.update_traces(hovertemplate='Feature: %{x} <br>Importance: %{y}') 
+
+st.plotly_chart(fig_importance)
+
+
+def plot_histogram(data, x_col, title, xlabel, ylabel):
+
+    fig = go.Figure()
+
+    fig = px.histogram(
+        data,
+        x=x_col,
+        color="Churn",
+        title=title
+    )
+
+    fig.update_xaxes(title=xlabel)
+    fig.update_yaxes(title=ylabel)
+    fig.update_traces(hovertemplate=xlabel + ': %{x} <br>' + ylabel + ': %{y}')
+
+    return fig
+
+
+st.plotly_chart(plot_histogram(df_all, 'internetservice', 'Churn rate according to internet service subscribtion', 'Internet service', 'Number of customers'))
+st.plotly_chart(plot_histogram(df_all, 'streamingmovies', 'Churn rate according to streaming movies subscribtion', 'Streaming movies', 'Number of customers'))
+st.plotly_chart(plot_histogram(df_all, 'streamingtv', 'Churn rate according to internet streaming tv subscribtion', 'Gender', 'Number of customers'))
+st.plotly_chart(plot_histogram(df_all, 'gender', 'Churn rate according to Gender', 'Gender', 'Number of customers'))
+st.plotly_chart(plot_histogram(df_all, 'totalcharges', 'Distribution of Total Charges according to Churn/Not', "Charge Value", 'Number of customers'))
+st.plotly_chart(plot_histogram(df_all, 'paymentmethod', 'Amount of each Payment Method', "Payment Method", 'Total Amount'))
+st.plotly_chart(plot_histogram(df_all, 'partner', 'Affect of having a partner on Churn/Not', "Have a partner", 'Number of customers'))
+
+st.success('🎉 📈 🤝 App Finished Successfully 🤝 📈 🎉')