Create app.py

app.py

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+# Importing required Library
+import streamlit as st
+import pandas as pd
+import numpy as np
+import os, pickle
+from sklearn import preprocessing
+from PIL import Image
+
+
+# Setting up page configuration and directory path
+st.set_page_config(page_title="Customer churn prediction App", page_icon="🛳️", layout="centered")
+DIRPATH = os.path.dirname(os.path.realpath(__file__))
+
+
+# Setting background image
+page_bg_img = '''
+<style>
+[data-testid="stAppViewContainer"] {
+background-color:black;
+background-image:
+radial-gradient(white, rgba(255,255,255,.2) 2px, transparent 40px),
+radial-gradient(white, rgba(255,255,255,.15) 1px, transparent 30px),
+radial-gradient(white, rgba(255,255,255,.1) 2px, transparent 40px),
+radial-gradient(rgba(255,255,255,.4), rgba(255,255,255,.1) 2px, transparent 30px);
+background-size: 550px 550px, 350px 350px, 250px 250px, 150px 150px;
+background-position: 0 0, 40px 60px, 130px 270px, 70px 100px;
+
+}
+
+</style>
+'''
+st.markdown(page_bg_img,unsafe_allow_html=True)
+
+
+
+# Setting up logo
+st.image('https://miro.medium.com/v2/resize:fit:786/format:webp/1*xT7y7u-DDssc3P_nT_qp2Q.png', width=700,caption=None, use_column_width=None, clamp=100, channels="RGB", output_format='JPEG')
+
+ # Setting up Sidebar
+social_acc = ['Data Field Description', 'EDA', 'About App']
+social_acc_nav = st.sidebar.radio('**INFORMATION SECTION**', social_acc)
+
+if social_acc_nav == 'Data Field Description':
+     st.sidebar.markdown("""
+    The table below gives a description on the variables required to make predictions.
+    | Variable      | Definition:       |
+    | :------------ |:--------------- |
+    | FREQUENCE     | number of times the client has made an income |
+    | TENURE        | duration in the network |
+    | FREQUENCE_RECH| number of times the customer refilled |
+    | MONTANT       | top-up amount   |
+    | DATA_VOLUME   | number of connections|
+    | ORANGE        | call to orange |
+    | TIGO          | call to Tigo   |
+    | ZONE1         | call to zones 1   |
+    | ZONE2         | call to zones 2   |
+    | ARPU_SEGMENT  | income over 90 days / 3 |
+    | ON_NET        | inter expresso call |
+    | REGULARITY    | number of times the client is active for 90 days   |
+    | FREQ_TOP_PACK | number of times client has activated the top pack packages|
+    | REVENUE       | monthly income of each client   |
+    """)
+
+elif social_acc_nav == 'EDA':
+    st.sidebar.markdown("<h2 style='text-align: center;'> Exploratory Data Analysis </h2> ", unsafe_allow_html=True)
+    st.sidebar.markdown('''---''')
+    st.sidebar.markdown('''The exploratory data analysis of this project can be find in a Jupyter notebook from the linl below''')
+    st.sidebar.markdown("[Open Notebook](https://github.com/Gyimah3/Store-Sales----Time-Series-Forecasting-Regression-project-/blob/main/Store%20Sales%20--%20Time%20Series%20Forecasting(Regression%20project).ipynb)")
+
+elif social_acc_nav == 'About App':
+    st.sidebar.markdown("<h2 style='text-align: center;'> Customer Churn prediction App </h2> ", unsafe_allow_html=True)
+    st.sidebar.markdown('''---''')
+    st.sidebar.markdown("""
+                        | Brief Introduction|
+                        | :------------ |
+                        This projet is based on a Zindi challenge for an African telecommunications company (Expresso)
+                        that provides customers with airtime and mobile data bundles. The objective of this challenge
+                        is to develop a machine learning model to predict the likelihood of each customer “churning,”
+                        i.e. becoming inactive and not making any transactions for 90 days. This solution will help
+                        this telecom company to better serve their customers by understanding which customers are at risk of leaving""")
+    st.sidebar.markdown("")
+    st.sidebar.markdown("[ Visit Github Repository for more information](https://github.com/Gyimah3/Store-Sales----Time-Series-Forecasting-Regression-project-)")
+    st.sidebar.markdown("Dedicated to: mom❄️ and Sis Evelyn❄️.")
+    st.sidebar.markdown("")
+    
+# Config & Setup
+@st.cache(allow_output_mutation=True)
+def Load_ml_items(relative_path):
+    "Load ML items to reuse them"
+    with open(relative_path, 'rb') as file:
+        loaded_object = pickle.load(file)
+    return loaded_object
+
+
+loaded_object = Load_ml_items(r'ml_com.pkl')
+
+
+
+pipeline_of_my_model = loaded_object["pipeline"]
+num_cols = loaded_object['numeric_columns']
+cat_cols = loaded_object['categorical_columns']
+
+
+
+# Setting up variables for input data
+@st.cache()
+def setup(tmp_df_file):
+    "Setup the required elements like files, models, global variables, etc"
+    pd.DataFrame(
+        dict(
+            TENURE=[],
+            MONTANT=[],
+            FREQUENCE_RECH=[],
+            REVENUE=[],
+            ARPU_SEGMENT=[],
+            FREQUENCE=[],
+            DATA_VOLUME=[],
+            ON_NET=[],
+            ORANGE=[],
+            TIGO=[],
+            ZONE1=[],
+            ZONE2=[],
+            REGULARITY=[],
+            FREQ_TOP_PACK=[]
+           
+        )
+    ).to_csv(tmp_df_file, index=False)
+
+# Setting up a file to save our input data
+tmp_df_file = os.path.join(DIRPATH, "tmp", "data.csv")
+setup(tmp_df_file)
+
+# setting Title for forms
+st.markdown("<h2 style='text-align: center;'> Customer Churn Prediction </h2> ", unsafe_allow_html=True)
+st.markdown("<h7 style='text-align: center;'> Fill in the details below and click on SUBMIT button to make a prediction </h7> ", unsafe_allow_html=True)
+
+
+# Creating columns for for input data(forms)
+left_col,right_col = st.columns(2)#[#20,20],gap="small")
+
+# Developing forms to collect input data
+with st.form(key="information", clear_on_submit=True):
+    # Setting up input data for 1st column
+    left_col.markdown("**FIRST COLUMN DATA**")
+    TENURE= left_col.selectbox("duration in the network:",['K > 24 month','J 21-24 month','I 18-21 month','H 15-18 month','G 12-15 month','F 9-12 month',  'E 6-9 month', 'D 3-6 month'])
+    MONTANT = left_col.number_input("top-up amount:",  min_value=0, max_value= 1000000000)
+    FREQUENCE_RECH = left_col.number_input("number of times the customer refilled:", min_value=0, max_value=1000)
+    REVENUE = left_col.number_input("monthly income of each client:", min_value=0, max_value=1000000000)
+    ARPU_SEGMENT=left_col.number_input("income over 90 days / 3:",min_value=0, max_value=30000)
+    FREQUENCE = left_col.number_input("number of times the client has made an income:", min_value=0, max_value=100)
+    DATA_VOLUME = left_col.number_input("number of connections:",min_value=0, max_value=10000)
+    
+
+    
+     # Setting up input data for 2nd column
+    right_col.markdown("**SECOND COLUMN DATA**")
+    ON_NET = right_col.number_input("inter expresso call:", min_value=0, max_value=10000)
+    ORANGE = right_col.number_input("call to orange:", min_value=0, max_value=10000)
+    TIGO = right_col.number_input("call to Tigo:", min_value=0, max_value=10000)
+    ZONE1 = right_col.number_input("call to Zones 1:", min_value=0, max_value=10000)
+    ZONE2 = right_col.number_input("call to Zones 2:", min_value=0, max_value=10000)
+    REGULARITY = right_col.number_input("number of times the client is active for 90 day:", min_value=0, max_value=100)
+    FREQ_TOP_PACK=right_col.number_input("number of times the client has activated the top pack packages:", min_value=0, max_value=100)
+
+    submitted = st.form_submit_button(label="Submit")
+    
+if submitted:
+    # Saving input data as csv after submission
+    pd.read_csv(tmp_df_file).append(
+        dict(
+            
+               
+            
+                TENURE=TENURE,
+                MONTANT=MONTANT,
+                FREQUENCE_RECH=FREQUENCE_RECH,
+                REVENUE=REVENUE,
+                ARPU_SEGMENT=ARPU_SEGMENT,
+                FREQUENCE=FREQUENCE,
+                DATA_VOLUME=DATA_VOLUME,
+                ON_NET=ON_NET,
+                ORANGE=ORANGE,
+                TIGO=TIGO,
+                ZONE1=ZONE1,
+                ZONE2=ZONE2,
+                REGULARITY=REGULARITY,
+                FREQ_TOP_PACK=FREQ_TOP_PACK
+           
+            ),
+            ignore_index=True,
+    ).to_csv(tmp_df_file, index=False)
+    st.balloons()
+     
+
+    df = pd.read_csv(tmp_df_file)
+    df= df.copy()
+   
+        
+    # Making Predictions
+    # Passing data to pipeline to make prediction
+    pred_output = pipeline_of_my_model.predict(df)
+    prob_output = np.max(pipeline_of_my_model.predict_proba(df))
+    
+    # Interpleting prediction output for display
+    X= pred_output[-1]
+    if X == 1:
+        explanation = 'Person will Churn'
+    else: 
+        explanation = "Person won't churn"
+    output = explanation
+    
+
+    # Displaying prediction results
+    st.markdown('''---''')
+    st.markdown("<h4 style='text-align: center;'> Prediction Results </h4> ", unsafe_allow_html=True)
+    st.success(f"Predicted Survival: {output}")
+    st.success(f"Confidence Probability: {prob_output}")
+    st.markdown('''---''')
+
+    # Making expander to view all records
+    expander = st.expander("See all records")
+    with expander:
+        df = pd.read_csv(tmp_df_file)
+        df['CHURN']= pred_output
+        st.dataframe(df)
+
+