initial commit

app.py

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+import pandas as pd
+from transformers import pipeline
+import streamlit as st
+import datetime
+from huggingface_hub import hf_hub_download
+import joblib
+
+REPO_ID = "AlbieCofie/predict-customer-churn"
+FILENAME = "sklearn_model.joblib"
+
+num_imputer = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="numerical_imputer.joblib")
+)
+
+cat_imputer = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="categorical_imputer.joblib")
+)
+
+encoder = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="encoder.joblib")
+)
+
+scaler = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="scaler.joblib")
+)
+
+model = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="Final_model.joblib")
+)
+
+# Create a function that applies the ML pipeline and makes predictions
+def predict(gender,SeniorCitizen,Partner,Dependents, tenure, PhoneService,MultipleLines,
+                       InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,
+                       Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges):
+
+
+
+    # Create a dataframe with the input data
+    input_df = pd.DataFrame({
+        'gender': [gender],
+        'SeniorCitizen': [SeniorCitizen],
+        'Partner': [Partner],
+        'Dependents': [Dependents],
+        'tenure': [tenure],
+        'PhoneService': [PhoneService],
+        'MultipleLines': [MultipleLines],
+        'InternetService': [InternetService],
+        'OnlineSecurity': [OnlineSecurity],
+        'OnlineBackup': [OnlineBackup],
+        'DeviceProtection': [DeviceProtection],
+        'TechSupport': [TechSupport],
+        'StreamingTV': [StreamingTV],
+        'StreamingMovies': [StreamingMovies],
+        'Contract': [Contract],
+        'PaperlessBilling': [PaperlessBilling],
+        'PaymentMethod': [PaymentMethod],
+        'MonthlyCharges': [MonthlyCharges],
+        'TotalCharges': [TotalCharges]
+        })
+
+# Selecting categorical and numerical columns separately
+    cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object']
+    num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object']
+
+    # Apply the imputers on the input data
+    input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns])
+    input_df_imputed_num = num_imputer.transform(input_df[num_columns])
+
+    # Encode the categorical columns
+    input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(),
+                                   columns=encoder.get_feature_names_out(cat_columns))
+
+    # Scale the numerical columns
+    input_df_scaled = scaler.transform(input_df_imputed_num)
+    input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns)
+
+
+    #joining the cat encoded and num scaled
+    final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1)
+
+    final_df = final_df.reindex(columns=['SeniorCitizen','tenure','MonthlyCharges','TotalCharges',
+     'gender_Female','gender_Male','Partner_No','Partner_Yes','Dependents_No','Dependents_Yes','PhoneService_No',
+     'PhoneService_Yes','MultipleLines_No','MultipleLines_Yes','InternetService_DSL','InternetService_Fiber optic',
+     'InternetService_No','OnlineSecurity_No','OnlineSecurity_Yes','OnlineBackup_No','OnlineBackup_Yes','DeviceProtection_No',
+     'DeviceProtection_Yes','TechSupport_No','TechSupport_Yes','StreamingTV_No','StreamingTV_Yes','StreamingMovies_No',
+     'StreamingMovies_Yes','Contract_Month-to-month','Contract_One year','Contract_Two year','PaperlessBilling_No',
+     'PaperlessBilling_Yes','PaymentMethod_Bank transfer (automatic)','PaymentMethod_Credit card (automatic)','PaymentMethod_Electronic check',
+     'PaymentMethod_Mailed check'])
+
+    # Make predictions using the model
+    predictions = model.predict(final_df)
+
+     # Make predictions using the model
+     #predictions = model.predict(final_df)
+
+     # Convert the numpy array to an integer
+     #prediction_label = int(predictions.item())
+
+    prediction_label = "Beware!!! This customer is likely to Churn" if predictions.item() == "Yes" else "This customer is Not likely churn"
+
+
+    return prediction_label
+
+     #return predictions    
+
+
+if 'clicked' not in st.session_state:
+    st.session_state.clicked = False
+
+def click_button():
+    st.session_state.clicked = True
+
+
+st.title("CUSTOMER CHURN PREDICTION APP")
+
+with st.form(key="customer-information"):
+    st.markdown("This app predicts whether a customer will leave your company or not. Enter the details of the customer below to see the result")
+    gender = st.radio('Select your gender', ('male', 'female'))
+    SeniorCitizen = st.radio("Are you a Seniorcitizen; No=0 and Yes=1", ('0', '1'))
+    Partner = st.radio('Do you have Partner', ('Yes', 'No'))
+    Dependents = st.selectbox('Do you have any Dependents?', ('No', 'Yes'))
+    tenure = st.number_input('Lenght of tenure (no. of months with Telco)', min_value=0, max_value=90, value=1, step=1)
+    PhoneService = st.radio('Do you have PhoneService? ', ('No', 'Yes'))
+    MultipleLines = st.radio('Do you have MultipleLines', ('No', 'Yes'))
+    InternetService = st.radio('Do you have InternetService', ('DSL', 'Fiber optic', 'No'))
+    OnlineSecurity = st.radio('Do you have OnlineSecurity?', ('No', 'Yes'))
+    OnlineBackup = st.radio('Do you have OnlineBackup?', ('No', 'Yes'))
+    DeviceProtection = st.radio('Do you have DeviceProtection?', ('No', 'Yes'))
+    TechSupport = st.radio('Do you have TechSupport?', ('No', 'Yes'))
+    StreamingTV = st.radio('Do you have StreamingTV?', ('No', 'Yes'))
+    StreamingMovies = st.radio('Do you have StreamingMovies?', ('No', 'Yes'))
+    Contract = st.selectbox('which Contract do you use?', ('Month-to-month', 'One year', 'Two year'))
+    PaperlessBilling = st.radio('Do you prefer PaperlessBilling?', ('Yes', 'No'))
+    PaymentMethod = st.selectbox('Which PaymentMethod do you prefer?', ('Electronic check', 'Mailed check', 'Bank transfer (automatic)',
+                                        'Credit card (automatic)'))
+    MonthlyCharges = st.number_input("Enter monthly charges (the range should between 0-120)")
+    TotalCharges = st.number_input("Enter total charges (the range should between 0-10.000)")           
+    st.form_submit_button('Predict', on_click=click_button)
+
+    if st.session_state.clicked:
+    # The message and nested widget will remain on the page
+        st.write(gender,SeniorCitizen,Partner,Dependents, tenure, PhoneService,MultipleLines,
+            InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,
+            Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges)
+        #predict(gender,SeniorCitizen,Partner,Dependents, tenure, PhoneService,MultipleLines,
+                       #InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,
+                       #Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges)

packages.txt

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+freeglut3-dev
+libgtk2.0-dev

requirements.txt

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+transformers
+streamlit
+huggingface_hub
+scikit-learn==1.2.2
+joblib
+torch
+pandas