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.gitattributes

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.gitignore

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-\venv
-\gradio

README.md

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----
-title: Customer Churn App
-emoji: 💻
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,133 +0,0 @@
-import gradio as gr
-import pickle 
-# import time
-import pandas as pd
-import numpy as np
-from utils import create_new_columns, create_processed_dataframe
-
-
-pipeline_pkl = "full_pipeline.pkl"
-log_reg = "logistic_reg_class_model.pkl"
-
-# hist_df = "history.csv"
-
-# def check_csv(csv_file, data):
-#     if os.path.isfile(csv_file):
-#         data.to_csv(csv_file, mode='a', header=False, index=False, encoding='utf-8')
-#     else:
-#         history = data.copy()
-#         history.to_csv(csv_file, index=False)
-
-
-
-
-
-
-
-def tenure_values():
-    cols = ['0-2', '3-5', '6-8', '9-11', '12-14', '15-17', '18-20', '21-23', '24-26', '27-29', '30-32', '33-35', '36-38', '39-41', '42-44', '45-47', '48-50', '51-53', '54-56', '57-59', '60-62', '63-65', '66-68', '69-71', '72-74']
-    return cols
-
-def predict_churn(gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, 
-                  OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, 
-                  Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges):
-    
-    data = [gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, 
-                   OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, 
-                   Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges]
-    
-    x = np.array([data])
-    dataframe = pd.DataFrame(x, columns=train_features)
-    dataframe = dataframe.astype({'MonthlyCharges': 'float', 'TotalCharges': 'float', 'tenure': 'float'})
-    dataframe_ = create_new_columns(dataframe)
-    try:
-        processed_data = pipeline.transform(dataframe_)
-    except Exception as e:
-        raise gr.Error('Kindly make sure to check/select all')
-    else:
-        # check_csv(hist_df, dataframe)
-        # history = pd.read_csv(hist_df)
-
-        processed_dataframe = create_processed_dataframe(processed_data, dataframe)
-        predictions = model.predict_proba(processed_dataframe)
-    return round(predictions[0][0], 3), round(predictions[0][1], 3)
-
-
-
-theme = gr.themes.Default().set(body_background_fill="#0E1117",
-                                 background_fill_secondary="#FFFFFF",
-                                 background_fill_primary="#262730",
-                                 body_text_color="#FF4B4B",
-                                 checkbox_background_color='#FFFFFF', 
-                                 button_secondary_background_fill="#FF4B4B")
-
-
-def load_pickle(filename):
-    with open(filename, 'rb') as file:
-        data = pickle.load(file)
-        return data
-
-pipeline = load_pickle(pipeline_pkl)
-model = load_pickle(log_reg)
-
-train_features = ['gender', 'SeniorCitizen', 'Partner', 'Dependents','tenure', 'PhoneService', 'MultipleLines', 'InternetService', 
-                   'OnlineSecurity', 'OnlineBackup', 'DeviceProtection','TechSupport','StreamingTV', 'StreamingMovies', 
-                   'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges']
-
-
-# theme = gr.themes.Base()
-with  gr.Blocks(theme=theme) as demo:
-    gr.HTML("""
-    <h1 style="color:white; text-align:center">Customer Churn Classification App</h1>
-    <h2 style="color:white;">Welcome Cherished User 👋 </h2>
-    <h4 style="color:white;">Start predicting customer churn.</h4>
-    
-    """)
-    with gr.Row():
-        gender = gr.Dropdown(label='Gender', choices=['Female', 'Male'])
-        Contract  = gr.Dropdown(label='Contract', choices=['Month-to-month', 'One year', 'Two year'])
-        InternetService = gr.Dropdown(label='Internet Service', choices=['DSL', 'Fiber optic', 'No'])
-
-    with gr.Accordion('Yes or no'):
-
-        with gr.Row():
-            OnlineSecurity = gr.Radio(label="Online Security", choices=["Yes", "No", "No internet service"])
-            OnlineBackup = gr.Radio(label="Online Backup", choices=["Yes", "No", "No internet service"])
-            DeviceProtection = gr.Radio(label="Device Protection", choices=["Yes", "No", "No internet service"])
-            TechSupport = gr.Radio(label="Tech Support", choices=["Yes", "No", "No internet service"])
-            StreamingTV = gr.Radio(label="TV Streaming", choices=["Yes", "No", "No internet service"])
-            StreamingMovies = gr.Radio(label="Movie Streaming", choices=["Yes", "No", "No internet service"]) 
-        with gr.Row():
-            SeniorCitizen = gr.Radio(label="Senior Citizen", choices=["Yes", "No"])
-            Partner = gr.Radio(label="Partner", choices=["Yes", "No"])
-            Dependents = gr.Radio(label="Dependents", choices=["Yes", "No"])
-            PaperlessBilling = gr.Radio(label="Paperless Billing", choices=["Yes", "No"])
-            PhoneService = gr.Radio(label="Phone Service", choices=["Yes", "No"])
-            MultipleLines = gr.Radio(label="Multiple Lines", choices=["No phone service", "Yes", "No"]) 
-    
-    with gr.Row():
-        MonthlyCharges = gr.Number(label="Monthly Charges")
-        TotalCharges = gr.Number(label="Total Charges")
-        Tenure = gr.Number(label='Months of Tenure')
-        PaymentMethod = gr.Dropdown(label="Payment Method", choices=["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"])
-
-    submit_button = gr.Button('Prediction')
-    # print(type([[122, 456]]))
-    
-    with gr.Row():
-        with gr.Accordion('Churn Prediction'):
-            output1 = gr.Slider(maximum=1,
-                                minimum=0,
-                                value=0.0,
-                                label='Yes')
-            output2 = gr.Slider(maximum=1,
-                                minimum=0,
-                                value=0.0,
-                                label='No')
-        # with gr.Accordion('Input History'):
-        #     output3 = gr.Dataframe()
-
-    submit_button.click(fn=predict_churn, inputs=[gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines,     
-                                                  InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges], outputs=[output1, output2])
-
-

full_pipeline.pkl

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-version https://git-lfs.github.com/spec/v1
-oid sha256:8258008cf92ff5e5446c62b85547661f5139b8034dd3408cb61224b44fdf7c1b
-size 3517

history.csv

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-gender,SeniorCitizen,Partner,Dependents,PhoneService,MultipleLines,InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges,Monthly Variations,tenure_group
-Female,Yes,Yes,Yes,No,Yes,Fiber optic,Yes,Yes,Yes,No internet service,Yes,Yes,Month-to-month,No,Bank transfer (automatic),123.0,1345.0,-131.0,12-14
-Female,Yes,Yes,Yes,No,Yes,Fiber optic,Yes,Yes,Yes,No internet service,Yes,Yes,Month-to-month,No,Bank transfer (automatic),123.0,1345.0,-131.0,12-14
-Female,Yes,Yes,No,No,Yes,Fiber optic,Yes,Yes,Yes,No internet service,Yes,Yes,Month-to-month,No,Bank transfer (automatic),123.0,5677.0,4201.0,12-14
-Male,Yes,Yes,Yes,Yes,No phone service,Fiber optic,No internet service,Yes,Yes,No,Yes,No internet service,Two year,Yes,Bank transfer (automatic),123.0,1200.0,-276.0,12-14
-Male,Yes,No,Yes,No,Yes,Fiber optic,No internet service,Yes,Yes,No,Yes,No internet service,Two year,No,Mailed check,123.0,1200.0,-276.0,12-14
-Female,No,Yes,Yes,Yes,No phone service,Fiber optic,Yes,Yes,Yes,Yes,No internet service,No internet service,One year,Yes,Credit card (automatic),128.0,1234.0,-1710.0,21-23
-Female,No,Yes,Yes,Yes,No phone service,Fiber optic,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),128.0,1234.0,-1710.0,21-23
-Female,No,Yes,Yes,Yes,Yes,Fiber optic,No internet service,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),128.0,1234.0,-1710.0,21-23
-Male,Yes,Yes,Yes,Yes,No phone service,Fiber optic,No,Yes,No internet service,Yes,Yes,Yes,One year,Yes,Mailed check,120.0,12003.0,10923.0,9-11
-Female,Yes,Yes,Yes,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No internet service,No internet service,One year,No,Bank transfer (automatic),167.0,2000.0,497.0,9-11

logistic_reg_class_model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:909c3db0e63bc22cacd72f7bd76e53e978f8667b32123fb543ff66831b0e9d1a
-size 1301

requirements.txt

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-gradio
-numpy==1.20.1
-pandas==1.2.4
-scikit-learn==0.24.1

theme.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.themes.builder()

utils.py

@@ -1,47 +0,0 @@
-import pandas as pd
-import numpy as np
-import pickle
-
-# Define the name of the pickle file containing a pre-trained data preprocessing pipeline.
-pipeline_pkl = "full_pipeline.pkl"
-
-# Function to load data from a pickle file.
-def load_pickle(filename):
-    with open(filename, 'rb') as file:
-        data = pickle.load(file)
-        return data
-
-# Load the pre-processing pipeline from the pickle file.
-preprocessor = load_pickle(pipeline_pkl)
-
-# Function to create new columns in the training data.
-def create_new_columns(train_data):
-    # Calculate 'Monthly Variations' column as the difference between 'TotalCharges' and the product of 'tenure' and 'MonthlyCharges'.
-    train_data['Monthly Variations'] = (train_data.loc[:, 'TotalCharges']) -((train_data.loc[:, 'tenure'] * train_data.loc[:, 'MonthlyCharges']))
-    
-    # Define labels for 'tenure_group' based on a range of values.
-    labels =['{0}-{1}'.format(i, i+2) for i in range(0, 73, 3)]
-    
-    # Create a 'tenure_group' column by binning 'tenure' values into the specified labels.
-    train_data['tenure_group'] = pd.cut(train_data['tenure'], bins=(range(0, 78, 3)), right=False, labels=labels)
-    
-    # Drop the 'tenure' column from the DataFrame.
-    train_data.drop(columns=['tenure'], inplace=True)
-    
-    return train_data
-
-# Function to create a processed DataFrame from the processed data.
-def create_processed_dataframe(processed_data, train_data):
-    # Select numerical columns from the training data.
-    train_num_cols=train_data.select_dtypes(exclude=['object', 'category']).columns
-    
-    # Get feature names from the categorical encoder in the preprocessor.
-    cat_features = preprocessor.named_transformers_['categorical']['cat_encoder'].get_feature_names()
-    
-    # Concatenate numerical and categorical feature names.
-    labels = np.concatenate([train_num_cols, cat_features])
-    
-    # Create a DataFrame from the processed data with the specified column labels.
-    processed_dataframe = pd.DataFrame(processed_data.toarray(), columns=labels)
-    
-    return processed_dataframe