Uploaded required files

app.py

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+import gradio as gr
+import pickle 
+from gradio.themes.base import Base
+# import time
+import pandas as pd
+import numpy as np
+from utils import create_new_columns, create_processed_dataframe
+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'})
+    create_new_columns(dataframe)
+    processed_data = pipeline.transform(dataframe)
+    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.Soft(
+    primary_hue="orange")
+
+
+def load_pickle(filename):
+    with open(filename, 'rb') as file:
+        data = pickle.load(file)
+        return data
+
+pipeline = load_pickle('full_pipeline.pkl')
+model = load_pickle('logistic_reg_class_model.pkl')
+
+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() as demo:
+    gr.Markdown(
+    """
+    # Welcome Cherished User 👋 !
+    ## Customer Churn Classification App
+    Start predicting customer churn.
+    """)
+    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')
+
+    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])
+
+    # if submit_button:
+        # print(predict_churn(gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines, InternetService, 
+                #   OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, 
+                #    Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges))
+
+#demo = gr.Interface(fn=predict_churn, inputs=[gender, SeniorCitizen, Partner, Dependents, Tenure, PhoneService, MultipleLines,
+                                                  # InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,TechSupport,StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges], outputs=['slider', 'slider'], theme=theme)
+demo.launch()

full_pipeline.pkl

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

logistic_reg_class_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:909c3db0e63bc22cacd72f7bd76e53e978f8667b32123fb543ff66831b0e9d1a
+size 1301

requirements.txt

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+matplotlib==3.3.4
+numpy==1.20.1
+pandas==1.2.4
+plotly==5.4.0
+seaborn==0.11.1
+scikit-learn==0.24.1
+IPython==7.22.0
+jupyter_client==6.1.12
+jupyter_core==4.7.1
+jupyterlab==3.0.14
+notebook==6.3.0

theme.py

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+import gradio as gr
+
+gr.themes.builder()

utils.py

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+import pandas as pd
+import numpy as np
+import pickle
+
+
+def load_pickle(filename):
+    with open(filename, 'rb') as file:
+        data = pickle.load(file)
+        return data
+
+preprocessor = load_pickle('full_pipeline.pkl')
+
+
+def create_new_columns(train_data):
+    train_data['Monthly Variations'] = (train_data.loc[:, 'TotalCharges']) -((train_data.loc[:, 'tenure'] * train_data.loc[:, 'MonthlyCharges']))
+    labels =['{0}-{1}'.format(i, i+2) for i in range(0, 73, 3)]
+    train_data['tenure_group'] = pd.cut(train_data['tenure'], bins=(range(0, 78, 3)), right=False, labels=labels)
+    train_data.drop(columns=['tenure'], inplace=True)
+
+
+
+
+def create_processed_dataframe(processed_data, train_data):
+    train_num_cols=train_data.select_dtypes(exclude=['object', 'category']).columns
+    cat_features = preprocessor.named_transformers_['categorical']['cat_encoder'].get_feature_names()
+    labels = np.concatenate([train_num_cols, cat_features])
+    processed_dataframe = pd.DataFrame(processed_data.toarray(), columns=labels)
+    return processed_dataframe