Spaces:
Sleeping
Sleeping
File size: 10,717 Bytes
8103156 b7561d0 8103156 e5afe31 4a495b4 d4fe3da b7561d0 8103156 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import gradio as gr
import pandas as pd
import joblib
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
# Load the saved full pipeline from the file
full_pipeline = joblib.load('pipe.pkl')
# Define the predict function
def predict(gender, SeniorCitizen, Partner, Dependents, Contract, tenure, MonthlyCharges,
TotalCharges, PaymentMethod, PhoneService, MultipleLines, InternetService,
OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV,
StreamingMovies, PaperlessBilling):
# Create a DataFrame from the input data
input_data = pd.DataFrame({
'gender': [gender] if gender else ['Male'], # Replace None with default value
'SeniorCitizen': [SeniorCitizen] if SeniorCitizen is not None else [0], # Replace None with default value
'Partner': [Partner] if Partner else ['No'], # Replace None with default value
'Dependents': [Dependents] if Dependents else ['No'], # Replace None with default value
'tenure': [tenure] if tenure else [1], # Replace None with default value
'PhoneService': [PhoneService] if PhoneService else ['Yes'], # Replace None with default value
'MultipleLines': [MultipleLines] if MultipleLines else ['No'], # Replace None with default value
'InternetService': [InternetService] if InternetService else ['DSL'], # Replace None with default value
'OnlineSecurity': [OnlineSecurity] if OnlineSecurity else ['No'], # Replace None with default value
'OnlineBackup': [OnlineBackup] if OnlineBackup else ['No'], # Replace None with default value
'DeviceProtection': [DeviceProtection] if DeviceProtection else ['No'], # Replace None with default value
'TechSupport': [TechSupport] if TechSupport else ['No'], # Replace None with default value
'StreamingTV': [StreamingTV] if StreamingTV else ['No'], # Replace None with default value
'StreamingMovies': [StreamingMovies] if StreamingMovies else ['No'], # Replace None with default value
'Contract': [Contract] if Contract else ['Month-to-month'], # Replace None with default value
'PaperlessBilling': [PaperlessBilling] if PaperlessBilling else ['No'], # Replace None with default value
'PaymentMethod': [PaymentMethod] if PaymentMethod else ['Electronic check'], # Replace None with default value
'MonthlyCharges': [MonthlyCharges] if MonthlyCharges else [0.0], # Replace None with default value
'TotalCharges': [TotalCharges] if TotalCharges else [0.0] # Replace None with default value
})
# Make predictions using the loaded logistic regression model
#predict probabilities
predictions = full_pipeline.predict_proba(input_data)
#take the index of the maximum probability
index=np.argmax(predictions)
higher_pred_prob=round((predictions[0][index])*100)
#return predictions[0]
print(f'[Info] Predicted probabilities{predictions},{full_pipeline.classes_}')
if full_pipeline.classes_[index] == "Yes":
return f"This Customer is likely to Churn\nWe are {higher_pred_prob}% confident about this prediction"
else:
return f"This Customer is Not likely to Churn \nWe are {higher_pred_prob}% confident about this prediction"
# Setting Gradio App Interface
with gr.Blocks(css=".gradio-container {background-color: grey}",theme=gr.themes.Base(primary_hue='blue'),title='Uriel') as demo:
gr.Markdown("# Teleco Customer Churn Prediction #\n*This App allows the user to predict whether a customer will churn or not by entering values in the given fields. Any field left blank takes the default value.*")
# Receiving ALL Input Data here
gr.Markdown("**Demographic Data**")
with gr.Row():
gender = gr.Dropdown(label="Gender", choices=["Male", "Female"])
SeniorCitizen = gr.Radio(label="Senior Citizen", choices=[1, 0])
Partner = gr.Radio(label="Partner", choices=["Yes", "No"])
Dependents = gr.Radio(label="Dependents", choices=["Yes", "No"])
gr.Markdown("**Service Length and Charges (USD)**")
with gr.Row():
Contract = gr.Dropdown(label="Contract", choices=["Month-to-month", "One year", "Two year"])
tenure = gr.Slider(label="Tenure (months)", minimum=1, step=1, interactive=True)
MonthlyCharges = gr.Slider(label="Monthly Charges", step=0.05)
TotalCharges = gr.Slider(label="Total Charges", step=0.05)
# Phone Service Usage part
gr.Markdown("**Phone Service Usage**")
with gr.Row():
PhoneService = gr.Radio(label="Phone Service", choices=["Yes", "No"])
MultipleLines = gr.Dropdown(label="Multiple Lines", choices=[
"Yes", "No", "No phone service"])
# Internet Service Usage part
gr.Markdown("**Internet Service Usage**")
with gr.Row():
InternetService = gr.Dropdown(label="Internet Service", choices=["DSL", "Fiber optic", "No"])
OnlineSecurity = gr.Dropdown(label="Online Security", choices=["Yes", "No", "No internet service"])
OnlineBackup = gr.Dropdown(label="Online Backup", choices=["Yes", "No", "No internet service"])
DeviceProtection = gr.Dropdown(label="Device Protection", choices=["Yes", "No", "No internet service"])
TechSupport = gr.Dropdown(label="Tech Support", choices=["Yes", "No", "No internet service"])
StreamingTV = gr.Dropdown(label="TV Streaming", choices=["Yes", "No", "No internet service"])
StreamingMovies = gr.Dropdown(label="Movie Streaming", choices=["Yes", "No", "No internet service"])
# Billing and Payment part
gr.Markdown("**Billing and Payment**")
with gr.Row():
PaperlessBilling = gr.Radio(
label="Paperless Billing", choices=["Yes", "No"])
PaymentMethod = gr.Dropdown(label="Payment Method", choices=["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"])
# Output Prediction
output = gr.Text(label="Outcome")
submit_button = gr.Button("Predict")
submit_button.click(fn= predict,
outputs= output,
inputs=[gender, SeniorCitizen, Partner, Dependents, Contract, tenure, MonthlyCharges, TotalCharges, PaymentMethod, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, PaperlessBilling],
),
# Add the reset and flag buttons
def clear():
output.value = ""
return 'Predicted values have been reset'
clear_btn = gr.Button("Reset", variant="primary")
clear_btn.click(fn=clear, inputs=None, outputs=output)
demo.launch(inbrowser = True)
# Make predictions using the loaded logistic regression model
predictions = full_pipeline.predict(input_data)
#return predictions[0]
if predictions[0] == "Yes":
return "Churn"
else:
return "Not Churn"
# Setting Gradio App Interface
with gr.Blocks(css=".gradio-container {background-color: grey}") as demo:
gr.Markdown("# Teleco Customer Churn Prediction #\n*This App allows the user to predict whether a customer will churn or not by entering values in the given fields. Any field left blank takes the default value.*")
# Receiving ALL Input Data here
gr.Markdown("**Demographic Data**")
with gr.Row():
gender = gr.Dropdown(label="Gender", choices=["Male", "Female"])
SeniorCitizen = gr.Radio(label="Senior Citizen", choices=[1, 0])
Partner = gr.Radio(label="Partner", choices=["Yes", "No"])
Dependents = gr.Radio(label="Dependents", choices=["Yes", "No"])
gr.Markdown("**Service Length and Charges (USD)**")
with gr.Row():
Contract = gr.Dropdown(label="Contract", choices=["Month-to-month", "One year", "Two year"])
tenure = gr.Slider(label="Tenure (months)", minimum=1, step=1, interactive=True)
MonthlyCharges = gr.Slider(label="Monthly Charges", step=0.05)
TotalCharges = gr.Slider(label="Total Charges", step=0.05)
# Phone Service Usage part
gr.Markdown("**Phone Service Usage**")
with gr.Row():
PhoneService = gr.Radio(label="Phone Service", choices=["Yes", "No"])
MultipleLines = gr.Dropdown(label="Multiple Lines", choices=[
"Yes", "No", "No phone service"])
# Internet Service Usage part
gr.Markdown("**Internet Service Usage**")
with gr.Row():
InternetService = gr.Dropdown(label="Internet Service", choices=["DSL", "Fiber optic", "No"])
OnlineSecurity = gr.Dropdown(label="Online Security", choices=["Yes", "No", "No internet service"])
OnlineBackup = gr.Dropdown(label="Online Backup", choices=["Yes", "No", "No internet service"])
DeviceProtection = gr.Dropdown(label="Device Protection", choices=["Yes", "No", "No internet service"])
TechSupport = gr.Dropdown(label="Tech Support", choices=["Yes", "No", "No internet service"])
StreamingTV = gr.Dropdown(label="TV Streaming", choices=["Yes", "No", "No internet service"])
StreamingMovies = gr.Dropdown(label="Movie Streaming", choices=["Yes", "No", "No internet service"])
# Billing and Payment part
gr.Markdown("**Billing and Payment**")
with gr.Row():
PaperlessBilling = gr.Radio(
label="Paperless Billing", choices=["Yes", "No"])
PaymentMethod = gr.Dropdown(label="Payment Method", choices=["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"])
# Output Prediction
output = gr.Text(label="Outcome")
submit_button = gr.Button("Predict")
submit_button.click(fn= predict,
outputs= output,
inputs=[gender, SeniorCitizen, Partner, Dependents, Contract, tenure, MonthlyCharges, TotalCharges, PaymentMethod, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, PaperlessBilling],
),
# Add the reset and flag buttons
def clear():
output.value = ""
return None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None
clear_btn = gr.Button("Reset", variant="primary")
clear_btn.click(fn=clear, inputs=None, outputs=output)
demo.launch(inbrowser = True) |