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import gradio as gr
import numpy as np
import pickle
import pandas as pd
def load_pickle(filename):
with open(filename, 'rb') as file:
data = pickle.load(file)
return data
pkl_file=load_pickle('modelcomponent1.pkl')
numeric_imputer=pkl_file['numerical_imputer']
categoric_imputer=pkl_file['categorical_imputer']
scaler_=pkl_file['scaler']
encoder_=pkl_file['encoder']
best_model = pkl_file['model']
top_pack=pkl_file['top_pack_values']
def preprocess_data(Data_Volume, On_Net,Orange, Tigo, Regularity, Freq_Top_Pack,
Region, Tenure, Top_Pack):
df=pd.DataFrame({
'Data_Volume':[Data_Volume],
'On_Net':[On_Net],
'Orange':[Orange],
'Tigo' :[Tigo],
'Regularity':[Regularity],
'Freq_Top_Pack':[Freq_Top_Pack],
'Region':[Region],
'Tenure' :[Tenure],
'Top_Pack':[Top_Pack]
})
# Preprocess the data
# Selecting categorical and numerical columns separately
cat_columns = df.select_dtypes(include=['object', 'category']).columns.tolist()
num_columns = df.select_dtypes(exclude=['object', 'category']).columns.tolist()
# Apply the imputers on the input data
cat_imputer=categoric_imputer.transform(df[cat_columns])
num_imputer=numeric_imputer.transform(df[num_columns])
#Encode
cat_encode = encoder_.transform(cat_imputer)
cat_encoded_df = pd.DataFrame(cat_encode.toarray(), columns=encoder_.get_feature_names_out(cat_columns))
print(num_imputer)
# Scale the numerical columns
num_scaler=scaler_.transform( num_imputer)
num_scaler_df=pd.DataFrame(num_scaler, columns=num_columns)
#combined
df_final = pd.concat([num_scaler_df, cat_encoded_df], axis=1)
#print(df_final)
return df_final
def predict_churn(Data_Volume, On_Net, Orange,Tigo, Regularity, Freq_Top_Pack, Region, Tenure, Top_Pack):
preprocessed_data = preprocess_data(Data_Volume, On_Net,Orange, Tigo, Regularity, Freq_Top_Pack, Region, Tenure, Top_Pack)
# Make predictions
predictions = best_model.predict_proba(preprocessed_data)
#print(predictions)
return round(predictions[0][0],3), round(predictions[0][1],3)
with gr.Blocks() as demo:
gr.Markdown(''' # Welcome cherished user, let's predict customer churn!''')
with gr.Row():
Region = gr.Dropdown(label='Region', choices=['DAKAR', 'DIOURBEL', 'FATICK',
'KAFFRINE', 'KAOLACK', 'KEDOUGOU',
'KOLDA', 'LOUGA', 'MATAM',
'SAINT-LOUIS', 'SEDHIOU', 'TAMBACOUNDA', 'THIES', 'ZIGUINCHOR'])
Tenure = gr.Dropdown(label='Tenure', choices=['K > 24 month', 'I 18-21 month', 'H 15-18 month' , 'G 12-15 month',
'J 21-24 month','F 9-12 month','E 6-9 month','D 3-6 month'])
Top_Pack =gr.Dropdown(label='Top_Pack', choices=top_pack)
with gr.Row():
Data_Volume= gr.Number(label='Data_Volume')
On_Net=gr.Number(label='On_Net')
Orange=gr.Number(label='Orange')
Tigo=gr.Number(label='Tigo')
Regularity=gr.Slider(label='Regularity', minimum=int(1), maximum=int(62), value=1, step=1)
Freq_Top_Pack=gr.Number(label='Freq_Top_Pack')
submit_button=gr.Button('Predict')
with gr.Row():
with gr.Accordion('Churn Prediction'):
output1=gr.Slider(maximum=1,
minimum=0,
value=1,
label='Yes')
output2=gr.Slider(maximum=1,
minimum=0,
value=1,
label='No')
submit_button.click(fn=predict_churn, inputs=[ Data_Volume, On_Net, Orange, Tigo, Regularity, Freq_Top_Pack, Region, Tenure, Top_Pack ], outputs=[output1, output2])
demo.launch()
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