Update app.py

app.py

@@ -1,97 +1,69 @@
-#import modules
+# Import necessary modules
 import numpy as np
 import gradio as gr
 import joblib
 import pandas as pd
 import os
 
-
+# Load the pre-trained model and preprocessor
 def load_model():
     cwd = os.getcwd()
-
     destination = os.path.join(cwd, "saved cap")
-
     Final_model_file_path = os.path.join(destination, "Final_model.joblib")
     preprocessor_file_path = os.path.join(destination, "preprocessor.joblib")
-    
 
     Final_model = joblib.load(Final_model_file_path)
     preprocessor = joblib.load(preprocessor_file_path)
-    
 
     return Final_model, preprocessor
 
 Final_model, preprocessor = load_model()
 
-
-#define prediction function
-def make_prediction(REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGMENT, FREQUENCE, DATA_VOLUME, ON_NET, ORANGE, TIGO, ZONE1, ZONE2,MRG, REGULARITY, FREQ_TOP_PACK):
-    #make a dataframe from input data
-    input_data = pd.DataFrame({'REGION':[REGION], 
-                               'TENURE':[TENURE], 
-                               'MONTANT':[MONTANT], 
-                               'FREQUENCE_RECH':[FREQUENCE_RECH], 
-                               'REVENUE':[REVENUE],
-                               'ARPU_SEGMENT':[ARPU_SEGMENT], 
-                               'FREQUENCE':[FREQUENCE], 
-                               'DATA_VOLUME':[DATA_VOLUME], 
-                               'ON_NET':[ON_NET],
-                               'ORANGE':[ORANGE], 
-                               'TIGO':[TIGO], 
-                               'ZONE1':[ZONE1], 
-                               'ZONE2':[ZONE2],
-                               'MRG':[MRG],
-                               'REGULARITY':[REGULARITY], 
-                               'FREQ_TOP_PACK':[FREQ_TOP_PACK]})
-    
+# Define the prediction function
+def make_prediction(input_data):
+    # Transform the input data using the preprocessor
     transformer = preprocessor.transform(input_data)
-   
-    predt = Final_model.predict(transformer) 
-    #return prediction
-    if predt[0]==1:
-        return "Customer will Churn" 
-    return "Customer will not Churn"
+    predt = Final_model.predict(transformer)
     
+    # Return prediction
+    if predt[0] == 1:
+        return "Customer will Churn"
+    return "Customer will not Churn"
+
+# Create the input components for gradio (organize them into two columns)
+input_column1 = [
+    gr.inputs.Dropdown(choices=['DAKAR', 'THIES', 'SAINT-LOUIS', 'LOUGA', 'KAOLACK', 'DIOURBEL', 'TAMBACOUNDA', 'KAFFRINE', 'KOLDA', 'FATICK', 'MATAM', 'ZIGUINCHOR', 'SEDHIOU', 'KEDOUGOU']),
+    gr.inputs.Dropdown(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']),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+]
 
-#create the input components for gradio
-REGION = gr.inputs.Dropdown(choices =['DAKAR', 'THIES', 'SAINT-LOUIS', 'LOUGA', 'KAOLACK', 'DIOURBEL', 'TAMBACOUNDA' 'KAFFRINE,KOLDA', 'FATICK', 'MATAM', 'ZIGUINCHOR', 'SEDHIOU', 'KEDOUGOU']) 
-TENURE = gr.inputs.Dropdown(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']) 
-MONTANT = gr.inputs.Number()
-FREQUENCE_RECH = gr.Number()
-REVENUE = gr.Number()
-ARPU_SEGMENT = gr.Number() 
-FREQUENCE = gr.Number() 
-DATA_VOLUME = gr.Number() 
-ON_NET = gr.Number()
-ORANGE = gr.Number()
-TIGO = gr.Number()
-ZONE1 = gr.Number() 
-ZONE2 = gr.Number()
-MRG = gr.inputs.Dropdown(choices =['NO'])    
-REGULARITY = gr.Number()
-FREQ_TOP_PACK = gr.Number()
+input_column2 = [
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+    gr.inputs.Dropdown(choices=['NO']),
+    gr.inputs.Number(),
+    gr.inputs.Number(),
+]
 
+# Define the output component
 output = gr.Textbox(label='Prediction')
-#create the interface component
 
-app = gr.Interface(fn =make_prediction,inputs =[REGION, 
-                                                TENURE, 
-                                                MONTANT, 
-                                                FREQUENCE_RECH, 
-                                                REVENUE, 
-                                                ARPU_SEGMENT, 
-                                                FREQUENCE, 
-                                                DATA_VOLUME, 
-                                                ON_NET, 
-                                                ORANGE, 
-                                                TIGO, 
-                                                ZONE1, 
-                                                ZONE2,
-                                                MRG, 
-                                                REGULARITY, 
-                                                FREQ_TOP_PACK],
-                                                 title ="Customer Churn Predictor", 
-                                                  description="Enter the feilds Below and click the submit button to Make Your Prediction",
-                                                 outputs = output)
+# Create the interface component with two columns
+app = gr.Interface(
+    fn=make_prediction,
+    inputs=[input_column1, input_column2],
+    title="Customer Churn Predictor",
+    description="Enter the fields below and click the submit button to Make Your Prediction",
+    outputs=output
+)
 
-app.launch(debug = True)
+app.launch(debug=True)