Update app.py

app.py

@@ -1,69 +1,97 @@
-# Import necessary modules
+#import 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 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)
+
+#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]})
     
-    # Return prediction
-    if predt[0] == 1:
-        return "Customer will Churn"
+    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"
+    
 
-# 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(),
-]
-
-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(),
-]
+#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()
 
-# Define the output component
 output = gr.Textbox(label='Prediction')
+#create the interface component
 
-# 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 = 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)
 
-app.launch(debug=True)
+app.launch(debug = True)