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app.py

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+# IMPORT LIBRARIES
+import gradio as gr
+import numpy as np
+import pandas as pd
+import pickle
+import xgboost as xgb
+from xgboost import XGBClassifier
+
+
+
+# Function to load ML toolkit
+def load_ml_toolkit(file_path):
+    with open(file_path, "rb") as file:
+        loaded_toolkit = pickle.load(file)
+    return loaded_toolkit
+
+
+# Importing the toolkit
+loaded_toolkit = load_ml_toolkit(r"export/App_toolkit.pkl")
+
+encoder = loaded_toolkit["encoder"]
+scaler = loaded_toolkit["scaler"]
+
+# Import the model
+model = XGBClassifier()
+model.load_model(r"xgb_model.json")
+
+
+#Colmuns to work with 
+input_cols = ["tenure", "montant", "frequence_rech", "arpu_segment", "frequence", "data_volume", "regularity", "freq_top_pack"]
+columns_to_scale = ["montant", "frequence_rech", "arpu_segment", "frequence", "data_volume", "regularity", "freq_top_pack"]
+categoricals = ["tenure"]
+
+
+
+# Function to process inputs and return prediction
+def process_and_predict(*args, encoder=encoder, scaler=scaler, model=model):
+   
+    # Convert inputs into a DataFrame
+    input_data = pd.DataFrame([args], columns=input_cols)
+
+    # Encode the categorical column
+    input_data["tenure"] = encoder.transform(input_data["tenure"])
+    
+    # Scale the numeric columns
+    input_data[columns_to_scale] = scaler.transform(input_data[columns_to_scale])
+
+    # Making the prediction
+    model_output = model.predict(input_data)
+    return {"Prediction: CHURN": float(model_output[0]), "Prediction: STAY": 1-float(model_output[0])}
+
+
+#App Interface
+with gr.Blocks() as turn_on_the_gradio:
+    gr.Markdown("# 📞 EXPRESSO TELECOM CUSTOMER CHURN ☎️")
+    gr.Markdown('''
+        
+        ## WELCOME CHERISHED USER👋 
+        
+        ### PLEASE GO AHEAD AND MAKE A PREDICTION 🙂''')
+    
+    # Receiving Inputs
+    
+    gr.Markdown("**SECTION ONE**")
+    gr.Markdown("**CUSTOMER NETWORK ACTTIVITIES**")
+    with gr.Row():
+        montant = gr.Slider(label="Top-up amount", minimum=20, step=1, interactive=True, value=1, maximum= 500000)
+        data_volume = gr.Slider(label="Number of connections", minimum=0, step=1, interactive=True, value=1, maximum= 2000000)
+
+   
+    with gr.Row():
+        frequence_rech = gr.Slider(label="Recharge Frequency", minimum=1, step=1, interactive=True, value=1, maximum=220)
+        freq_top_pack = gr.Slider(label="Top Package Activation Frequency", minimum=1, step=1, interactive=True, value=1, maximum=1050)
+        regularity = gr.Slider(label="Regularity (out of 90 days)", minimum=1, step=1, interactive=True, value=1, maximum=90)        
+        tenure = gr.Dropdown(label="Tenure (time on the network)", choices=["D 3-6 month", "E 6-9 month", "F 9-12 month", "G 12-15 month", "H 15-18 month", "I 18-21 month", "J 21-24 month", "K > 24 month"], value="K > 24 month")
+
+
+    gr.Markdown("**SECTION 2**")
+    gr.Markdown("**CUSTOMER INCOME DETAILS**")
+    with gr.Row():
+        arpu_segment = gr.Slider(label="Income over the last 90 days", step=1, maximum=287000, interactive=True)
+        frequence = gr.Slider(label="Number of times the customer has made an income", step=1, minimum=1, maximum=91, interactive=True)
+
+    # Output Prediction
+    output = gr.Label("...")
+    submit_button = gr.Button("Submit")
+    
+   
+    
+    submit_button.click(fn = process_and_predict,
+                        outputs = output,
+                        inputs=[tenure, montant, frequence_rech, arpu_segment, frequence, data_volume, regularity, freq_top_pack])
+
+turn_on_the_gradio.launch(inbrowser= True)

export/App_toolkit.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:501d2fdba2a9b4e73eb95f028a2bef643a6ca8c4c1b8bd190fc22378632a0a81
+size 362950