Update My Project File.

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 import pandas as pd
 import pickle
 
+IMAGENET_1K_URL ="https://user-images.githubusercontent.com/115732734/271723332-6c824e95-5e2f-48ec-af1c-b66ac7db1d7a.jpeg"
+
 # Load the model and encoder and scaler
 model = pickle.load(open("model.pkl", "rb"))
 encoder = pickle.load(open("encoder.pkl", "rb"))
@@ -80,32 +82,7 @@ def predict_churn(gender, SeniorCitizen, Partner, Dependents, tenure,
     # Make prediction
     prediction = model.predict(processed_df)
     return "Churn" if prediction[0] == 1 else "No Churn"
-
-
-# Add Image Tags 
-gr.Markdown(
-"""
-<div class="row">
-  <div class="column">
-    <img src="https://user-images.githubusercontent.com/115732734/271723332-6c824e95-5e2f-48ec-af1c-b66ac7db1d7a.jpeg" style="width:550"></div>
-  
-  <div class="column">
-    <img src="https://user-images.githubusercontent.com/115732734/271723345-50f27ca9-94ee-4e7c-ad3b-2b10f27d31bb.jpeg" style="width:550"></div>
-</div>
-<style>
-.row {
-  display: flex;
-}
-.column {
-  flex: 33.33%;
-  padding: 5px;
-}
-</style>
-""",
-unsafe_allow_html=True
-)
-
-
+           
 # Launch the Gradio app
 iface = gr.Interface(predict_churn, inputs=input_components, outputs=output_components)
 iface.launch(inbrowser= True, show_error= True)