Update app.py

app.py

@@ -52,11 +52,10 @@ def classify_compliant(text):
 #build the Gradio app
 #Instructuction = "Write an imaginary review about a product or service you might be interested in."
 title="Customer Complaints Categorization"
-description = """This application uses fine-tune BERT to perform Customer Complaints Categorization. BERT models are usually pretrained on a large corpus of text and then fine tuned for specific tasks.
-Effectively handling customer complaints provides an opportunity for the service provider to resolve the customer’s problems on time and thus reduce dissatisfaction levels.
-Write a complaint on an insurance product or service and see how the machine learning model is able to Categorisation your Complaint.
-The Complaints Type are:
-1. Debt Collection 
+description = """
+This application uses fine-tuned Roberta to perform Customer Complaints Categorization. Roberta is a popular pre-trained language model that can be used for a variety of natural language processing tasks. This text classification model helps the company to categorize incoming support requests submitted by users and determine the appropriate course of action. The application can provide an opportunity for the service provider to resolve the customer’s problems on time and therefore, reduce dissatisfaction levels.
+Write a complaint on an insurance product or service and see how the machine learning model is able to Categorization your Complaint. Below is the type in which the complaints are segmented:
+1. Debt Collection
 2. False Claim or Statement
 3. Legal Issue
 4. Improper contact or sharing of info