Update app.py

app.py

@@ -1,33 +1,8 @@
 
-###Implement a question answering system using Gradio's lower-level API. The system features two input fields: the first for the context and the second for the user's question. The system then outputs the model's response
-
-
 #Import libraries
 import gradio as gr
 
 
-# Load the question-answering pipeline #using deepset/roberta-base-squad2 model
-qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
-
-
-
-# List of questions  #sample questions that users might ask
-questions = [
-    "What is cryptocurrency and how is it secured?",
-    "Can you explain how blockchain technology is related to cryptocurrencies?",
-    "What are the different types of cryptocurrencies and their purposes?",
-    "Are cryptocurrencies legal, and how does their legal status vary by country?",
-    "What safety measures should I consider when investing in cryptocurrencies?",
-    "What are the advantages and disadvantages of investing in cryptocurrencies?",
-    "How can I buy cryptocurrencies, and what are the options available?",
-    "How does the regulatory landscape affect the use and investment in cryptocurrencies?",
-    "What are the risks associated with investing in cryptocurrencies, and how can I mitigate them?",
-    "Can you provide a summary of the key points about cryptocurrency?"
-]
-
-# Print the questions  #sample questions that users might ask
-for i, question in enumerate(questions, 1):
-    print(f"Question {i}: {question}")
 
 # Define a function to get the model's response
 def get_response(context, question):