Update app.py

app.py

@@ -234,11 +234,11 @@ data = {
 # Step 5: Define a Question-Answering Model (Hugging Face)
 qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
 
-# Helper function for answering dataset questions
+        # Helper function for answering dataset questions
 def get_predefined_answer(question):
-    for q, ans in zip(data["Questions"], data["Answers"]):
+    for q, name in zip(data["Questions"], data["Name"]):  # Use "Name" for predefined answers
         if question.lower() in q.lower():
-            return ans
+            return name
     return "Sorry, I don't have an answer for that. Please ask a different question."
 
 # Define answer query function with dataset context
@@ -252,6 +252,7 @@ def answer_query(question):
         response = qa_model(question=question, context=context)
         return response['answer']
 
+
 # Step 6: Create Gradio Interface
 with gr.Blocks(theme=gr.themes.Monochrome()) as app:
     gr.Markdown("## Government Boys Degree College Daulatpur")