Update app.py

app.py

@@ -30,11 +30,13 @@ desc = """
 It works by first retrieving relevant documents from an external knowledge source (like PDF files) and then using a LLM model to produce responses based on both the input query and the retrieved content.  
 This method enhances factual correctness and allows the model to access up-to-date or domain-specific information without retraining.
 
-Click the button below to load a **User Guide** and an **Installation Guide** for the smoke alarm device into the vector database. 
+Click the button below to load a **User Guide** and an **Installation Guide** for a smoke alarm device into the vector database. 
 Once you see the message *"PDF indexed successfully!"*, go to the **Chatbot** tab to ask any relevant questions about the device.
 
 """
 
+sample_button = "Load User Guide and Installation Guide documents"
+
 
 examples_questions = [["How long is the lifespan of this smoke alarm?"],
             ["How often should I change the battery?"],
@@ -148,7 +150,7 @@ with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
         # pdf_input = gr.Textbox(label="PDF File")
         # index_button = gr.Button("Index PDF")
         # load_sample = gr.Button("Alternatively, Load and Index [Attention Is All You Need.pdf] as a Sample")
-        load_sample = gr.Button("Load and Index the following three papers as a RAG Demo")
+        load_sample = gr.Button(sample_button)
         index_output = gr.Textbox(label="Indexing Status")
         # index_button.click(index_pdf, inputs=pdf_input, outputs=index_output)
         load_sample.click(load_sample_pdf, inputs=None, outputs=index_output)