Update app.py

app.py

@@ -30,7 +30,10 @@ examples_questions = [["What is Transformer?"],
             ["What are Encoder and Decoder?"],
             ["Describe more about the Transformer."],
             ["Why use self-attention?"],
-           ]
+            ["Describe Parameter-Efficient fine-tuning?"],
+            ["Describe Generative Adversarial Networks?"],                      
+            ["How does GAN work?"]
+        ]
 
 template = \
 """Use the following pieces of context to answer the question at the end.
@@ -114,16 +117,7 @@ def generate_response(query, history, model, temperature, max_tokens, top_p, see
     return response
 
 
-# gr.Markdown("""
-# ### 1. Attention Is All You Need (Vaswani et al., 2017)
-# This groundbreaking paper introduced the **Transformer** architecture. It revolutionized natural language processing by enabling parallelization and significantly improving performance on tasks like translation, leading to models like *BERT* and *GPT*.
 
-# ### 2. Generative Adversarial Nets (Goodfellow et al., 2014)
-# This paper proposed **GANs**, a novel framework for generative modeling using two neural networks—a generator and a discriminator—that compete in a zero-sum game.  
-
-# ### 3. Parameter-Efficient Transfer Learning for NLP (Houlsby et al., 2019)
-# This paper introduces **adapter modules**, a method for fine-tuning large pre-trained language models with significantly fewer parameters.  
-# """)
 
 
 additional_inputs = [
@@ -141,8 +135,17 @@ 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 [Attention Is All You Need.pdf] as a Sample")
-        sample_description = gr.Markdown("This sample PDF is a seminal paper in the field of machine learning, titled 'Attention Is All You Need' at https://arxiv.org/abs/1706.03762. It introduces the Transformer model, which has become foundational in natural language processing.")
+        load_sample = gr.Button("Load and Index the following three papers as a RAG Demo")
+        sample_description = gr.Markdown("""
+## 1. Attention Is All You Need (Vaswani et al., 2017)
+This groundbreaking paper introduced the **Transformer** architecture. It revolutionized natural language processing by enabling parallelization and significantly improving performance on tasks like translation, leading to models like *BERT* and *GPT*.
+## 2. Generative Adversarial Nets (Goodfellow et al., 2014)
+This paper proposed **GANs**, a novel framework for generative modeling using two neural networks—a generator and a discriminator—that compete in a zero-sum game.  
+## 3. Parameter-Efficient Transfer Learning for NLP (Houlsby et al., 2019)
+This paper introduces **adapter modules**, a method for fine-tuning large pre-trained language models with significantly fewer parameters.
+
+It could take several minutes to load and index the files.
+""")
         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)