app update

app.py

@@ -212,14 +212,14 @@ def generate_wikipedia_outputs(prompt=None, max_new_tokens=2000):
 
 title = "Nano GPT"
 
-description1 = "Nano GPT trained on Shakespeare dataset. It is trained on a very small amount of data to understand how GPT's are trained and built. The implementation can be found <a href='https://github.com/karpathy/nanoGPT'>here.</a>"
+description1 = "Nano GPT trained on <a href='https://www.kaggle.com/datasets/mikeortman/wikipedia-sentences'>Shakespeare dataset</a>. It is trained on a very small amount of data to understand how GPT's are trained and built. The implementation can be found <a href='https://github.com/karpathy/nanoGPT'>here.</a>"
 
 shakespeare_interface = gr.Interface(generate_shakespeare_outputs,
                     inputs=[gr.Textbox(label="Enter any prompt ", type="text", value="Once upon a time,"),
                             gr.Slider(minimum=100, maximum=5000, step=100, value=2000, label="Max new tokens")],
                     outputs=gr.Textbox(label="Output generated", type="text"), description=description1)
 
-description2 = "Nano GPT trained on Wikipedia dataset. It is trained on a very small amount of data to understand how GPT's are trained and built. The implementation can be found <a href='https://github.com/karpathy/nanoGPT'>here.</a>"
+description2 = "Nano GPT trained on <a href='https://github.com/karpathy/char-rnn/blob/6f9487a6fe5b420b7ca9afb0d7c078e37c1d1b4e/data/tinyshakespeare/input.txt'>Wikipedia dataset</a>. It is trained on a very small amount of data to understand how GPT's are trained and built. The implementation can be found <a href='https://github.com/karpathy/nanoGPT'>here.</a>"
 
 wiki_interface = gr.Interface(generate_wikipedia_outputs,
                     inputs=[gr.Textbox(label="Enter any prompt ", type="text", value="James Bond"),