Update app.py

app.py

@@ -28,8 +28,8 @@ def get_image_input(title, given_input_style):
 
 # Available models for generate lyrics pipeline
 # checkpoint = 'wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics'
-# checkpoint = 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics'
-checkpoint = 'wvangils/BLOOM-560m-Beatles-Lyrics-finetuned'
+checkpoint = 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics'
+# checkpoint = 'wvangils/BLOOM-560m-Beatles-Lyrics-finetuned'
 
 # Setup all the pipelines we need
 title_generator = pipeline('summarization', model='czearing/story-to-title')
@@ -72,7 +72,7 @@ def generate_beatles(input_prompt, temperature, top_p, given_input_style):
     return (title, generated_lyrics, image, image_style)
     
 # Create textboxes for input and output
-input_box = gr.Textbox(label="Write the start of a song here", placeholder="Write the start of a new song here", value="Looking out of my window", lines=2, max_lines=5)
+input_box = gr.Textbox(label="Write the start of a song here", placeholder="Write the start of a new song here", value="It's been three days ", lines=2, max_lines=5)
 gen_lyrics = gr.Textbox(label="Song lyrics", lines=15)
 gen_title = gr.Textbox(label="Proposed songtitle", lines=1)
 gen_image = gr.Gallery(label="Proposed song cover").style(grid=1, height="auto")
@@ -83,7 +83,7 @@ title='Beatles lyrics generator'
 description="<p style='text-align: center'>We've fine-tuned multiple language models on lyrics from The Beatles to generate Beatles-like text. Below are the results we obtained fine-tuning a GPT Neo model. After generation a title is generated using <a href='https://huggingface.co/czearing/story-to-title' target='_blank'>this model</a>. On top we use the generated title to suggest an album cover using <a href='https://huggingface.co/CompVis/stable-diffusion-v1-4' target='_blank'>Stable Diffusion 1.4</a>. Give it a try!</p>"
 article="""<p style='text-align: left'>These text generation models that output Beatles-like text were created by data scientists working for <a href='https://cmotions.nl/' target="_blank">Cmotions.</a>  
         We tried several text generation models that we were able to load in Colab: a general <a href='https://huggingface.co/gpt2-medium' target='_blank'>GPT2-medium</a> model, the Eleuther AI small-sized GPT model <a href='https://huggingface.co/EleutherAI/gpt-neo-125M' target='_blank'>GPT-Neo</a> and the new kid on the block build by the <a href='https://bigscience.notion.site/BLOOM-BigScience-176B-Model-ad073ca07cdf479398d5f95d88e218c4' target='_blank'>Bigscience</a> initiative <a href='https://huggingface.co/bigscience/bloom-560m' target='_blank'>BLOOM 560m</a>. 
-        Further we've put together a <a href='https://huggingface.co/datasets/cmotions/Beatles_lyrics' target='_blank'> Huggingface dataset</a> containing all known lyrics created by The Beatles. Currently we are fine-tuning models and are evaluating the results. Once finished we will publish a blog at this <a href='https://www.theanalyticslab.nl/blogs/' target='_blank'>location </a> with all the steps we took including a Python notebook using Huggingface.
+        Further we've put together a <a href='https://huggingface.co/datasets/cmotions/Beatles_lyrics' target='_blank'> Huggingface dataset</a> containing all known lyrics created by The Beatles. Keep an eye on this <a href='https://www.theanalyticslab.nl/blogs/' target='_blank'>location </a> where we will publish several blogs on the creation of these models and their evaluation.
         The default output contains 100 tokens and has a repetition penalty of 1.0. 
          </p>"""
 css = """