Update app.py

app.py

@@ -10,19 +10,6 @@ Original file is located at
 
 Put together a Gradio App bases on the Huggingface tutorials so that users can generate Beatles-like poetry based on an input prompt.
 """
-
-# Commented out IPython magic to ensure Python compatibility.
-# %%capture
-# !pip install transformers
-
-# Commented out IPython magic to ensure Python compatibility.
-# %%capture
-# !pip install gradio
-
-# Commented out IPython magic to ensure Python compatibility.
-# %%capture
-# !pip install --upgrade diffusers transformers scipy ftfy "ipywidgets>=7,<8"
-
 # Import libraries
 from transformers import pipeline
 from numpy import random
@@ -98,52 +85,4 @@ def generate_beatles(input_prompt, temperature, top_p, given_input_style):
 
     # Generate the image
     image = get_image(image_input)
-    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 beyond the Broad Horizon", lines=2, max_lines=5)
-gen_lyrics = gr.Textbox(label="Proposed song lyrics", lines=15)
-gen_title = gr.Textbox(label="Proposed song title", lines=1)
-gen_image = gr.Gallery(label="Proposed song cover").style(grid=1, height="auto")
-gen_image_style = gr.Textbox(label="Image style", lines=1)
-
-# Layout and text around the app
-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.
-        The default output contains 100 tokens and has a repetition penalty of 1.0. 
-         </p>"""
-css = """
-        .gr-button-primary {
-          text-indent: -9999px;
-          line-height: 0;
-        }  
-        .gr-button-primary:after {
-          content: "Beatlify!";
-          text-indent: 0;
-          display: block;
-          line-height: initial;
-        }
-""" 
-
-# Let users select their own temperature and top-p
-temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Change the temperature \r\n (higher temperature = more creative in lyrics generation, but posibbly less Beatly)", value=0.7, show_label=True)  #high = sensitive for low probability tokens
-top_p = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Change top probability of the next word \n (higher top probability = more words to choose from for the next word, but possibly less Beatly)", value=0.5, show_label=True)
-given_input_style = gr.Dropdown(choices=image_input_styles, value="Random", label="Choose the art style for the song cover", show_label=True)
-#checkpoint = gr.Radio(checkpoint_choices, value='wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics', interactive=True, label = 'Select fine-tuned model', show_label=True)
-
-# Use generate Beatles function in demo-app Gradio
-gr.Interface(fn=generate_beatles
-             , inputs=[input_box, temperature, top_p, given_input_style]
-             , outputs=[gen_title, gen_lyrics, gen_image, gen_image_style]
-             , title=title
-             , css=css
-             , description=description
-             , article=article
-             , allow_flagging='never'
-             ).launch()
-
-# Uncomment the line below for testing
-#generate_beatles(input_prompt="When I look out my window", temperature=0.7, top_p=0.5)
+    return (title, generated_lyrics, image, image_style)