New version of App

Add the ability for users to select a language model to generate text. Now: a radio button choice of 3 models.

app.py

@@ -2,18 +2,13 @@ import transformers
 from transformers import pipeline
 import gradio as gr
 
-# Available models for pipeline
-# checkpoint = 'wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics'
-checkpoint = 'wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics'
-#checkpoint = 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics'
-# checkpoint = 'wvangils/GPT2-Beatles-Lyrics-finetuned-newlyrics'
-# checkpoint = 'wvangils/DistilGPT2-Beatles-Lyrics-finetuned-newlyrics'
-
-# Create generator 
-generator = pipeline("text-generation", model=checkpoint)
+checkpoint_choices = ['wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics', 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics', 'wvangils/BLOOM-350m-Beatles-Lyrics-finetuned-newlyrics']
 
 # Create function for generation
-def generate_beatles(input_prompt, temperature, top_p):
+def generate_beatles(checkpoint, input_prompt, temperature, top_p):
+    # Create generator for different models
+    generator = pipeline("text-generation", model=checkpoint)
+    
     generated_lyrics = generator(input_prompt
                               , max_length = 100
                               , num_return_sequences = 1
@@ -32,30 +27,25 @@ def generate_beatles(input_prompt, temperature, top_p):
 
 # Create textboxes for input and output
 input_box = gr.Textbox(label="Input prompt:", placeholder="Write the start of a song here", value="In my dreams I am", lines=2, max_lines=5)
-output_box = gr.Textbox(label="Lyrics by The Beatles and GPT:", lines=25)
-
-# Specify examples
-examples = [['In my dreams I am', 0.7], ['I don\'t feel alive when', 0.7]]
+output_box = gr.Textbox(label="Lyrics by The Beatles and chosen language model:", lines=25)
 
 # Layout and text above the App
-title='Beatles lyrics generator based on GPT2'
-description="<p style='text-align: center'>A medium class GPT2 model was fine-tuned on lyrics from The Beatles to generate Beatles-like text. Give it a try!</p>"
-article="""<p style='text-align: left'>A couple of data scientists working for <a href='https://cmotions.nl/' targeet="_blank">Cmotions</a> came together to construct a Language Generation model that will ouput Beatles-like text. 
-        We used several models that were able to load in Colab and choose <a href='https://huggingface.co/gpt2-medium' target='_blank'>GPT2-medium</a> as the winner. 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. <a href='https://www.theanalyticslab.nl/blogs/' target='_blank'>Read this blog </a> to see how this model was build in a Python the notebook using Huggingface.
+title='Beatles lyrics generator'
+description="<p style='text-align: center'>Multiple language models were fine-tuned on lyrics from The Beatles to generate Beatles-like text. Give it a try!</p>"
+article="""<p style='text-align: left'>A couple of data scientists working for <a href='https://cmotions.nl/' target="_blank">Cmotions</a> came together to construct a text generation model that will output Beatles-like text. 
+        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='bigscience/bloom-350m' target='_blank'>BLOOM 350m</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. <a href='https://www.theanalyticslab.nl/blogs/' target='_blank'>Read this blog </a> to see how this model was build in a Python notebook using Huggingface.
         The default output contains 100 tokens and has a repetition penalty of 1.0. 
          </p>"""
 
 # Let users select their own temperature and top-p
 temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Temperature (high = sensitive for low probability tokens)", value=0.7, show_label=True)
 top_p = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Top-p (sample next possible words from given probability p)", value=0.5, show_label=True)
-
-# Can I put examples in an input dropdown box?
-#examples_dropdown = gr.Dropdown(choises=examples, value = 'In my dreams I am', label='Examples', 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]
+             , inputs=[checkpoint, input_box, temperature, top_p]
              , outputs=output_box
              #, examples=examples # output is not very fancy as you have to specify all inputs for every example
              , title=title