import transformers
from transformers import pipeline
import gradio as gr

checkpoint_choices = ['wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics', 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics', 'wvangils/BLOOM-560m-Beatles-Lyrics-finetuned']

# Create function for generation
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
                              , return_full_text = True
                              , verbose = 0
                              #, num_beams = 1
                              #, early_stopping = True    # Werkt niet goed lijkt 
                              , temperature = temperature    # Default 1.0    # Randomness, temperature = 1 minst risicovol, 0 meest risicovol
                              #, top_k = 50     # Default 50
                              , top_p = top_p    # Default 1.0
                              , no_repeat_ngram_size = 3   # Default = 0
                              , repetition_penalty = 1.0   # Default = 1.0
                              #, do_sample = True     # Default = False
                              )[0]["generated_text"]
    return generated_lyrics

# 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 chosen language model:", lines=25)

# Layout and text above the App
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'>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>"""

# 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)
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=[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
             , description=description
             , article=article
             , allow_flagging='never'
             ).launch()