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) # Create function for generation def generate_beatles(input_prompt, temperature): 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 = 0.5 # 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", lines=2) output_box = gr.Textbox(label="Lyrics by The Beatles and GPT:", lines=20) examples = [['In my dream I am', 0.7], ['I don\'t feel alive', 0.7]] title='Beatles lyrics generator based on GPT2' description='A medium class GPT2 model was fine-tuned on lyrics from The Beatles to generate Beatles-like text. Give it a try!' temperature = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Temperature (high = sensitive for low probability tokens)", value=0.7, show_label=True) # Use generate Beatles function in demo-app Gradio gr.Interface(fn=generate_beatles , inputs=[input_box, temperature] , outputs=output_box , examples=examples , title=title , description=description ).launch()