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Create app.py
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app.py
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from transformers import pipeline
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import gradio as gr
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# Available models for pipeline
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# checkpoint = 'wvangils/CTRL-Beatles-Lyrics-finetuned-newlyrics'
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checkpoint = 'wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics'
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# checkpoint = 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics'
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# checkpoint = 'wvangils/GPT2-Beatles-Lyrics-finetuned-newlyrics'
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# checkpoint = 'wvangils/DistilGPT2-Beatles-Lyrics-finetuned-newlyrics'
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# Create generator
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generator = pipeline("text-generation", model=checkpoint)
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# Create function for generation
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def generate_beatles(input_prompt, temperature):
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generated_lyrics = generator(input_prompt
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, max_length = 100
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, num_return_sequences = 1
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, return_full_text = True
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, verbose = 0
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#, num_beams = 1
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#, early_stopping = True # Werkt niet goed lijkt
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, temperature = temperature # Default 1.0 # Randomness, temperature = 1 minst risicovol, 0 meest risicovol
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#, top_k = 50 # Default 50
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, top_p = 0.5 # Default 1.0
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, no_repeat_ngram_size = 3 # Default = 0
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, repetition_penalty = 1.0 # Default = 1.0
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#, do_sample = True # Default = False
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)[0]["generated_text"]
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return generated_lyrics
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# Create textboxes for input and output
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input_box = gr.Textbox(label="Input prompt:", placeholder="Write the start of a song here", lines=2)
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output_box = gr.Textbox(label="Lyrics by The Beatles and GPT:", lines=20)
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examples = [['In my dream I am', 0.7], ['I don\'t feel alive', 0.7]]
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title='Beatles lyrics generator based on GPT2'
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description='A medium class GPT2 model was fine-tuned on lyrics from The Beatles to generate Beatles-like text. Give it a try!'
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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)
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# Use generate Beatles function in demo-app Gradio
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gr.Interface(fn=generate_beatles
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, inputs=[input_box, temperature]
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, outputs=output_box
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, examples=examples
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, title=title
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, description=description
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).launch()
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