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import gradio as gr |
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from happytransformer import HappyGeneration |
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happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/GPT-Greentext-1.5b") |
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from happytransformer import GENSettings |
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def generate(text, length=100, penalty=3, temperature=0.8): |
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args_top_k = GENSettings(no_repeat_ngram_size=penalty, do_sample=True, top_k=80, temperature=temperature, max_length=length, early_stopping=False) |
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inputText = "Write a greentext from 4chan.org. The story should be like a bullet-point list using > as the start of each line. It must be funny and have a twist near the end.\n" |
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inputText += ">" + text + "\n>" |
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print(inputText) |
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result = happy_gen.generate_text(inputText, args=args_top_k) |
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generated_text = result.text |
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generated_text = generated_text.replace('\\n', '\n') |
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generated_text = generated_text.replace('>', '\n>') |
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generated_text = generated_text.replace('\\"', "\"") |
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generated_text = ">" + text + "\n>" + generated_text |
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return generated_text |
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examples = [ |
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["be me"], |
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["be going to heaven"], |
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["be a plague doctor"] |
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] |
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demo = gr.Interface( |
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fn=generate, |
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inputs=[ |
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gr.inputs.Textbox(lines=5, label="Input Text"), |
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gr.inputs.Slider(5, 200, label='Length', default=100, step=5), |
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gr.inputs.Slider(1, 10, label='no repeat ngram size', default=2, step=1), |
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gr.inputs.Slider(0.0, 1.0, label='Temperature - control randomness', default=0.6, step=0.1) |
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], |
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outputs=gr.outputs.Textbox(label="Generated Text"), |
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examples=examples, |
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title="GPT-Greentext Playground", |
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description="Using the 1.5b size model. You may need to run it a few times in order to get something good!" |
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) |
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demo.launch() |
