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import os |
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os.system("pip install transformers") |
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os.system("pip install gradio") |
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os.system("pip install tensorflow") |
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import gradio as gr |
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import tensorflow as tf |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer, set_seed |
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large") |
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model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id) |
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def generate(prompt,textCount): |
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input_ids = tokenizer.encode(prompt, return_tensors='pt') |
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output = model.generate(input_ids, max_length=textCount, num_beams=5, no_repeat_ngram_size=2, early_stopping=True) |
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out = tokenizer.decode(output[0], skip_special_tokens=True) |
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return out |
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demo = gr.Interface( |
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fn=generate, |
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inputs=[gr.Textbox(lines=8, placeholder="Paragraph Here..."),"number"], |
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outputs="text",title="Text generation app with GPT2", |
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description="This is a text generation app, it can prove useful when you want to generate texts. All you need to do is copy and paste a short prompt . ", |
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examples=[ |
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["During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in" |
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],["The bald eagle is"] |
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], |
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) |
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demo.launch() |
