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app.py
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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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# generate text until the output length (which includes the context length) reaches 50
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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()
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