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app.py
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# URL: https://huggingface.co/spaces/gradio/text_generation
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# imports
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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# loading the model
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
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model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
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# defining the core function
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def generate(text):
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generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
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result = generation_pipeline(text)
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return result[0]["generated_text"]
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# defining title, description and examples
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title = "Text Generation with GPT-J-6B"
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description = "This demo generates text using GPT-J 6B: a transformer model trained using Ben Wang's Mesh Transformer JAX."
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examples = [
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["The Moon's orbit around Earth has"],
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["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
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]
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# defining the interface
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demo = gr.Interface(
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fn=generate,
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inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
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outputs=gr.outputs.Textbox(label="Generated Text"),
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title=title,
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description=description,
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examples=examples,
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)
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# launching
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demo.launch()
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