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  1. DESCRIPTION.md +1 -0
  2. README.md +1 -1
  3. app.py +0 -10
DESCRIPTION.md ADDED
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+ This text generation demo takes in input text and returns generated text. It uses the Transformers library to set up the model and has two examples.
README.md CHANGED
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  ---
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  title: text_generation
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- emoji: 🤗
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  colorFrom: indigo
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  colorTo: indigo
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  sdk: gradio
 
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  ---
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  title: text_generation
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+ emoji: 🔥
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  colorFrom: indigo
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  colorTo: indigo
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  sdk: gradio
app.py CHANGED
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- # URL: https://huggingface.co/spaces/gradio/text_generation
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- # DESCRIPTION: This text generation demo takes in input text and returns generated text. It uses the Transformers library to set up the model and has two examples.
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- # imports
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  import gradio as gr
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  from transformers import pipeline
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- # loading the model
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  generator = pipeline('text-generation', model='gpt2')
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-
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- # defining the core function
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  def generate(text):
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  result = generator(text, max_length=30, num_return_sequences=1)
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  return result[0]["generated_text"]
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-
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- # defining examples
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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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  examples=examples
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  )
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- # launching
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  demo.launch()
 
 
 
 
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  import gradio as gr
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  from transformers import pipeline
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  generator = pipeline('text-generation', model='gpt2')
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  def generate(text):
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  result = generator(text, max_length=30, num_return_sequences=1)
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  return result[0]["generated_text"]
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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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  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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  examples=examples
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  )
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  demo.launch()