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import gradio as gr
#def greet(name):
# return "Hello " + name + "!!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()
#git clone https://github.com/huggingface/transformers.git
#cd transformers
#pip install
#python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"
#pip install 'transformers[torch]'
#source .env/bin/activate
#pipenv install git+https://github.com/huggingface/transformers
#python -m pip install git+https://github.com/huggingface/transformers
#source ENV/bin/activate
#python -m venv .env
#pip install transformers
#pip install transformers
#pip install -U git+https://github.com/huggingface/transformers.git
#! pip install -U git+https://github.com/huggingface/accelerate.git
from transformers import pipeline
get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
def summarize(input):
output = get_completion(input)
return output[0]['summary_text']
gr.close_all()
demo = gr.Interface(fn=summarize,
inputs=[gr.Textbox(label="Text to summarize", lines=6)],
outputs=[gr.Textbox(label="Result", lines=3)],
title="Text summarization with distilbart-cnn",
description="Summarize any text using the `shleifer/distilbart-cnn-12-6` model under the hood!"
)
demo.launch(share=True)
#demo.launch(share=True, server_port=int(os.environ['PORT2'])) |