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from transformers import pipeline
import gradio as gr
MODELS = {
"gsarti": pipeline("summarization", model="gsarti/it5-base-wiki-summarization"),
"facebook": pipeline("summarization", model="facebook/bart-large-cnn"),
"lincoln": pipeline(
"summarization", model="lincoln/mbart-mlsum-automatic-summarization"
),
"t5-small": pipeline("summarization", model="t5-small"),
}
def predict(prompt, model_name, max_length):
if model_name is None:
model = MODELS["t5-small"]
else:
model = MODELS[model_name]
prompt = prompt.replace("\n", " ")
summary = model(prompt, max_length)[0]["summary_text"]
return summary
options_1 = list(MODELS.keys())
with gr.Blocks() as demo:
drop_down = gr.Dropdown(choices=options_1, label="model")
textbox = gr.Textbox(placeholder="Enter text block to summarize", lines=4)
length = gr.Number(value=100, label="the max number of characher for summerized")
gr.Interface(fn=predict, inputs=[textbox, drop_down, length], outputs="text")
demo.launch()
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