app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Loading summarization model
summarization_tokenizer = AutoTokenizer.from_pretrained("ieuniversity/sciencebrief_summarization")
summarization_model = AutoModelForSeq2SeqLM.from_pretrained("ieuniversity/sciencebrief_summarization")
summarizer = pipeline("summarization", tokenizer=summarization_tokenizer, model=summarization_model)

def summarize(text):
    return summarizer(text, max_length=120, min_length=30, do_sample=False)[0]['summary_text']

iface_summarize = gr.Interface(
    fn=summarize,
    inputs=gr.inputs.Textbox(lines=10, label="Input Text"),
    outputs=gr.outputs.Textbox(label="Summary"),
    title="ScienceBrief Summarization",
    description="Get a summary of your text using the ScienceBrief summarization model.",
    theme="compact"
)

iface_summarize.launch()