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")

def summarize(text):
    input_ids = summarization_tokenizer.encode(text, return_tensors="pt")
    output_ids = summarization_model.generate(input_ids)
    output_text = summarization_tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return output_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 model",
    description="Get a summary of your text using the ScienceBrief summarization model.",
    theme="compact"
)

iface_summarize.launch()