app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Loading translation model
tokenizer = AutoTokenizer.from_pretrained("ieuniversity/sciencebrief_translation")
model = AutoModelForSeq2SeqLM.from_pretrained("ieuniversity/sciencebrief_translation")

# Loading summarization model

summarization_tokenizer = AutoTokenizer.from_pretrained("ieuniversity/sciencebrief_summarization")
summarization_model = AutoModelForSeq2SeqLM.from_pretrained("ieuniversity/sciencebrief_summarization")



#translation function
def translate_text(text):
    input_ids = tokenizer.encode(text, return_tensors="pt")
    outputs = model.generate(input_ids)
    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return decoded_output

#summarization function
def summarize_text(text):
    input_ids = summarization_tokenizer.encode(text, return_tensors="pt")
    output_ids = summarization_model.generate(input_ids)
    summary = summarization_tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return summary

# Building the gradio interface
input_text = gr.inputs.Textbox(label="Input Text")
summarization_output = gr.outputs.Textbox(label="Summarization Output")
translation_output = gr.outputs.Textbox(label="Translation Output")

gr.Interface(
    fn=[summarize_text, translate],
    inputs=input_text,
    outputs=[summarization_output, translation_output],
    layout="vertical",
    title="Scientific Papers Text Summarization and Translation",
    description="Enter some text and get a summary and translation in Spanish."
).launch()