app.py

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

tokenizer = AutoTokenizer.from_pretrained("human-centered-summarization/financial-summarization-pegasus")
model = AutoModelForSeq2SeqLM.from_pretrained("human-centered-summarization/financial-summarization-pegasus")

pipe = pipeline(task="text2text-generation", model=model, tokenizer=tokenizer)

with open('text1.txt') as f:
    text1 = f.read()

with open('text2.txt') as f:
    text2 = f.read()

with open('text3.txt') as f:
    text3 = f.read()

gr.Interface.from_pipeline(pipe, 
                    title="Financial Summarization",
                    description="Financial Summarization using google/pegasus-xsum fine-tuned on financial news dataset. Model can be found at https://huggingface.co/human-centered-summarization/financial-summarization-pegasus. Examples are news articles from business.inquirer.net.",
                    examples=[text1,text2,text3]
                    ).launch()