app.py

from pathlib import Path
from typing import List, Dict, Tuple

import seaborn as sns
from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TextClassificationPipeline

sns.set_theme()

www_dir = Path(__file__).parent.resolve() / "www"

def server(input: Inputs, output: Outputs, session: Session):
    model_name = 'lincoln/flaubert-mlsum-topic-classification'
    
    loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
    loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)
    
    nlp = TextClassificationPipeline(model=loaded_model, tokenizer=loaded_tokenizer)
    result = nlp(Inputs, truncation=True)
    return result


app = App(
    Inputs = "Le Bayern Munich prend la grenadine.",
    server,
    static_assets=str(www_dir),
)