app.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
import pickle
import streamlit as st
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

# model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli"
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# model = AutoModelForSequenceClassification.from_pretrained(model_name)

classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")



label_names = ["γάμος", "αλλοδαπός", "φορολογία", "κληρονομικά", "στέγη", "οικογενειακό", "εμπορικό","κλοπή","απάτη"]


def classify(text):
    output = classifier(text, label_names, multi_label=True)
    return output


text = st.text_input('Enter some text:')  # Input field for new text
if text:
    st.text(classify(text))