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")

# with open('chapter_titles.pkl', 'rb') as file:
#     titles_astiko = pickle.load(file)
labels1 = ["κληρονομικό", "ακίνητα", "διαζύγιο"]
labels2 = ["αποδοχή κληρονομιάς", "αποποίηση", "διαθήκη"]
labels3 = ["μίσθωση", "κυριότητα", "έξωση", "απλήρωτα νοίκια"]



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


def classify(text):
    output = classifier(text, labels1, multi_label=False)
    output2 = classifier(text, labels2, multi_label=False)
    output3 = classifier(text, labels3, multi_label=False)
    
    
    # for i in range(len(scores)):
    #     if scores[i] > 0.99:
    #         return_labels.append(labels[i])
    #     else:
    #         break
    # # output = output[0:10]
    # return return_labels
    return output, output2, output3


text = st.text_input('Enter some text:')  # Input field for new text
if text:
    output1, output2, output3 = classify(text)
    st.text(output1)
    st.text(output3)
    st.text(output2)