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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
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)
def classify(text)
input = tokenizer(text, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["θυμός", "χαρά", "λύπη"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
return prediction
text = st.text_input('Enter some text:') # Input field for new text
if text:
st.text(classify(text)) |