Create app.py

app.py

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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))