Update app.py

app.py

@@ -4,20 +4,20 @@ 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)
+# 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):
-    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()
-    prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
-    return prediction
+    output = classifier(text, label_names, multi_label=True)
+    return output
 
 
 text = st.text_input('Enter some text:')  # Input field for new text