Update app.py

app.py

@@ -7,11 +7,17 @@ model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
+with open('articles_list.pkl', 'rb') as file:
+    articles_list = pickle.load(file)
+    
+label_names = []
+for i in articles_list:
+    label_names.append(i[0:15])
+
 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