Update app.py

app.py

@@ -10,22 +10,18 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp
 
 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)
-
+# with open('chapter_titles.pkl', 'rb') as file:
+#     titles_astiko = pickle.load(file)
+labels1 = ["κληρονομικό", "εμπορικό", "διαζύγιο"]
+labels2 = ["αποδοχή κληρονομιάς", "κληρονόμοι", "ιδιόγραφη διαθήκη"]
 
 # titles_astiko = ["γάμος", "αλλοδαπός", "φορολογία", "κληρονομικά", "στέγη", "οικογενειακό", "εμπορικό","κλοπή","απάτη"]
 
 
 def classify(text):
-    output = classifier(text, titles_astiko, multi_label=True)
-    labels = output['labels']
-    scores = output['scores']
-    return_labels = []
-    st.text(scores)
-
-    st.text(len(scores))
-    st.text(len(labels))
+    output = classifier(text, labels1, multi_label=False)
+    output2 = classifier(text, labels2, multi_label=False)
+    
     
     # for i in range(len(scores)):
     #     if scores[i] > 0.99:
@@ -34,9 +30,12 @@ def classify(text):
     #         break
     # # output = output[0:10]
     # return return_labels
-    return labels[0:20]
+    return output, output2
 
 
 text = st.text_input('Enter some text:')  # Input field for new text
 if text:
-    st.text(classify(text))
+    output1 = classify(text)
+    output2 = classify(text)
+    st.text(output1)
+    st.text(output1)