Update app.py

app.py

@@ -44,8 +44,8 @@ def sentiment_analysis(text):
     X_input_ids, X_attention_mask = preprocess_input_data(text, arabert_tokenizer)
     preds = model(X_input_ids)
     import numpy as np
-    predicted_class=list(np.where(preds <0.5,0,1).reshape(len(preds),1))
-
+    predicted_classe=list(np.where(preds <0.5,0,1).reshape(len(preds),1))
+    predicted_class = ''.join(str(x) for x in np.where(preds < 0.5, 0, 1).flatten())
     return predicted_class