Update app.py

app.py

@@ -88,7 +88,7 @@ Our extensive experiments show that ME²-BERT outperforms existing state-of-the-
 Use this web app app to predict moral foundations (CARE/HARM, FAIRNESS/CHEATING, AUTHORITY/SUBVERSION, LOYALTY/BETRAYAL, PURITY/DEGRADATION) 
 by entering a text or by uploading a csv file with at least a 'text' column. 
 
-The link to the original paper is [here](https://aclanthology.org/2025.coling-main.638.pdf). The source code is available [here](https://github.com/lorenzozangari/ME2-BERT).
+[Paper](https://aclanthology.org/2025.coling-main.638.pdf) | [Source code](https://github.com/lorenzozangari/ME2-BERT)
 """
 )
 
@@ -117,15 +117,13 @@ if st.session_state["prediction_result"]:
     st.write("**Predicted moral foundations:**")
     st.json(st.session_state["prediction_result"])
 
-# SEZIONE AGGIUNTA PER IL FILE CSV (MODIFICHE RICHIESTE)
-# ---------------------------------------------------------------
+
 st.markdown("## CSV File Upload")
 st.write("You can upload a space-separated CSV file with a 'text' column. The file must have at most 50 rows.")
 
 uploaded_file = st.file_uploader("Upload CSV (space-separated)", type=["csv"])
 if uploaded_file is not None:
     df = pd.read_csv(uploaded_file, sep=' ')
-    # Controllo numero di righe
     if len(df) > 50:
         st.error("The CSV file must have at most 50 rows.")
     else:
@@ -136,22 +134,21 @@ if uploaded_file is not None:
             text_list = df['text'].tolist()
             predictions = predict_moralities(text_list)
             
-            # Stampa row-by-row come da codice precedente
+       
             st.write("### Predictions for each row:")
             for i, row_text in enumerate(text_list):
                 st.write(f"Row {i+1}: {row_text}")
                 st.json(predictions[i])
             
-            # Creiamo un DataFrame con text e predizioni per permettere download e copia
             results_for_download = []
             for txt, preds in zip(text_list, predictions):
                 row_data = {"text": txt}
-                row_data.update(preds)  # Aggiunge CARE/HARM, FAIRNESS/CHEATING, ecc.
+                row_data.update(preds)  
                 results_for_download.append(row_data)
             
             df_results = pd.DataFrame(results_for_download)
             
-            # Convertiamo in CSV in memoria
+         
             csv_buffer = io.StringIO()
             df_results.to_csv(csv_buffer, index=False)
             csv_data = csv_buffer.getvalue()