Update app.py

app.py

@@ -1,7 +1,8 @@
 import streamlit as st
 import torch
 from transformers import AutoModel, AutoTokenizer
-import pandas as pd 
+import pandas as pd
+import io
 
 st.set_page_config(page_title="ME2-BERT Moralities", layout="centered")
 
@@ -116,17 +117,17 @@ 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 10 rows.")
+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=' ')
-
-    if len(df) > 10:
-        st.error("The CSV file must have at most 10 rows.")
+    # Controllo numero di righe
+    if len(df) > 50:
+        st.error("The CSV file must have at most 50 rows.")
     else:
         # Controllo esistenza colonna 'text'
         if 'text' not in df.columns:
@@ -134,10 +135,37 @@ if uploaded_file is not None:
         else:
             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.
+                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()
+            
+            # Bottone per il download
+            st.download_button(
+                label="Download predictions as CSV",
+                data=csv_data,
+                file_name="predictions.csv",
+                mime="text/csv"
+            )
+            
+            # Text area per poter copiare il CSV
+            st.text_area("Copy the results as CSV text", value=csv_data, height=200)
 # ---------------------------------------------------------------
 
 st.markdown(