Update app.py

app.py

@@ -144,22 +144,22 @@ if st.button('predict'):
                 output.append(None)
             
     
-            if CFG.num_beams > 1:
-                output_df = pd.DataFrame(np.array(output).reshape(1, -1), columns=['input'] + [f'{i}th' for i in range(CFG.num_beams)] + ['valid compound'] + [f'{i}th score' for i in range(CFG.num_beams)] + ['valid compound score'])
-            else:
-                output_df = pd.DataFrame(np.array([input_compound]+output).reshape(1, -1), columns=['input', '0th', 'valid compound'])
-            st.table(output_df)
-    
-            @st.cache
-            def convert_df(df):
-                # IMPORTANT: Cache the conversion to prevent computation on every rerun
-                return df.to_csv(index=False)
-    
-            csv = convert_df(output_df)
-            
-            st.download_button(
-                label="Download data as CSV",
-                data=csv,
-                file_name='output.csv',
-                mime='text/csv',
-            )
+        if CFG.num_beams > 1:
+            output_df = pd.DataFrame(np.array(output).reshape(1, -1), columns=['input'] + [f'{i}th' for i in range(CFG.num_beams)] + ['valid compound'] + [f'{i}th score' for i in range(CFG.num_beams)] + ['valid compound score'])
+        else:
+            output_df = pd.DataFrame(np.array([input_compound]+output).reshape(1, -1), columns=['input', '0th', 'valid compound'])
+        st.table(output_df)
+
+        @st.cache
+        def convert_df(df):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return df.to_csv(index=False)
+
+        csv = convert_df(output_df)
+        
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name='output.csv',
+            mime='text/csv',
+        )