Update app.py

app.py

@@ -64,11 +64,6 @@ if submit:
             ]
                 
             print (f'\nresult: {result}')
-            input_column.markdown(f"<small>Compute Finished in {int(time.time() - last_time)} seconds</small>", unsafe_allow_html=True)
-            prediction = np.argmax(result, axis=-1)
-            input_column.success(f"This news is {label[prediction]}.")
-            st.text(f"{int(result[prediction]*100)}% confidence")
-            input_column.progress(result[prediction])
             
         with st.spinner("Searching for references"):
             title_embeddings = base_model.encode(title)
@@ -77,11 +72,17 @@ if submit:
                 data["embeddings"]
             ).flatten()
             sorted = np.argsort(similarity_score)[::-1].tolist()
-            
-            for i in sorted[:5]:
-                reference_column.write(f"""
-                <a href={data["url"][i]}><small>turnbackhoax.id</small></a>
-                <h5>{data["title"][i]}</h5>
-                """, unsafe_allow_html=True)
-                with reference_column.beta_expander("read content"):
-                    st.write(data["text"][i])
+                    
+        input_column.markdown(f"<small>Compute Finished in {int(time.time() - last_time)} seconds</small>", unsafe_allow_html=True)
+        prediction = np.argmax(result, axis=-1)
+        input_column.success(f"This news is {label[prediction]}.")
+        input_column.text(f"{int(result[prediction]*100)}% confidence")
+        input_column.progress(result[prediction])
+        
+        for i in sorted[:5]:
+            reference_column.write(f"""
+            <a href={data["url"][i]}><small>turnbackhoax.id</small></a>
+            <h5>{data["title"][i]}</h5>
+            """, unsafe_allow_html=True)
+            with reference_column.expander("read content"):
+                st.write(data["text"][i])