Update app.py

app.py

@@ -65,22 +65,23 @@ if submit:
                 
             print (f'\nresult: {result}')
             
+            title_embeddings = base_model.encode(title)
+            similarity_score = cosine_similarity(
+                [title_embeddings],
+                data["embeddings"]
+            ).flatten()
+            sorted = np.argsort(similarity_score)[::-1].tolist()
+            
             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])
             
-        title_embeddings = base_model.encode(title)
-        similarity_score = cosine_similarity(
-            [title_embeddings],
-            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.expander("read content"):
-                st.write(data["text"][i])
+            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])