Update app.py

app.py

@@ -70,7 +70,6 @@ def tag_sentence(text):
     predictions = predict_ner_labels(model, tokenizer, text)
     # Créez un DataFrame avec les colonnes "words" et "tags"
     df = pd.DataFrame({'words': text.split(), 'tags': predictions})
-    df['tags'] = df['tags'].map(lambda x: 'background-color: lightblue' if x != 'O' else '')
     return df
 
 st.title("📘 Named Entity Recognition Wolof")
@@ -98,23 +97,20 @@ if submit_button:
         with c3:
             jsonbutton = st.download_button(label="📥 Download .json", data=convert_json(results),
                                             file_name="results.json", mime='application/json', key='json')
-
         st.header("")
-
         c1, c2, c3 = st.columns([1, 3, 1])
-
         with c2:
-            st.table(results.style.format(precision=2))
+            st.table(results[['words', 'tags']])
 
 st.header("")
 st.header("")
 st.header("")
 with st.expander("ℹ️ - About this app", expanded=True):
     st.write(
-        """     
+        """
 -   The **Named Entity Recognition Wolof** app is a tool that performs named entity recognition in Wolof.
 -   The available entities are: *corporation*, *location*, *person*, and *date*.
 -   The app uses the [XLMRoberta model](https://huggingface.co/xlm-roberta-base), fine-tuned on the [masakhaNER](https://huggingface.co/datasets/masakhane/masakhaner2) dataset.
 -   The model uses the **byte-level BPE tokenizer**. Each sentence is first tokenized.
         """
-)   
+)