Update app.py

app.py

@@ -70,6 +70,7 @@ 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: f'background-color: lightblue' if x != 'O' else '')
     return df
 
 st.title("📘 Named Entity Recognition Wolof")
@@ -96,21 +97,4 @@ if submit_button:
                                             file_name="results.text", mime='text/plain', key='text')
         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[['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.
-        """
-)
+                                            file_name="results.json", mime='application/json