Update app.py

app.py

@@ -168,27 +168,15 @@ if st.button("Results"):
             labels = ["person", "location", "country", "city", "organization", "time", "date", "product", "event name", "money", "affiliation", "ordinal value", "percent value", "position"]
             entities = model.predict_entities(text, labels)
             df = pd.DataFrame(entities)
-            
+            st.dataframe(df)
             properties = {"border": "2px solid gray", "color": "blue", "font-size": "16px"}
             df_styled = df.style.set_properties(**properties)
             st.dataframe(df_styled)
-            if df is not None:
-                value_counts1 = df['label'].value_counts()
-                
-                df1 = pd.DataFrame(value_counts1)
-                
-                final_df = df1.reset_index().rename(columns={"index": "label"})
+            
                 
-            col1, col2 = st.columns(2)
-            with col1:
-                fig1 = px.pie(final_df, values='count', names='label', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted labels')
-                fig1.update_traces(textposition='inside', textinfo='percent+label')
-                st.plotly_chart(fig1)
-            with col2:
-                fig2 = px.bar(final_df, x="count", y="label", color="label", text_auto=True, title='Occurrences of predicted labels')
-                st.plotly_chart(fig2)
-
-                dfa = pd.DataFrame(
+            
+
+            dfa = pd.DataFrame(
                 data={
                     
                     'text': ['entity extracted from file'], 'score': ['accuracy score'], 'label': ['label assigned to the extracted entity'],