Update pages/1_Earnings_Sentiment_Analysis_📈_.py

pages/1_Earnings_Sentiment_Analysis_📈_.py

@@ -49,14 +49,13 @@ if st.session_state.url or st.session_state.upload:
             l=5,
             r=5,
             b=5,
-            t=5,
+            t=10,
             pad=2
         )
     )
     
-    col1, col2 = st.columns(2)
     
-    col1.plotly_chart(fig)
+    st.plotly_chart(fig)
     
     ## Display sentiment score
     pos_perc = grouped[grouped['sentiment']=='Positive']['count'].iloc[0]*100/sen_df.shape[0]
@@ -65,14 +64,14 @@ if st.session_state.url or st.session_state.upload:
     
     sentiment_score = neu_perc+pos_perc-neg_perc
     
-    fig = go.Figure()
+    fig_1 = go.Figure()
     
-    fig.add_trace(go.Indicator(
+    fig_1.add_trace(go.Indicator(
         mode = "delta",
         value = sentiment_score,
         domain = {'row': 1, 'column': 1}))
     
-    fig.update_layout(
+    fig_1.update_layout(
     	template = {'data' : {'indicator': [{
             'title': {'text': "Sentiment score"},
             'mode' : "number+delta+gauge",
@@ -89,7 +88,9 @@ if st.session_state.url or st.session_state.upload:
         )
     )
     
-    col2.plotly_chart(fig)
+    with st.sidebar:
+    
+        st.plotly_chart(fig_1)
     
     ## Display negative sentence locations
     fig = px.scatter(sen_df, y='label', color='label', size='score', hover_data=['text'], color_discrete_map={"Negative":"firebrick","Neutral":"navajowhite","Positive":"darkgreen"}, title='Sentiment Score Distribution')