empty.container

app.py

@@ -210,42 +210,42 @@ with st.expander('Adjust your tone intensities for your preference: '):
 
 Loxz_recom_box = st.checkbox('Select Loxz Recommended Tones for Optimal Output')
 
-if st.button('Generate Predictions'):
-    start_time = time.time() 
-    if input_text == "":
-        st.error('Please enter a sentence!')
-    else:
-        placeholder = st.empty()
-        placeholder.text('Loading Data...')
-
-        # Starting predictions
-        bucket='emailcampaignmodeldata'
-        # file_key = 'fullEmailBody/fullemailtextbody_labeled_3rates_8tones_20220524.csv'
-
-        # email_data = utils.get_files_from_aws(bucket,file_key)
-        tone_key = 'ModelSADataSets/Tone_and_target.csv'
-        tone_data = FunctionsModelSA_V1.load_data()
-        test_predictions,tones = FunctionsModelSA_V1.convert_text_to_tone(input_text)
-
-        # st.dataframe(test_predictions)
-        # st.dataframe(tones)
-        campaign_val='campaign_type_'+ campaign
-        industry_val='industry_'+ industry
-        pred,lower,upper,model = FunctionsModelSA_V1.prediction(tones,campaign_val,industry_val,target)
-        worst_target,best_target,best_target_tones = FunctionsModelSA_V1.find_max_cat(tone_data,target,industry_val,campaign_val)
-        #best_target, best_target_tones
-        #FunctionsModelSA_V1.plot_CI(pred,lower,upper)
-        st.write('The Predicted Value is ' + str(pred))
-        fig1 = go.Figure(go.Bar(
-            name = 'Tone Levels',
-            x=[joyful_tone_value, confident_tone_value, urgent_tone_value, friendly_tone_value, optimistic_tone_value, analytical_tone_value, casual_tone_value],
-            y=['Joyful', 'Confident', 'Urgent', 'Friendly', 'Optimistic', 'Analytical', 'Casual'],
-            orientation='h')
-        )
-        st.plotly_chart(fig1, use_container_width=True)
-
-        #if((best_target!=0) and (pred<best_target)):
-        with st.empty():
+with st.empty().container():
+    if st.button('Generate Predictions'):
+        start_time = time.time() 
+        if input_text == "":
+            st.error('Please enter a sentence!')
+        else:
+            placeholder = st.empty()
+            placeholder.text('Loading Data...')
+
+            # Starting predictions
+            bucket='emailcampaignmodeldata'
+            # file_key = 'fullEmailBody/fullemailtextbody_labeled_3rates_8tones_20220524.csv'
+
+            # email_data = utils.get_files_from_aws(bucket,file_key)
+            tone_key = 'ModelSADataSets/Tone_and_target.csv'
+            tone_data = FunctionsModelSA_V1.load_data()
+            test_predictions,tones = FunctionsModelSA_V1.convert_text_to_tone(input_text)
+
+            # st.dataframe(test_predictions)
+            # st.dataframe(tones)
+            campaign_val='campaign_type_'+ campaign
+            industry_val='industry_'+ industry
+            pred,lower,upper,model = FunctionsModelSA_V1.prediction(tones,campaign_val,industry_val,target)
+            worst_target,best_target,best_target_tones = FunctionsModelSA_V1.find_max_cat(tone_data,target,industry_val,campaign_val)
+            #best_target, best_target_tones
+            #FunctionsModelSA_V1.plot_CI(pred,lower,upper)
+            st.write('The Predicted Value is ' + str(pred))
+            fig1 = go.Figure(go.Bar(
+                name = 'Tone Levels',
+                x=[joyful_tone_value, confident_tone_value, urgent_tone_value, friendly_tone_value, optimistic_tone_value, analytical_tone_value, casual_tone_value],
+                y=['Joyful', 'Confident', 'Urgent', 'Friendly', 'Optimistic', 'Analytical', 'Casual'],
+                orientation='h')
+            )
+            st.plotly_chart(fig1, use_container_width=True)
+
+            #if((best_target!=0) and (pred<best_target)):
             if Loxz_recom_box == True:
                 recommended_changes=(best_target_tones)   
                 change=best_target-pred
@@ -275,7 +275,8 @@ if st.button('Generate Predictions'):
                 fig2.update_layout(title_text=f'The following Changes will yield a {out} increase in {target}')
                 st.plotly_chart(fig2, use_container_width=True)
                 #FunctionsModelSA_V1.corrections(best_target_tones,test_predictions))
-            
-        placeholder.empty()
+                
+            placeholder.empty()
+
+    WHITE_SPACE(10)
 
-WHITE_SPACE(10)