col 2

app.py

@@ -122,7 +122,7 @@ region_variables = ['Africa', 'America', 'Asia', 'Europe', 'Oceania']
 st.markdown("#### Sentiment Analysis: Email Industry v1.2")
 
 stats_col1, stats_col2, stats_col3, stats_col4 = st.columns([1,1,1,1])
-'''
+
 with stats_col1:
     st.caption("Verified: Production")
     #st.metric(label="Verified", value= "Production")
@@ -136,8 +136,8 @@ with stats_col3:
 with stats_col4:
     st.caption("Industry: Email")
 #st.metric(label="Industry", value="Email")
-'''
-coluno, coldos = st.columns([5, 1])
+
+coluno, coldos = st.columns([5, 3])
 
 with coluno:
     input_text = st.text_area("Please enter your email text here: ", height=50)
@@ -220,67 +220,67 @@ with coluno:
         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
-                #recommend(tones,recommended_changes,change,target)
-
-                fig2 = go.Figure()
-                fig2.add_trace(go.Bar(
-                    y=tone_labels,
-                    x=recommended_changes,
-                    name='Recommend changes',
-                    orientation='h',
-                    text=np.round(recommended_changes,3),
-                    width=.5,
-                    marker=dict(
-                    color='#e60f00',
-                    line=dict(color='rgba(58, 71, 80, 1.0)', width=1)))
+            with coldos:
+                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')
                 )
-                fig2.update_traces(textfont_size=18, textposition="outside", cliponaxis=False)
-                if target == 'Revenue_Per_Email':
-                    out = f"${round(change,2)}"
-                    st.write("The output will be between the range " + str(round(lower,2)) + ' and ' + str(round(upper,2)))
-                    st.write("The Predicted "+str(target) +" is "+ str(round(pred,2)))
-                else:
-                    out = f"{round(change,2)*100}%"
-                    st.write("The output will be between the range " + str(round(lower,2) * 100) + ' and ' + str(round(upper,2) *100))
-                    st.write("The Predicted "+str(target) +" is "+ str(round(pred,2)*100))
-                fig2.update_layout(title_text=f'The following Changes will yield a {out} increase in {target}')
-                st.plotly_chart(fig2, use_container_width=True)
-                #FunctionsModelSA_V1.corrections(best_target_tones,test_predictions))
-                
-            placeholder.empty()
-
-    WHITE_SPACE(10)
+                st.plotly_chart(fig1, use_container_width=True)
+
+                #if((best_target!=0) and (pred<best_target)):
+                if Loxz_recom_box == True:
+                    recommended_changes=(best_target_tones)   
+                    change=best_target-pred
+                    #recommend(tones,recommended_changes,change,target)
+
+                    fig2 = go.Figure()
+                    fig2.add_trace(go.Bar(
+                        y=tone_labels,
+                        x=recommended_changes,
+                        name='Recommend changes',
+                        orientation='h',
+                        text=np.round(recommended_changes,3),
+                        width=.5,
+                        marker=dict(
+                        color='#e60f00',
+                        line=dict(color='rgba(58, 71, 80, 1.0)', width=1)))
+                    )
+                    fig2.update_traces(textfont_size=18, textposition="outside", cliponaxis=False)
+                    if target == 'Revenue_Per_Email':
+                        out = f"${round(change,2)}"
+                        st.write("The output will be between the range " + str(round(lower,2)) + ' and ' + str(round(upper,2)))
+                        st.write("The Predicted "+str(target) +" is "+ str(round(pred,2)))
+                    else:
+                        out = f"{round(change,2)*100}%"
+                        st.write("The output will be between the range " + str(round(lower,2) * 100) + ' and ' + str(round(upper,2) *100))
+                        st.write("The Predicted "+str(target) +" is "+ str(round(pred,2)*100))
+                    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()
+
 WHITE_SPACE(10)