app version 1.2

app.py

@@ -1,13 +1,45 @@
 from ast import arg
 import FunctionsModelSA_V1 
+import numpy as np
 import streamlit as st
 import pandas as pd
 import PIL
 import time
-import streamlit_analytics
 
 import main_app
 import utils
+import s3fs
+from colour import Color
+import plotly.graph_objects as go
+from nltk import tokenize
+from IPython.display import Markdown
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+import nltk
+nltk.download('punkt')
+import email
+import codecs
+import pickle
+import string
+from scipy import spatial
+import re
+import pytorch_lightning as pl
+from bs4 import BeautifulSoup
+import ipywidgets as widgets
+from ipywidgets import FileUpload
+from urlextract import URLExtract
+from transformers import BertTokenizerFast as BertTokenizer, BertModel, BertConfig
+import torch.nn as nn
+import torch
+from ipywidgets import interact, Dropdown
+import boto3
+from sagemaker import get_execution_role
+from scipy import spatial
+from ipyfilechooser import FileChooser
+import random
+import streamlit_analytics
+
+from FunctionsModelSA_V1 import *
+#from model_V1 import *
 
 def table_data():
     # creating table data
@@ -58,7 +90,7 @@ def add_bg_from_url():
 
 # add_bg_from_url() 
 
-st.markdown("### Sentiment Analysis: Email Industry")
+st.markdown("#### Sentiment Analysis: Email Industry v1.2")
 
 stats_col1, stats_col2, stats_col3, stats_col4 = st.columns([1,1,1,1])
 
@@ -108,59 +140,146 @@ campaign_types = ['Webinar', 'Engagement', 'Product_Announcement', 'Promotional'
 
 target_variables = ['Conversion_Rate','Click_To_Open_Rate','Revenue_Per_Email']
 
+region_variables = ['Africa', 'America', 'Asia', 'Europe', 'Oceania']
+
+input_text = st.text_area("Please enter your email text here: ", height=50)
+
+with st.expander('Please select your parameters: '):
+    with streamlit_analytics.track('test123'):
+        industry = st.selectbox(
+            'Please select your industry',
+            industry_lists,
+            index=6
+        )
+        
+        target = st.selectbox(
+            'Please select your target variable',
+            target_variables,
+            index=1
+        )
+
+        campaign = st.selectbox(
+            'Please select your campaign type',
+            campaign_types,
+            index=7
+        )
+
+        region = st.selectbox(
+            'Select your target region to generate the email with a more appropriate tone:',
+            region_variables,
+            index=1
+        )
+
+
+with st.expander('Adjust your tone intensities for your preference: '):
+#'Joyful', 'Confident', 'Urgent', 'Friendly', 'Optimistic', 'Analytical', 'Casual'
+    joyful_tone_value = st.slider(
+        'Joyful: ',
+        value = 0
+    )
+    st.write('Joyful: ', joyful_tone_value)
 
+    confident_tone_value = st.slider(
+        'Confident: ',
+        value = 0
+    )
+    st.write('Confident: ', confident_tone_value)
 
-input_text = st.text_area("Please enter your email text here", height=50)
-
-with streamlit_analytics.track(unsafe_password="test123"):
-    industry = st.selectbox(
-        'Please select your industry',
-        industry_lists,
-        index=6
+    urgent_tone_value = st.slider(
+        'Urgent: ',
+        value = 0
     )
+    st.write('Urgent: ', urgent_tone_value)
 
-    campaign  = st.selectbox(
-        'Please select your campaign type',
-        campaign_types,
-        index=5
+    friendly_tone_value = st.slider(
+        'Friendly: ',
+        value = 0
     )
+    st.write('Friendly: ', friendly_tone_value)
 
-    target = st.selectbox(
-        'Please select your target variable',
-        target_variables,
-        index=1
+    optimistic_tone_value = st.slider(
+        'Optimistic: ',
+        value = 0
     )
+    st.write('Optimistic: ', optimistic_tone_value)
 
+    analytical_tone_value = st.slider(
+        'Analytical: ',
+        value = 0
+    )
+    st.write('Analytical: ', analytical_tone_value)
 
-    if st.button('Generate Predictions'):
-        start_time = time.time() 
-        if input_text is "":
-            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(bucket,tone_key)
-            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)
-            best_target,best_target_tones = FunctionsModelSA_V1.find_max_cat(tone_data,target,industry_val,campaign_val)
-
-            FunctionsModelSA_V1.plot_CI(pred,lower,upper,streamlit=True)
-            if((best_target!=0) and (pred<best_target)):
-                recommended_changes=(best_target_tones-tones.loc[0]) 
-                change=best_target-pred
-                FunctionsModelSA_V1.recommend(tones,recommended_changes,change,target,streamlit=True)
-                FunctionsModelSA_V1.corrections(best_target_tones,test_predictions,streamlit=True)
-
-            placeholder.empty()
+    casual_tone_value = st.slider(
+        'Casual: ',
+        value = 0
+    )
+    st.write('Casual: ', casual_tone_value)
+
+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(bucket,tone_key)
+        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)))
+            )
+            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()