added streamlit analytics

app.py

@@ -4,6 +4,7 @@ import streamlit as st
 import pandas as pd
 import PIL
 import time
+import streamlit_analytics
 
 import main_app
 import utils
@@ -111,55 +112,55 @@ target_variables = ['Conversion_Rate','Click_To_Open_Rate','Revenue_Per_Email']
 
 input_text = st.text_area("Please enter your email text here", height=50)
 
-
-industry = st.selectbox(
-    'Please select your industry',
-    industry_lists,
-    index=6
-)
-
-campaign  = st.selectbox(
-    'Please select your campaign type',
-    campaign_types,
-    index=5
-)
-
-target = st.selectbox(
-    'Please select your target variable',
-    target_variables,
-    index=1
-)
-
-
-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()
+with streamlit_analytics.track():
+    industry = st.selectbox(
+        'Please select your industry',
+        industry_lists,
+        index=6
+    )
+
+    campaign  = st.selectbox(
+        'Please select your campaign type',
+        campaign_types,
+        index=5
+    )
+
+    target = st.selectbox(
+        'Please select your target variable',
+        target_variables,
+        index=1
+    )
+
+
+    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()

requirements.txt

@@ -12,4 +12,6 @@ bs4
 boto3
 torch
 bokeh==2.4.1
-scikit-learn
+scikit-learn
+
+streamlit-analytics