adding Genrative AI email genearion support

app.py

@@ -7,6 +7,8 @@ from io import StringIO
 import boto3
 from urlextract import URLExtract
 import time
+from utils import * 
+
 # from joblib import dump, load
 
 import joblib
@@ -18,7 +20,6 @@ import os
 #from ipyfilechooser import FileChooser
 
 #from IPython.display import display
-from io import BytesIO
 from bs4 import BeautifulSoup
 import matplotlib.pyplot as plt
 import numpy as np
@@ -380,7 +381,7 @@ if st.button('Generate Predictions'):
             print(
                 "Sorry, Current model couldn't provide predictions on the target variable you selected.")
         else:
-            st.markdown('#### Current Character Count in Your Email is: <span style="color:blue">{}</span>'.format(
+            st.markdown('##### Current Character Count in Your Email is: <span style="color:yellow">{}</span>'.format(
                 character_cnt), unsafe_allow_html=True)
             # st.info('The model predicts that it achieves a {} of {}%'.format(target, str(round(output_rate*100,2))))
             if target == 'conversion_rate':
@@ -388,7 +389,7 @@ if st.button('Generate Predictions'):
             else:
                 target_vis = 'Open_Rate'
                 
-            st.markdown('#### The model predicts that it achieves a <span style="color:blue">{}</span> of <span style="color:blue">{}</span>%'.format(
+            st.markdown('##### The model predicts that it achieves a <span style="color:yellow">{}</span> of <span style="color:yellow">{}</span>%'.format(
                 target_vis, str(round(output_rate*100, 3))), unsafe_allow_html=True)
             selected_industry_code = industry_code_dict.get(industry)
 
@@ -440,10 +441,10 @@ if st.button('Generate Predictions'):
             if selected_variable == "Click_Through_Rate":
                 selected_variable = "Conversion_Rate"
 
-            st.markdown('#### To get higher, <span style="color:blue">{}</span>, the model recommends the following options:'.format(
+            st.markdown('##### To get higher, <span style="color:yellow">{}</span>, the model recommends the following options:'.format(
                 selected_variable), unsafe_allow_html=True)
             if len(df_reco_opt_rank) == 0:
-                st.markdown('#### You ve already achieved the highest, <span style="color:blue">{}</span>, with the current character count!'.format(
+                st.markdown('##### You ve already achieved the highest, <span style="color:yellow">{}</span>, with the current character count!'.format(
                     selected_variable), unsafe_allow_html=True)
             else:
                 #for _, row in df_reco_opt_rank.iterrows():
@@ -461,8 +462,13 @@ if st.button('Generate Predictions'):
                     selected_variable_number = row[3]
                     chars.append(int(Character_Count))
                     sel_var_values.append(round(selected_variable_number, 3)*100)
-                    st.write(f"·Number of Characters: {int(Character_Count)}, Target Rate: {round(round(selected_variable_number, 3)*100, 3)}", "%")
+                    # st.write(f"·Number of Characters: {int(Character_Count)}, Target Rate: {round(round(selected_variable_number, 3)*100, 3)}", "%")
                 st.write("\n")
+                df_modelpred=pd.DataFrame(list(zip(chars, sel_var_values)), columns=["Number of Characters", "Target_Rate"])
+                # st.checkbox("Use container width", value=False, key="use_container_width")
+                # st.dataframe(df_modelpred.style.highlight_max(axis=0), use_container_width=st.session_state.use_container_width)
+                df_modelpred.sort_values(by='Target_Rate', ascending=False, inplace = True)
+                st.dataframe(df_modelpred)
 
                 if len(chars) > 1:
                     #fig = plt.figure()
@@ -473,7 +479,7 @@ if st.button('Generate Predictions'):
                     #ax.bar_label(bars)
 
                     ax.set_yticks(np.arange(len(chars)))
-                    ax.set_yticklabels(np.array(chars), fontsize=14)
+                    ax.set_yticklabels(tuple(chars), fontsize=14)
                     ax.set_title('Character Counts vs. Target Variable Rates', fontsize=18)
                     ax.set_ylabel('Character Counts', fontsize=16)
                     ax.set_xlabel('Target Rates %', fontsize=16)
@@ -490,7 +496,29 @@ if st.button('Generate Predictions'):
                     st.plotly_chart(fig, use_container_width=True)
 
                     st.write("\n")
+                    chars_out = dict(zip(chars, sel_var_values))
+                    sorted_chars_out = sorted(chars_out.items(), key=lambda x: x[1], reverse=True)
+                    prefrence_variables=res=["charcter counts: "+str(x)+", Target Rate: "+str(y) for x,y in zip(chars,sel_var_values)]
+                    preference = st.selectbox(
+                                'Please select your preferences',
+                                prefrence_variables,
+                                index=1
+                            )
+                    if st.button('Generate AI Recommended Email'):
+                        if(preference is None):
+                            st.error('Please upload a email (HTML format)')
+                        else:
+                            ai_generated_email=generate_example_email_with_context(email_body, campaign, industry, target, sorted_chars_out, preference)
+                            st.markdown('##### Here is the recommended Generated Email for you:')
+                            st.markdown('####### {}:'.format(ai_generated_email),unsafe_allow_html=True)
+                    preference= "charcter counts: "+str(573)+", Target Rate: "+str(37.2)
+                    ai_generated_email=generate_example_email_with_context(email_body, campaign, industry, target, sorted_chars_out, preference)
+                    print("ai_generated_email: ",ai_generated_email)
+                    st.markdown('##### Here is the recommended Generated Email for you:')
+                    st.markdown('####### {}'.format(ai_generated_email),unsafe_allow_html=True)
                     #st.write(np.array(chars))
+
+
                 chars_out = dict(zip(chars, sel_var_values))
                 sorted_chars_out = sorted(chars_out.items(), key=lambda x: x[1], reverse=True)
                 

config.py

@@ -0,0 +1,22 @@
+'''
+Configurations 
+'''
+import os
+import argparse
+
+class Config(object): 
+    OPEN_API_KEY='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'
+    OPENAI_MODEL_TYPE='text-davinci-003'
+    OPENAI_MODEL_TEMP=0.3
+    MODELCC_DATA_S3PATH='s3://emailcampaigntrainingdata/modelCC'
+    TRAINING_DATA_S3PATH='s3://emailcampaigntrainingdata/trainingdata'
+    MODEL_FILE_NAME='modelCC.sav'
+    MODEL_ALLOCATION_PREFIX='sagemakermodelcc'
+    MODEL_BUCKET_NAME='sagemakermodelcc'
+    DATASET_TRAINING='email_dataset_training'
+    DATASET_TRAINING_RAW='email_dataset_training_raw'
+    MODELCC_TRAINING_DATA='training.csv'
+    MODELCC_TEST_DATA='Xtest.csv'
+    MODELCC_TEST_LABEL='ytest.csv'
+
+config=Config()

main_app.py

@@ -1,15 +0,0 @@
-import streamlit as st
-
-st.set_page_config(layout="wide")
-
-st.markdown(
-    """
-<style>
-body {
-    background-image: linear-gradient(#2e7bcf,#2e7bcf);
-    color: white;
-}
-</style>
-""",
-    unsafe_allow_html=True,
-)

requirements.txt

@@ -1,3 +1,6 @@
+# transformers
+# torch
+streamlit
 altair<5
 pickle5
 numpy
@@ -11,4 +14,5 @@ urlextract
 bs4
 matplotlib
 plotly
-streamlit==1.25.0
+streamlit==1.25.0
+openai==0.8.0

utils.py

@@ -0,0 +1,58 @@
+import openai
+from io import BytesIO
+from config import config
+
+openai.api_key = config.OPEN_API_KEY
+
+
+def ask_chat_gpt(prompt, model=config.OPENAI_MODEL_TYPE, temp=0, max_tokens=500):
+    response = openai.Completion.create(
+        engine=model,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        stop=None,
+        temperature=temp,
+    )
+    message = response.choices[0].text
+    return message.strip()
+
+def chat_gpt_user_input_loop():
+    prompt = "Ask me anything on regarding email optimization.       "
+    user_input = input(prompt)
+    response = ask_chat_gpt(prompt + user_input)
+    chat_gpt_user_input_loop()
+
+
+def generate_example_email_with_context(email_body, selected_campaign_type, selected_industry, selected_variable, chars_out, dropdown_cc):
+    if len(chars_out) == 1:
+        if str(chars_out[0][0]) in dropdown_cc:
+            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[0][0]+200) + "characters in length."
+            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[0][0] + 200)
+            return generate_email_response
+            
+    if len(chars_out) == 2:
+        if str(chars_out[0][0]) in dropdown_cc:
+            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[0][0]+200) + "characters in length."
+            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[0][0] + 200)
+            return generate_email_response
+        
+        if str(chars_out[1][0]) in dropdown_cc:
+            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[1][0]+200) + "characters in length." + "Add more information and description as needed."
+            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[1][0] + 200)
+            return generate_email_response
+    
+    if len(chars_out) == 3:
+        if str(chars_out[0][0]) in dropdown_cc:
+            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[0][0]+200) + "characters in length."
+            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[0][0] + 200)
+            return generate_email_response
+        
+        if str(chars_out[1][0]) in dropdown_cc:
+            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[1][0]+200) + "characters in length." + "Add more information and description as needed."
+            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[1][0] + 200)
+            return generate_email_response
+        
+        if str(chars_out[2][0]) in dropdown_cc:
+            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[2][0]+200) + "characters in length."
+            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[2][0] + 200)
+            return generate_email_response