adding gen ai 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
@@ -478,7 +479,7 @@ if st.button('Generate Predictions'):
                     #ax.bar_label(bars)
 
                     ax.set_yticks(np.arange(len(chars)))
-                    ax.set_yticklabels(tuple([str(x) for x in 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)
@@ -495,9 +496,25 @@ 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)
                     #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)
+                    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
+                            )
+                    for x in sel_var_values:
+                        if str(x) in preference:
+                            pref_cc=x
+                            ai_generated_email=generate_example_email_with_context(email_body, campaign, industry, target, sorted_chars_out, pref_cc)
+                    st.markdown('##### Here is the recommended Generated Email for you:')
+                    st.markdown('####### {}:'.format(ai_generated_email),unsafe_allow_html=True)
+
+
+                # chars_out = dict(zip(chars, sel_var_values))
+                # sorted_chars_out = sorted(chars_out.items(), key=lambda x: x[1], reverse=True)
                 
 
         placeholder.empty()

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()

requirements.txt

@@ -14,4 +14,5 @@ urlextract
 bs4
 matplotlib
 plotly
-streamlit==1.25.0
+streamlit==1.25.0
+openai==0.8.0

utils.py

@@ -0,0 +1,56 @@
+import openai
+from io import BytesIO
+from config import config
+
+
+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