adding rayan prompt

app.py

@@ -2,9 +2,6 @@ from ast import arg
 import streamlit as st
 import pandas as pd
 import PIL
-import re
-from io import StringIO
-import boto3
 from urlextract import URLExtract
 import time
 from utils import * 
@@ -304,24 +301,6 @@ st.markdown("""---""")
 #    index=1)
 
 
-def get_files_from_aws(bucket, prefix):
-    """
-        get files from aws s3 bucket
-    bucket (STRING): bucket name
-    prefix (STRING): file location in s3 bucket
-    """
-    s3_client = boto3.client('s3',
-                             aws_access_key_id=st.secrets["aws_id"],
-                             aws_secret_access_key=st.secrets["aws_key"])
-
-    file_obj = s3_client.get_object(Bucket=bucket, Key=prefix)
-    body = file_obj['Body']
-    string = body.read().decode('utf-8')
-
-    df = pd.read_csv(StringIO(string))
-
-    return df
-
 
 # st.info([industry,campaign,target,char_reco_preference])
 
@@ -515,22 +494,22 @@ if st.session_state.get('button') == True:
                     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)
-            options = st.multiselect(
-                        'Select propmts you want to use to generate your email:',
-                        ["Convey key message in fewer words",
-                        "Rephrase sentences to be more concise",
-                        "Remove unnecessary details/repetitions",
-                        "Use bullet points or numbered lists",
-                        "Include clear call-to-action in the email",
-                        "Link to information instead of writing it out",
-                        "Shorten the subject line",
-                        "Replace technical terms with simpler language"],
-                        ["Remove unnecessary details/repetitions"])
-            optimized_email, optimized_char_cnt, optimized_url_cnt = optimize_email_prompt_multi(email_body, options)
-            charc, tmval=get_optimized_prediction("sagemakermodelcc", "modelCC.sav", "sagemakermodelcc", target, industry, 
-                optimized_char_cnt, optimized_url_cnt, industry_code_dict)
-            st.markdown('##### Current Character Count in Your Optimized Email is: {}'.format(charc), unsafe_allow_html=True)
-            st.markdown('##### The model predicts that it achieves a {} of {}%'.format(target,tmval), unsafe_allow_html=True)
+                    options = st.multiselect(
+                                'Select propmts you want to use to generate your email:',
+                                ["Convey key message in fewer words",
+                                "Rephrase sentences to be more concise",
+                                "Remove unnecessary details/repetitions",
+                                "Use bullet points or numbered lists",
+                                "Include clear call-to-action in the email",
+                                "Link to information instead of writing it out",
+                                "Shorten the subject line",
+                                "Replace technical terms with simpler language"],
+                                ["Remove unnecessary details/repetitions"])
+                    optimized_email, optimized_char_cnt, optimized_url_cnt = optimize_email_prompt_multi(email_body, options)
+                    charc, tmval=get_optimized_prediction("sagemakermodelcc", "modelCC.sav", "sagemakermodelcc", target, industry, 
+                        optimized_char_cnt, optimized_url_cnt, industry_code_dict)
+                    st.markdown('##### Current Character Count in Your Optimized Email is: {}'.format(charc), unsafe_allow_html=True)
+                    st.markdown('##### The model predicts that it achieves a {} of {}%'.format(target,tmval), unsafe_allow_html=True)
             
     # st.session_state['button'] = False
         # preference= "character counts: "+str(573)+", Target Rate: "+str(37.2)

utils.py

@@ -1,6 +1,14 @@
 import openai
 from io import BytesIO
 from config import config
+import re
+import pandas as pd
+import random
+import boto3
+s3 = boto3.resource('s3')
+from io import StringIO
+import joblib
+s3_client = boto3.client('s3')
 
 openai.api_key = config.OPEN_API_KEY
 
@@ -80,6 +88,27 @@ def optimize_email_prompt_multi(email_body, dropdown_opt):
     # Return the character count and URL count
     return generate_email_response, character_count, url_count
 
+def import_data(bucket, key):
+    return get_files_from_aws(bucket, key)
+
+def get_files_from_aws(bucket, prefix):
+    """
+        get files from aws s3 bucket
+    bucket (STRING): bucket name
+    prefix (STRING): file location in s3 bucket
+    """
+    s3_client = boto3.client('s3',
+                             aws_access_key_id=st.secrets["aws_id"],
+                             aws_secret_access_key=st.secrets["aws_key"])
+
+    file_obj = s3_client.get_object(Bucket=bucket, Key=prefix)
+    body = file_obj['Body']
+    string = body.read().decode('utf-8')
+
+    df = pd.read_csv(StringIO(string))
+
+    return df
+
 def get_optimized_prediction(modellocation, model_filename, bucket_name, selected_variable, selected_industry, 
                 char_cnt_uploaded, url_cnt_uploaded, industry_code_dict): #preference, industry_code_dict):
     training_dataset = import_data("s3://emailcampaigntrainingdata/modelCC", 'training.csv')