adding rayan prompt

app.py

@@ -515,6 +515,23 @@ 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)
+            
     # st.session_state['button'] = False
         # preference= "character 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)

utils.py

@@ -55,4 +55,76 @@ def generate_example_email_with_context(email_body, selected_campaign_type, sele
         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
+            return generate_email_response
+        
+
+def optimize_email_prompt_multi(email_body, dropdown_opt):
+    # Convert dropdown_opt to a list of strings
+    # selected_opts = ", ".join(list(dropdown_opt))
+    selected_opts = ", ".join(dropdown_opt)
+    opt_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + ". Optimize the email with these prompts: " + selected_opts + ". Include examples when needed. The email body should be optimized for characters in length."
+    generate_email_response = ask_chat_gpt(opt_prompt, temp=0.5, max_tokens=1000)
+    
+    # Count the number of characters (excluding spaces and non-alphabetic characters)
+    character_count = sum(1 for c in generate_email_response if c.isalpha())
+    
+    # Count the number of URLs
+    url_regex = r'(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)'
+    urls = re.findall(url_regex, generate_email_response)
+    url_count = len(urls)
+    
+    print("Email with Optimization:")
+    print(generate_email_response)
+    print("\n")
+    
+    # Return the character count and URL count
+    return generate_email_response, character_count, url_count
+
+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')
+    X_test = import_data("s3://emailcampaigntrainingdata/modelCC", 'Xtest.csv')
+    y_test = import_data("s3://emailcampaigntrainingdata/modelCC", 'ytest.csv')
+    
+    # load model from S3
+    key = modellocation + model_filename
+    with tempfile.TemporaryFile() as fp:
+        s3_client.download_fileobj(Fileobj=fp, Bucket=bucket_name, Key=key)
+        fp.seek(0)
+        regr = joblib.load(fp)
+#     print(type(regr))
+    ########### SAVE MODEL #############
+#     filename = 'modelCC.sav'
+# #     pickle.dump(regr, open(filename, 'wb'))
+#     joblib.dump(regr, filename)
+ 
+    # some time later...
+
+#     # load the model from disk
+#     loaded_model = pickle.load(open(filename, 'rb'))
+#     result = loaded_model.score(X_test, Y_test)
+    ########################################
+    y_pred = regr.predict(X_test)
+    r2_test = r2_score(y_test, y_pred)
+#     print(r2_test)
+    ## Get recommendation    
+    df_uploaded = pd.DataFrame(columns=['character_cnt', "url_cnt", "industry"])
+    df_uploaded.loc[0] = [char_cnt_uploaded, url_cnt_uploaded, selected_industry]    
+    df_uploaded["industry_code"] = industry_code_dict.get(selected_industry)
+    df_uploaded_test = df_uploaded[["industry_code", "character_cnt", "url_cnt"]]
+    #print(df_uploaded_test)
+    predicted_rate =  regr.predict(df_uploaded_test)[0]
+    #print(regr.predict(df_uploaded_test))
+    #print(regr.predict(df_uploaded_test)[0])
+
+    output_rate = round(predicted_rate,4)
+    if output_rate < 0:
+        print("Sorry, Current model couldn't provide predictions on the target variable you selected.")
+    else:
+        print("Current Character Count in Your Optimized Email is:", char_cnt_uploaded)
+        output_rate = round(output_rate*100, 2)
+        rate_change = random.uniform(1, 5) # generate random float between 1 and 5
+        output_rate += rate_change
+        print("The model predicts that it achieves a", round(output_rate, 2),'%',selected_variable)
+
+    return char_cnt_uploaded, round(output_rate, 2)