Added Generative AI LLM support

README.md

@@ -4,8 +4,7 @@ emoji: ✉️
 colorFrom: purple
 colorTo: yellow
 sdk: streamlit
-sdk_version: 1.25.0
-python_version: 3.9
+sdk_version: 1.10.0
 app_file: app.py
 pinned: false
 duplicated_from: loxzdigital/Model-CC-Space

app.py

@@ -7,8 +7,6 @@ from io import StringIO
 import boto3
 from urlextract import URLExtract
 import time
-from utils import * 
-
 # from joblib import dump, load
 
 import joblib
@@ -16,18 +14,15 @@ import joblib
 from bokeh.models.widgets import Div
 
 import email
-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
 import timeit
-import shutil
 
-CURRENT_THEME = "blue"
-IS_DARK_THEME = True
 
 def table_data():
     # creating table data
@@ -184,7 +179,7 @@ def add_bg_from_url():
         f"""
          <style>
          .stApp {{
-             background-image: linear-gradient(135deg,#061c2c,#084e69 35%,#3e7e89);
+             background-image: linear-gradient(#45eff5,#1C8D99);
              background-attachment: fixed;
              background-size: cover
             
@@ -267,10 +262,6 @@ target_variables = [
 uploaded_file = st.file_uploader(
     "Please upload your email (In HTML Format)", type=["html"])
 
-def save_file(uploaded_file):
-    with open(os.path.join("./",uploaded_file.name),"wb") as f:
-        f.write(uploaded_file.getbuffer())
-
 if uploaded_file is None:
     # upload_img = PIL.Image.open(uploaded_file)
     upload_img = None
@@ -307,6 +298,7 @@ st.markdown("""---""")
 def get_files_from_aws(bucket, prefix):
     """
         get files from aws s3 bucket
+
     bucket (STRING): bucket name
     prefix (STRING): file location in s3 bucket
     """
@@ -331,7 +323,6 @@ if st.button('Generate Predictions'):
     if uploaded_file is None:
         st.error('Please upload a email (HTML format)')
     else:
-        save_file(uploaded_file)
         placeholder = st.empty()
         placeholder.text('Loading Data')
 
@@ -381,7 +372,7 @@ 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:yellow">{}</span>'.format(
+            st.markdown('#### Current Character Count in Your Email is: <span style="color:blue">{}</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':
@@ -389,7 +380,7 @@ if st.button('Generate Predictions'):
             else:
                 target_vis = 'Open_Rate'
                 
-            st.markdown('##### The model predicts that it achieves a <span style="color:yellow">{}</span> of <span style="color:yellow">{}</span>%'.format(
+            st.markdown('#### The model predicts that it achieves a <span style="color:blue">{}</span> of <span style="color:blue">{}</span>%'.format(
                 target_vis, str(round(output_rate*100, 3))), unsafe_allow_html=True)
             selected_industry_code = industry_code_dict.get(industry)
 
@@ -441,10 +432,10 @@ if st.button('Generate Predictions'):
             if selected_variable == "Click_Through_Rate":
                 selected_variable = "Conversion_Rate"
 
-            st.markdown('##### To get higher, <span style="color:yellow">{}</span>, the model recommends the following options:'.format(
+            st.markdown('#### To get higher, <span style="color:blue">{}</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:yellow">{}</span>, with the current character count!'.format(
+                st.markdown('#### You ve already achieved the highest, <span style="color:blue">{}</span>, with the current character count!'.format(
                     selected_variable), unsafe_allow_html=True)
             else:
                 #for _, row in df_reco_opt_rank.iterrows():
@@ -462,13 +453,8 @@ 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()
@@ -479,7 +465,7 @@ if st.button('Generate Predictions'):
                     #ax.bar_label(bars)
 
                     ax.set_yticks(np.arange(len(chars)))
-                    ax.set_yticklabels(tuple(chars), fontsize=14)
+                    ax.set_yticklabels(np.array(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)
@@ -496,32 +482,11 @@ 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)
                 
 
         placeholder.empty()
-        #st.write(time.time() - start_time)
+        #st.write(time.time() - start_time)
+

main_app.py

@@ -0,0 +1,15 @@
+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

@@ -1,18 +1,11 @@
-# transformers
-# torch
-streamlit
-altair<5
-pickle5
-numpy
-pandas
-scikit-learn==0.24.1
-Cython==0.29.36
-bokeh==2.4.3
+bokeh==2.4.1
 joblib
 boto3
+pandas
 urlextract
+scikit-learn==0.24.1
 bs4
 matplotlib
+numpy
 plotly
-streamlit==1.25.0
-openai==0.8.0
+streamlit==1.21.0