conversion output

classes/response_df_overview.py

@@ -0,0 +1,92 @@
+from io import StringIO
+from urllib.parse import urlparse
+import streamlit as st
+import requests
+from dotenv import load_dotenv
+import os
+from helper.upload_response import upload_response
+from helper.upload_File import uploadFile
+import json
+from pymongo import MongoClient
+
+class dfOverview:
+    def __init__(self, model_url):
+        self.uploaded_files = []
+        self.file_dict = {}
+        self.model_url = model_url
+        #self.analyst_name = analyst_name
+        #self.data_src = data_src
+        #self.analyst_description = analyst_description
+        self.initialize()
+        self.row1()
+
+    def initialize(self):
+        # FOR ENV
+        load_dotenv()
+
+        # AGENT NAME
+        #st.header(self.analyst_name)
+    
+    def request_model(self, payload_txt, headers):
+        response = requests.post(self.model_url, json=payload_txt, headers=headers)
+        response.raise_for_status()
+        output = response.json()
+        #st.write(output)
+        text = output["outputs"][0]["outputs"][0]["results"]["text"]["data"]["text"]
+        #text = json.loads(text)
+        st.write(text)
+        return text
+    
+    def fetch_data(self, data_field):
+        mongodb_uri = os.getenv("MONGODB_URI")
+        myclient = MongoClient(mongodb_uri)
+        mydb = myclient.get_database()
+        mycol = mydb["df_data"]
+        
+        # Sort by timestamp field in descending order
+        x = mycol.find_one(
+            {"data_field": data_field},
+            sort=[("timestamp", -1)]  
+        )
+        
+        x = x["result"]
+        return x
+    
+    def process(self):
+                with st.spinner('DF Overview Analyst...', show_time=True):
+                        st.write('')
+                        headers = {"Content-Type": "application/json", "x-api-key": f"{os.getenv('x-api-key')}"}         
+                        try:
+                                payload_txt = {"input_value": self.payload, "output_type": "text", "input_type": "chat"}
+                                payload_txt_model = self.request_model(payload_txt, headers)
+                                debug_info = {'data_field' : 'DF Overview Analyst', 'result': payload_txt_model}
+                                upload_response(debug_info)
+
+                                st.session_state['target_market'] = ''
+                                count = 0
+                        except Exception as e:
+                            pass
+                        st.session_state['analyzing'] = False    
+                      
+    def row1(self):
+            st.session_state['analyzing'] = False
+            self.payload = ""  
+            count = 0
+            try:
+                session_client_summary = st.session_state['client_summary']
+                if session_client_summary == 'uploaded':
+                    count += 1
+                    self.payload += self.fetch_data("Client Summary")
+                    self.payload += self.fetch_data("Client Name")
+                
+            except Exception as e:
+                pass
+            
+            if count >= 1:
+                self.process()
+                                       
+
+if __name__ == "__main__":
+    st.set_page_config(layout="wide")
+
+upload = uploadFile()

classes/response_executive_summary.py

@@ -0,0 +1,115 @@
+import streamlit as st
+import requests
+from dotenv import load_dotenv
+import os
+from helper.upload_response import upload_response
+from helper.upload_File import uploadFile
+from pymongo import MongoClient
+from helper.data_field import get_analyst_response
+
+
+class ExecutiveSummary:
+    def __init__(self, model_url):
+        self.uploaded_files = []
+        self.file_dict = {}
+        self.model_url = model_url
+        #self.analyst_name = analyst_name
+        #self.data_src = data_src
+        #self.analyst_description = analyst_description
+        self.initialize()
+        self.row1()
+
+    def initialize(self):
+        # FOR ENV
+        load_dotenv()
+
+        # AGENT NAME
+        #st.header(self.analyst_name)
+    
+    def request_model(self, payload_txt, headers):
+        response = requests.post(self.model_url, json=payload_txt, headers=headers)
+        response.raise_for_status()
+        output = response.json()
+        #st.write(output)
+        text = output["outputs"][0]["outputs"][0]["results"]["text"]["data"]["text"]
+        #text = json.loads(text)
+        #st.write(text)
+        return text
+    
+    def fetch_data(self, data_field):
+        mongodb_uri = os.getenv("MONGODB_URI")
+        myclient = MongoClient(mongodb_uri)
+        mydb = myclient.get_database()
+        mycol = mydb["df_data"]
+        
+        # Sort by timestamp field in descending order
+        x = mycol.find_one(
+            {"data_field": data_field},
+            sort=[("timestamp", -1)]  
+        )
+        
+        x = x["result"]
+        return x
+    
+    def process(self):
+                with st.spinner('Executive Summary...', show_time=True):
+                        st.write('')
+                        headers = {"Content-Type": "application/json", "x-api-key": f"{os.getenv('x-api-key')}"}         
+                        try:
+                                payload_txt = {"input_value": self.payload, "output_type": "text", "input_type": "chat"}
+                                payload_txt_model = self.request_model(payload_txt, headers)
+                                debug_info = {'data_field' : 'Executive Summary', 'result': payload_txt_model}
+                                upload_response(debug_info)
+
+                        except Exception as e:
+                            pass
+                        st.session_state['analyzing'] = False    
+                      
+    def row1(self):
+            st.session_state['analyzing'] = False
+            self.payload = ""
+            
+            self.website_and_tools_data = get_analyst_response("Website and Tools Analyst")
+            self.sem_data = get_analyst_response("SEM/PPC Analyst")
+            self.seo_data = get_analyst_response("SEO Analyst")
+            self.on_page_data = get_analyst_response("On Page Analyst")
+            self.off_page_data = get_analyst_response("Off Page Analyst")
+            self.social_media_data = get_analyst_response("Social Media Analyst")
+            self.content_data = get_analyst_response("Content Analyst")
+            self.marketpalce_data = get_analyst_response("Marketplace Analyst")
+            self.target_market_data = get_analyst_response("Target Market Analyst")
+            self.website_audience_data = get_analyst_response("Pull through offers Analyst")
+            self.pull_through_data = get_analyst_response("Website Audience Acquisition Analyst")
+            self.lld_data = get_analyst_response("LLD/PM/LN Analyst")
+            self.pna_data = get_analyst_response("Content - Process and Assets Analyst")
+            
+            analyst_data_dict = {
+                "Website and Tools": self.website_and_tools_data,
+                "SEM/PPC": self.sem_data,
+                "SEO": self.seo_data,
+                "On Page": self.on_page_data,
+                "Off Page": self.off_page_data,
+                "Social Media": self.social_media_data,
+                "Content": self.content_data,
+                "Marketplace": self.marketpalce_data,
+                "Target Market": self.target_market_data,
+                "Pull through offers": self.website_audience_data,
+                "Website Audience Acquisition": self.pull_through_data,
+                "LLD/PM/LN": self.lld_data,
+                "Content - Process and Assets": self.pna_data
+            }
+
+
+            for analyst_name, data in analyst_data_dict.items():
+                self.payload += f"\n\n--- {analyst_name} Analysis ---\n"
+                if isinstance(data, list):
+                    self.payload += "\n".join(map(str, data))
+                else:
+                    self.payload += str(data)
+            
+            self.process()
+                                       
+if __name__ == "__main__":
+    st.set_page_config(layout="wide")
+
+upload = uploadFile()

classes/response_snapshot.py

@@ -0,0 +1,101 @@
+import streamlit as st
+import requests
+from dotenv import load_dotenv
+import os
+from helper.upload_response import upload_response
+from helper.upload_File import uploadFile
+from pymongo import MongoClient
+from helper.data_field import get_analyst_response
+import json
+
+
+class Snapshot:
+    def __init__(self, model_url):
+        self.uploaded_files = []
+        self.file_dict = {}
+        self.model_url = model_url
+        #self.analyst_name = analyst_name
+        #self.data_src = data_src
+        #self.analyst_description = analyst_description
+        self.initialize()
+        self.row1()
+
+    def initialize(self):
+        # FOR ENV
+        load_dotenv()
+
+        # AGENT NAME
+        #st.header(self.analyst_name)
+    
+    def request_model(self, payload_txt, headers):
+        response = requests.post(self.model_url, json=payload_txt, headers=headers)
+        response.raise_for_status()
+        output = response.json()
+        #st.write(output)
+        text = output["outputs"][0]["outputs"][0]["results"]["text"]["data"]["text"]
+        text = json.loads(text)
+        #st.write(text)
+        return text
+    
+    def fetch_data(self, data_field):
+        mongodb_uri = os.getenv("MONGODB_URI")
+        myclient = MongoClient(mongodb_uri)
+        mydb = myclient.get_database()
+        mycol = mydb["df_data"]
+        
+        # Sort by timestamp field in descending order
+        x = mycol.find_one(
+            {"data_field": data_field},
+            sort=[("timestamp", -1)]  
+        )
+        
+        x = x["result"]
+        return x
+    
+    def process(self):
+                with st.spinner('Snapshot...', show_time=True):
+                        st.write('')
+                        headers = {"Content-Type": "application/json", "x-api-key": f"{os.getenv('x-api-key')}"}         
+                        try:
+                                payload_txt = {"input_value": self.payload, "output_type": "text", "input_type": "chat"}
+                                payload_txt_model = self.request_model(payload_txt, headers)
+                                debug_info = {'data_field' : 'Snapshot Analyst', 'result': payload_txt_model}
+                                upload_response(debug_info)
+
+                        except Exception as e:
+                            pass
+                        st.session_state['analyzing'] = False    
+                      
+    def row1(self):
+            st.session_state['analyzing'] = False
+            self.payload = ""
+            
+            self.website_and_tools_data = get_analyst_response("Website and Tools Analyst")
+            self.sem_data = get_analyst_response("SEM/PPC Analyst")
+            self.seo_data = get_analyst_response("SEO Analyst")
+            self.social_media_data = get_analyst_response("Social Media Analyst")
+            self.content_data = get_analyst_response("Content Analyst")
+            self.marketpalce_data = get_analyst_response("Marketplace Analyst")
+            
+            analyst_data_dict = {
+                "Website and Tools": self.website_and_tools_data,
+                "SEM/PPC": self.sem_data,
+                "SEO": self.seo_data,
+                "Social Media": self.social_media_data,
+                "Content": self.content_data,
+                "Marketplace": self.marketpalce_data,
+            }
+
+            for analyst_name, data in analyst_data_dict.items():
+                self.payload += f"\n\n--- {analyst_name} Analysis ---\n"
+                if isinstance(data, list):
+                    self.payload += "\n".join(map(str, data))
+                else:
+                    self.payload += str(data)
+            
+            self.process()
+                                       
+if __name__ == "__main__":
+    st.set_page_config(layout="wide")
+
+upload = uploadFile()

helper/data_field.py

@@ -5,9 +5,9 @@ def data_field(data_src):
         myclient = MongoClient(mongodb_uri)
         mydb = myclient.get_database()
         mycol = mydb["df_data"]
-        x = mycol.find_one({"data_field": data_src})
-        x = x["result"]["question"]
-        #st.write(x)
+        x = mycol.find_one({"data_field": data_src},
+                sort=[('timestamp', -1)])
+        x = x["result"]
         return x
 
 def get_analyst_response(data_src):

pages/analyzing_page.py

@@ -13,6 +13,9 @@ from classes.response_content import Content
 from classes.response_sem_ppc import Sem_PPC
 from classes.response_marketplace import Marketplace
 from classes.response_target_market import TargetMarket
+from classes.response_df_overview import dfOverview
+from classes.response_executive_summary import ExecutiveSummary
+from classes.response_snapshot import Snapshot
 
 def run_analysis():
     # Placeholders for status updates
@@ -27,6 +30,9 @@ def run_analysis():
     sem_ppc = st.empty()
     marketplace = st.empty()
     target_market = st.empty()
+    df_overview_status = st.empty()
+    executive_summary_status = st.empty()
+    snapshot_status = st.empty()
 
     def run_off_page_analysis():
         try:
@@ -138,6 +144,16 @@ def run_analysis():
             target_market.error(f"Target Market Analysis failed: {e}")
             return None
         
+    def df_overview_analysis():
+        try:
+            df_overview_status.info("DF Overview Analysis...")
+            result = dfOverview(os.getenv('Model_DF_Overview_Analyst'))
+            df_overview_status.success("DF Overview Analysis completed successfully.")
+            return result
+        except Exception as e:
+            df_overview_status.error(f"DF Overview Analysis failed: {e}")
+            return None
+    
     # Create threads for concurrent execution
     off_page_thread = threading.Thread(target=run_off_page_analysis)
     on_page_thread = threading.Thread(target=run_on_page_analysis)
@@ -150,6 +166,7 @@ def run_analysis():
     content_sem_ppc_thread = threading.Thread(target=run_sem_ppc_analysis)
     marketplace_thread = threading.Thread(target=run_marketplace_analysis)
     target_market_thread = threading.Thread(target=run_target_market_analysis)
+    df_overview_thread = threading.Thread(target=df_overview_analysis)
 
     # Attach Streamlit context to threads
     add_script_run_ctx(off_page_thread)
@@ -163,6 +180,7 @@ def run_analysis():
     add_script_run_ctx(content_sem_ppc_thread)
     add_script_run_ctx(marketplace_thread)
     add_script_run_ctx(target_market_thread)
+    add_script_run_ctx(df_overview_thread)
 
     # Start threads
     off_page_thread.start()
@@ -176,6 +194,7 @@ def run_analysis():
     content_sem_ppc_thread.start()
     marketplace_thread.start()
     target_market_thread.start()
+    df_overview_thread.start()
 
     # Wait for threads to complete
     off_page_thread.join()
@@ -189,9 +208,27 @@ def run_analysis():
     content_sem_ppc_thread.join()
     marketplace_thread.join()
     target_market_thread.join()
+    df_overview_thread.join()
 
     st.markdown("---")
+    snapshot_status.info("Starting Snapshot by Channel Analysis...")
+    try:
+        snapshot = Snapshot(os.getenv('Model_Snapshot_by_Channel_Analyst'))
+        snapshot_status.success("Snapshot by Channel Analysis completed successfully.")
+    except Exception as e:
+        snapshot_status.error(f"Snapshot by Channel Analysis failed: {e}")    
+
+    executive_summary_status.info("Starting Executive Summary Analysis...")
+    try:
+        executive_summary = ExecutiveSummary(os.getenv('Model_Executive_Summary_Analyst'))
+        executive_summary_status.success("Executive Summary Analysis completed successfully.")
+    except Exception as e:
+        executive_summary_status.error(f"Executive Summary Analysis failed: {e}")    
+        st.success("🎉 All analyses completed!") # Final success message
+    
+
     st.success("🎉 All analyses completed!") # Final success message
+
     # --- Display Button After Completion ---
     if st.button("View Results", icon="📃"):
         st.switch_page("pages/output.py")

pages/home.py

@@ -54,7 +54,6 @@ class DigitalFootprintDashboard:
             else:
                 st.session_state["analyze"] = ''
 
-                    
             self.analyze_button = st.button("Analyze", icon="✨", use_container_width=True)
             if self.analyze_button == True:
                 st.switch_page("pages/analyzing_page.py")

pages/output.py

@@ -1,7 +1,7 @@
 import json
 import os
 import streamlit as st
-from helper.data_field import get_analyst_response
+from helper.data_field import get_analyst_response, data_field
 import time
 
 st.set_page_config(layout="centered") 
@@ -21,21 +21,21 @@ def write_client_footprint():
     markdown_table = "| Source/Channel | Current KPI |\n"
     markdown_table += "|---|---|\n"
     markdown_table += f"| Website Health Score | {result_web['website_overall_health_score']} |\n"
-    markdown_table += f"| Organic Traffic to the Website | {seo['organic_traffic']} |\n"
-    markdown_table += f"| Paid Traffic to the Website | {seo['paid_traffic']} |\n"
-    markdown_table += f"| Referral Traffic to the Website | {seo['referral_traffic']} |\n"
+    markdown_table += f"| Organic Traffic to the Website | {seo.get('organic_traffic', 'N/A')} |\n"
+    markdown_table += f"| Paid Traffic to the Website | {seo.get('paid_traffic', 'N/A')} |\n"
+    markdown_table += f"| Referral Traffic to the Website | {seo.get('referral_traffic', 'N/A')} |\n"
     markdown_table += f"| Email Traffic to the Website | N/A |\n"
-    markdown_table += f"| Direct Traffic to the Website | {seo['direct_traffic']} |\n"
+    markdown_table += f"| Direct Traffic to the Website | {seo.get('direct_traffic', 'N/A')} |\n"
     markdown_table += f"| Social Traffic to the Website | N/A |\n"
     markdown_table += f"| Display Traffic to the Website | N/A |\n"
     markdown_table += f"| Email Database | N/A |\n"
-    markdown_table += f"| Facebook Followers | {socmed['facebook_followers']} |\n"
-    markdown_table += f"| Twitter Followers | {socmed['twitter_followers']} |\n"
-    markdown_table += f"| Instagram Followers | {socmed['instagram_followers']} |\n"
-    markdown_table += f"| Linkedin Followers | {socmed['linkedin_followers']} |\n"
+    markdown_table += f"| Facebook Followers | {socmed.get('facebook_followers', 'N/A')} |\n"
+    markdown_table += f"| Twitter Followers | {socmed.get('twitter_followers', 'N/A')} |\n"
+    markdown_table += f"| Instagram Followers | {socmed.get('instagram_followers', 'N/A')} |\n"
+    markdown_table += f"| Linkedin Followers | {socmed.get('linkedin_followers', 'N/A')} |\n"
     markdown_table += f"| Google My Business | N/A |\n"
-    markdown_table += f"| # of Keywords Ranking in Top 10 | {seo['keyword_ranking_in_top_10']} |\n"
-    markdown_table += f"| # of Keywords Ranking in Top 100 | {seo['keyword_ranking_in_top_100']} |\n"
+    markdown_table += f"| # of Keywords Ranking in Top 10 | {seo.get('keyword_ranking_in_top_10', 'N/A')} |\n"
+    markdown_table += f"| # of Keywords Ranking in Top 100 | {seo.get('keyword_ranking_in_top_100', 'N/A')} |\n"
     
     return markdown_table
     
@@ -145,14 +145,11 @@ def seo_on_page_table(df_data):
     else:
         st.warning("No data retrieved for analysis.")
     # --- End: Loop and display data ---
-
-     
     
 def display_outputs():
-    st.markdown("<div id='top'></div>", unsafe_allow_html=True);
-    client_name = "RMX Creatives"
-    client_website = "https://rmxcreatives.com/"
-    overview = f"""**{client_name}** is a financial services company based in Auckland, New Zealand, specializing in providing quick and flexible loan solutions for businesses and individuals. Represented by Paul Stone, LoansOne has enlisted ShoreMarketing to perform a deep dive into their digital footprint to have a view of the holistic status of their digital properties and determine how each property can play part in implementing a stronger digital marketing plan.\n
+    client_name = data_field("Client Name")
+    client_website = data_field("Client Website")
+    overview = f"""{client_name} is a financial services company based in Auckland, New Zealand, specializing in providing quick and flexible loan solutions for businesses and individuals. Represented by Paul Stone, LoansOne has enlisted ShoreMarketing to perform a deep dive into their digital footprint to have a view of the holistic status of their digital properties and determine how each property can play part in implementing a stronger digital marketing plan.\n
 The Digital Marketing Footprint consists of deep-dive research by ShoreMarketing specialists to help the business leaders of LoansOne understand the effectiveness of their existing digital initiatives with the view of giving them an insight to developing a strategy and effectively allocating business resources to digital properties that will give them the best results.\n
 This document represents the results of our audit of LoansOne’s digital marketing and management practices. Our audit covered reviews of key digital areas: Website and Tools, PPC/SEM, SEO, Social Media, and Market Places."""
     
@@ -166,7 +163,7 @@ This document represents the results of our audit of LoansOne’s digital market
     st.markdown(f"{overview}")
     st.markdown("---")
     st.markdown("### Executive Summary")
-    st.markdown(f"Simtech LED's digital footprint reveals significant strengths and areas for improvement that can enhance its competitive positioning in the casino, gaming, and entertainment LED market. The analysis highlights the following key findings and recommendations")
+    st.markdown(get_analyst_response("Executive Summary Analyst"))
     st.markdown("---")
     
     st.markdown("### CLIENT FOOTPRINT")
@@ -285,11 +282,13 @@ Regardless, it is still a great channel worth investing to improve a business’
     st.write("TBD")
     
     st.markdown("##### WHAT IS THE PULL-THROUGH OFFER?")
-    st.write(get_analyst_response("Pull through offers Analyst"))
+    pull_through_data = get_analyst_response("Pull through offers Analyst")
+    st.write(pull_through_data)
     
     
     st.markdown("##### WEBSITE AUDIENCE ACQUISITION")
-    st.write(get_analyst_response("Website Audience Acquisition Analyst"))
+    website_audience_data = get_analyst_response("Website Audience Acquisition Analyst")
+    st.write(website_audience_data)
     
     #LLD/PM/LN
     lld_data = get_analyst_response("LLD/PM/LN Analyst")
@@ -328,25 +327,46 @@ We have evaluated the process of content development strategy and existing conte
         st.markdown("##### DECISION STAGE")
         st.write(None)
         
-    st.markdown("---")       
+    
     st.markdown("<a href='#top'>Go to top</a>", unsafe_allow_html=True)
+    st.markdown("---")       
+    
     
+    conversion = get_analyst_response("Conversion Analyst")
     st.markdown("#### CONVERSION –  ACTIVATION OF VISITORS")
-    st.markdown("##### AWARENESS TO TRAFFIC")
-    st.write("TBD")
-    st.markdown("##### TRAFFIC TO LEAD CONVERSION")
-    st.write("TBD")
-    st.markdown("##### LEAD TO SALES CONVERSION")
-    st.write("TBD")
-    st.markdown("##### CONVERSION TO BRAND LOYALTY")
-    st.write("TBD")
     
+    if conversion:
+        st.markdown("##### AWARENESS TO TRAFFIC")
+        st.write(conversion.get('awareness_to_traffic', 'N/A'))
+        
+        st.markdown("##### TRAFFIC TO LEAD CONVERSION")
+        st.write(conversion.get('traffic_to_lead', 'N/A'))
+        
+        st.markdown("##### LEAD TO SALES CONVERSION")
+        st.write(conversion.get('lead_to_sales', 'N/A'))
+        
+        st.markdown("##### CONVERSION TO BRAND LOYALTY")
+        st.write(conversion.get('conversion_to_brand', 'N/A'))
+    else:
+        st.markdown("##### AWARENESS TO TRAFFIC")
+        st.write(None)
+        st.markdown("##### TRAFFIC TO LEAD CONVERSION")
+        st.write(None)
+        st.markdown("##### LEAD TO SALES CONVERSION")
+        st.write(None)
+        st.markdown("##### CONVERSION TO BRAND LOYALTY")
+        st.write(None)
+    
+    
+    conversion = get_analyst_response("Connection Analyst")
     st.markdown("##### CONNECTION OF ALL ONLINE AND OFFLINE TOUCH POINTS")
-    st.write("TBD")
+    st.write(conversion)
     
     st.markdown("<a href='#top'>Go to top</a>", unsafe_allow_html=True)
     
-    
+
+
+st.markdown("<div id='top'></div>", unsafe_allow_html=True);    
 if st.button("Back to Dashboard", icon="🏠"):
         st.switch_page("pages/home.py")
 display_outputs()