Merge branch 'main' of https://huggingface.co/spaces/roniorque/df_ai_int into pr/7

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()

pages/analyzing_page.py

@@ -13,6 +13,8 @@ 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_executive_summary import ExecutiveSummary
+from classes.response_snapshot import Snapshot
 
 def run_analysis():
     # Placeholders for status updates
@@ -27,6 +29,8 @@ def run_analysis():
     sem_ppc = st.empty()
     marketplace = st.empty()
     target_market = st.empty()
+    executive_summary_status = st.empty()
+    snapshot_status = st.empty()
 
     def run_off_page_analysis():
         try:
@@ -137,7 +141,7 @@ def run_analysis():
         except Exception as e:
             target_market.error(f"Target Market 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)
@@ -191,7 +195,24 @@ def run_analysis():
     target_market_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

@@ -145,8 +145,6 @@ def seo_on_page_table(df_data):
     else:
         st.warning("No data retrieved for analysis.")
     # --- End: Loop and display data ---
-
-     
     
 def display_outputs():
     client_name = data_field("Client Name")
@@ -284,11 +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")