Add DF Overview analysis functionality and integrate into the analyzing page with concurrent execution

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()

pages/analyzing_page.py

@@ -13,6 +13,7 @@ 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
 
@@ -29,6 +30,7 @@ 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()
 
@@ -141,6 +143,16 @@ def run_analysis():
         except Exception as e:
             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)
@@ -154,6 +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)
@@ -167,6 +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()
@@ -180,6 +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()
@@ -193,6 +208,7 @@ 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...")