updated the endpoints

main.py

@@ -21,6 +21,7 @@ business_interaction_graph = BusinessInteractionChatbot()
 brainstrom_graph = brainstroming_graph()
 
 stored_data={}
+# stored_data['business_details']={"business_type": "restaurant", "platform": "instagram", "target_audience": "youths", "business_goals": "to go global", "offerings": "nepali foods", "Challenges_faced": "finding new customers, attracting large customers"}
 
 class UserMessage(BaseModel):
     message: str
@@ -29,7 +30,8 @@ def context_analysis(msg: UserMessage):
     response = context_analysis_graph.chat(msg.message)
     if context_analysis_graph.is_complete(response):
         details = context_analysis_graph.extract_details()
-        details = details.model_dump()
+        if type(details) != dict:
+            details = details.model_dump()
         print('Business_details:',details)
         if isinstance(details, str):
             details= ast.literal_eval(details)
@@ -43,8 +45,10 @@ def context_analysis(msg: UserMessage):
 
 @app.post("/business-interaction")
 def business_interaction(interaction: str):
-    business_details = {"business_type": "restaurant", "platform": "instagram", "target_audience": "youths", "business_goals": "to go global", "offerings": "nepali foods", "Challenges_faced": "finding new customers, attracting large customers"}
-    response = business_interaction_graph.chat(interaction , business_details)
+    response,business_details = business_interaction_graph.chat(interaction , stored_data['business_details'])
+    if stored_data['business_details']!=business_details:
+        save_to_db(business_details)
+    stored_data['business_details']=business_details
     return {'response': response}