Update app.py

app.py

@@ -26,11 +26,17 @@ logger = logging.getLogger(__name__)
 def run_graph(input_message, history, user_details):
     def invoke_workflow(q):
         try:
+            # Determine if the query is general or personal
+            if "how" in input_message.lower() or "what" in input_message.lower():
+                general_query = True if "general" in input_message.lower() else False
+            else:
+                general_query = False
+
             system_prompt = (
                 "You are a fitness and health assistant. "
-                "Provide actionable, tailored advice based on the user's personal details and goals. "
-                "Incorporate BMI and daily caloric needs into your suggestions. "
-                "Offer steps for achieving goals like weight loss or fitness improvement."
+                "If the user's query is personal, provide tailored advice based on their details, including BMI and caloric needs. "
+                "If the query is general, respond broadly without relying on personal metrics. "
+                "Encourage the user to ask follow-up questions and maintain a conversational tone."
             )
 
             # Summarize user details
@@ -46,7 +52,7 @@ def run_graph(input_message, history, user_details):
             # Messages for the workflow
             messages = [
                 {"role": "system", "content": system_prompt},
-                {"role": "user", "content": f"User details: {user_details_summary}"},
+                {"role": "user", "content": f"User details: {user_details_summary}" if not general_query else "General query"},
                 {"role": "user", "content": input_message}
             ]
 
@@ -55,7 +61,7 @@ def run_graph(input_message, history, user_details):
             # Extract LLM response
             llm_response = None
             for msg in response.get("messages", []):
-                if isinstance(msg, HumanMessage) and msg.name in ["nutritionist", "workout_coach"]:
+                if isinstance(msg, HumanMessage) and msg.name in ["nutritionist", "workout_coach", "general_expert"]:
                     llm_response = msg.content
                     break
 
@@ -204,7 +210,7 @@ with gr.Blocks() as demo:
 
                 history.append(("User", message))
                 if isinstance(response, str):
-                    history.append(("FIT.AI", response))
+                    history.append(("FIT.AI", response + "\nLet me know if there's anything else you'd like to ask! 😊"))
                 else:
                     history.append(("FIT.AI", "An unexpected response was received."))