Update app.py

app.py

@@ -25,14 +25,12 @@ def run_graph(input_message, history, user_details):
         try:
             system_prompt = (
                 "You are a fitness and health assistant. "
-                "Provide advice based on the user's personal details and goals. "
-                "If user details like weight, height, and activity level are provided, prioritize personalized advice. "
-                "Incorporate metrics such as BMI and daily caloric needs directly into your suggestions. "
-                "Visualize the user's metrics and provide actionable advice tailored to their needs. "
-                "If details are missing, provide general advice but encourage users to share their metrics for tailored guidance."
+                "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."
             )
 
-            # Compose input for the LLM
+            # Summarize user details
             user_details_summary = (
                 f"Name: {user_details.get('name', 'Unknown')}, "
                 f"Age: {user_details.get('age', 'Unknown')}, "
@@ -42,6 +40,7 @@ def run_graph(input_message, history, user_details):
                 f"Activity Level: {user_details.get('activity_level', 'Unknown')}"
             )
 
+            # Messages for the workflow
             messages = [
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": f"User details: {user_details_summary}"},
@@ -51,20 +50,20 @@ def run_graph(input_message, history, user_details):
             logger.debug("Invoking workflow with messages: %s", messages)
             response = graph.invoke({"messages": messages})
             logger.debug("Workflow response: %s", response)
-            # Check for proper response structure
+
+            # Check and return tailored response
             if "messages" in response and len(response["messages"]) > 1:
                 q.put(response["messages"][1].get("content", "No content in response."))
             else:
                 q.put("The workflow did not return a valid response. Please try again.")
         except Exception as e:
             logger.error("Error in run_graph: %s", str(e))
-            q.put(f"An error occurred while processing your request: {e}")
+            q.put(f"An error occurred: {e}")
 
-    # Create a queue and thread for timeout handling
     q = queue.Queue()
     thread = threading.Thread(target=invoke_workflow, args=(q,))
     thread.start()
-    thread.join(timeout=300)  # Increased timeout to 60 seconds
+    thread.join(timeout=30)
 
     if thread.is_alive():
         logger.error("Workflow timed out.")
@@ -181,6 +180,7 @@ with gr.Blocks() as demo:
                     chart_path = visualize_bmi_and_calories(bmi, calories)
 
                     user_prompt = (
+                        f"User wants advice on: {message}\n"
                         f"User Details:\n"
                         f"- Name: {user_details['name']}\n"
                         f"- Age: {user_details['age']}\n"
@@ -193,7 +193,7 @@ with gr.Blocks() as demo:
                         f"\nProvide tailored advice based on these metrics."
                     )
 
-                    response = run_graph(message, history, user_details)
+                    response = run_graph(user_prompt, history, user_details)
                     history.append((f"User", message))
                     history.append(("FIT.AI", response))
                     return history, chart_path