Update app.py

app.py

@@ -9,6 +9,8 @@ from agents import *
 from config import *
 from workflow import create_workflow
 import logging
+import threading
+import queue
 
 # Initialize workflow
 graph = create_workflow()
@@ -19,41 +21,51 @@ logger = logging.getLogger(__name__)
 
 # Helper Functions
 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."
-        )
-
-        # Compose input for the LLM
-        user_details_summary = (
-            f"Name: {user_details.get('name', 'Unknown')}, "
-            f"Age: {user_details.get('age', 'Unknown')}, "
-            f"Gender: {user_details.get('gender', 'Unknown')}, "
-            f"Weight: {user_details.get('weight', 'Unknown')} kg, "
-            f"Height: {user_details.get('height', 'Unknown')} cm, "
-            f"Activity Level: {user_details.get('activity_level', 'Unknown')}"
-        )
-
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": f"User details: {user_details_summary}"},
-            {"role": "user", "content": input_message}
-        ]
-
-        # Generate the response
-        logger.debug("Invoking workflow with messages: %s", messages)
-        response = graph.invoke({"messages": messages}, timeout=10)  # Add a timeout for safety
-        logger.debug("Workflow response: %s", response)
-        return response["messages"][1]["content"]
-
-    except Exception as e:
-        logger.error("Error in run_graph: %s", str(e))
-        return f"An error occurred while processing your request: {e}"
+    def invoke_workflow(q):
+        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."
+            )
+
+            # Compose input for the LLM
+            user_details_summary = (
+                f"Name: {user_details.get('name', 'Unknown')}, "
+                f"Age: {user_details.get('age', 'Unknown')}, "
+                f"Gender: {user_details.get('gender', 'Unknown')}, "
+                f"Weight: {user_details.get('weight', 'Unknown')} kg, "
+                f"Height: {user_details.get('height', 'Unknown')} cm, "
+                f"Activity Level: {user_details.get('activity_level', 'Unknown')}"
+            )
+
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": f"User details: {user_details_summary}"},
+                {"role": "user", "content": input_message}
+            ]
+
+            logger.debug("Invoking workflow with messages: %s", messages)
+            response = graph.invoke({"messages": messages})
+            logger.debug("Workflow response: %s", response)
+            q.put(response["messages"][1]["content"])
+        except Exception as e:
+            logger.error("Error in run_graph: %s", str(e))
+            q.put(f"An error occurred while processing your request: {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=10)  # Wait for 10 seconds
+
+    if thread.is_alive():
+        logger.error("Workflow timed out.")
+        return "The request timed out. Please try again later."
+    return q.get()
 
 
 def calculate_bmi(height, weight):
@@ -189,13 +201,11 @@ with gr.Blocks() as demo:
                     ]
 
                     logger.debug("Submitting messages to LLM: %s", messages)
-                    response = graph.invoke({"messages": messages}, timeout=10)  # Add a timeout
+                    response = run_graph(message, history, user_details)
                     logger.debug("Received LLM response: %s", response)
 
-                    personalized_response = response["messages"][1]["content"]
-
                     history.append((f"User", message))
-                    history.append(("FIT.AI", personalized_response))
+                    history.append(("FIT.AI", response))
                     return history, chart_path
 
                 except Exception as e:
@@ -240,4 +250,4 @@ with gr.Blocks() as demo:
                 outputs=[bmi_output, calorie_output, bmi_chart_calc]
             )
 
-demo.launch(share=True)
+demo.launch(share=True)