Update app.py

app.py

@@ -8,10 +8,15 @@ from tools import tools
 from agents import *
 from config import *
 from workflow import create_workflow
+import logging
 
 # Initialize workflow
 graph = create_workflow()
 
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
 # Helper Functions
 def run_graph(input_message, history, user_details):
     try:
@@ -41,10 +46,13 @@ def run_graph(input_message, history, user_details):
         ]
 
         # Generate the response
-        response = graph.invoke({"messages": messages})
+        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}"
 
 
@@ -141,50 +149,58 @@ with gr.Blocks() as demo:
             clear_button = gr.Button("Clear Chat")
 
             def submit_message(message, history=[]):
-                user_details = {
-                    "name": user_name.value,
-                    "age": user_age.value,
-                    "weight": user_weight.value,
-                    "height": user_height.value,
-                    "activity_level": activity_level.value,
-                    "gender": user_gender.value
-                }
-
-                bmi, status = calculate_bmi(user_details['height'], user_details['weight'])
-                calories = calculate_calories(
-                    user_details['age'], user_details['weight'], user_details['height'], user_details['activity_level'], user_details['gender']
-                )
-                chart_path = visualize_bmi_and_calories(bmi, calories)
-
-                # Compose input for the LLM dynamically
-                system_prompt = (
-                    "You are a fitness and health assistant. Provide detailed advice based on the user's metrics, including BMI and caloric needs."
-                )
-
-                user_prompt = (
-                    f"User Details:\n"
-                    f"- Name: {user_details['name']}\n"
-                    f"- Age: {user_details['age']}\n"
-                    f"- Gender: {user_details['gender']}\n"
-                    f"- Weight: {user_details['weight']} kg\n"
-                    f"- Height: {user_details['height']} cm\n"
-                    f"- Activity Level: {user_details['activity_level']}\n"
-                    f"- BMI: {bmi:.2f} ({status})\n"
-                    f"- Daily Caloric Needs: {calories:.2f} kcal\n"
-                    f"\nProvide tailored advice based on these metrics."
-                )
-
-                messages = [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": user_prompt}
-                ]
-
-                response = graph.invoke({"messages": messages})
-                personalized_response = response["messages"][1]["content"]
-
-                history.append((f"User", message))
-                history.append(("FIT.AI", personalized_response))
-                return history, chart_path
+                try:
+                    user_details = {
+                        "name": user_name.value,
+                        "age": user_age.value,
+                        "weight": user_weight.value,
+                        "height": user_height.value,
+                        "activity_level": activity_level.value,
+                        "gender": user_gender.value
+                    }
+
+                    bmi, status = calculate_bmi(user_details['height'], user_details['weight'])
+                    calories = calculate_calories(
+                        user_details['age'], user_details['weight'], user_details['height'], user_details['activity_level'], user_details['gender']
+                    )
+                    chart_path = visualize_bmi_and_calories(bmi, calories)
+
+                    # Compose input for the LLM dynamically
+                    system_prompt = (
+                        "You are a fitness and health assistant. Provide detailed advice based on the user's metrics, including BMI and caloric needs."
+                    )
+
+                    user_prompt = (
+                        f"User Details:\n"
+                        f"- Name: {user_details['name']}\n"
+                        f"- Age: {user_details['age']}\n"
+                        f"- Gender: {user_details['gender']}\n"
+                        f"- Weight: {user_details['weight']} kg\n"
+                        f"- Height: {user_details['height']} cm\n"
+                        f"- Activity Level: {user_details['activity_level']}\n"
+                        f"- BMI: {bmi:.2f} ({status})\n"
+                        f"- Daily Caloric Needs: {calories:.2f} kcal\n"
+                        f"\nProvide tailored advice based on these metrics."
+                    )
+
+                    messages = [
+                        {"role": "system", "content": system_prompt},
+                        {"role": "user", "content": user_prompt}
+                    ]
+
+                    logger.debug("Submitting messages to LLM: %s", messages)
+                    response = graph.invoke({"messages": messages}, timeout=10)  # Add a timeout
+                    logger.debug("Received LLM response: %s", response)
+
+                    personalized_response = response["messages"][1]["content"]
+
+                    history.append((f"User", message))
+                    history.append(("FIT.AI", personalized_response))
+                    return history, chart_path
+
+                except Exception as e:
+                    logger.error("Error in submit_message: %s", str(e))
+                    return history + [("FIT.AI", "An error occurred while processing your request." + str(e))], None
 
             submit_button.click(submit_message, inputs=[text_input, chatbot], outputs=[chatbot, bmi_chart])
             clear_button.click(lambda: ([], ""), inputs=None, outputs=[chatbot, bmi_chart])
@@ -209,10 +225,14 @@ with gr.Blocks() as demo:
             calculate_button = gr.Button("Calculate")
 
             def calculate_metrics(age, weight, height, gender, activity_level):
-                bmi, status = calculate_bmi(height, weight)
-                bmi_path = visualize_bmi_and_calories(bmi, calculate_calories(age, weight, height, activity_level, gender))
-                calories = calculate_calories(age, weight, height, activity_level, gender)
-                return f"Your BMI is {bmi:.2f}, considered {status}.", f"Daily calorie needs: {calories:.2f} kcal", bmi_path
+                try:
+                    bmi, status = calculate_bmi(height, weight)
+                    bmi_path = visualize_bmi_and_calories(bmi, calculate_calories(age, weight, height, activity_level, gender))
+                    calories = calculate_calories(age, weight, height, activity_level, gender)
+                    return f"Your BMI is {bmi:.2f}, considered {status}.", f"Daily calorie needs: {calories:.2f} kcal", bmi_path
+                except Exception as e:
+                    logger.error("Error in calculate_metrics: %s", str(e))
+                    return "An error occurred.", "", None
 
             calculate_button.click(
                 calculate_metrics,