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app.py

@@ -572,10 +572,10 @@ class NutritionBot:
         """
         Initialize the NutritionBot class, setting up memory, the LLM client, tools, and the agent executor.
         """
-        print("Entered NutritionBot...")
+        st.write("Entered NutritionBot...")
         # Initialize a memory client to store and retrieve customer interactions
         self.memory = MemoryClient(api_key=MEM0_api_key) # userdata.get("mem0"))  # Complete the code to define the memory client API key
-        print("Got memory...")
+        st.write("Got memory...")
 
         # Initialize the OpenAI client using the provided credentials
         self.client = ChatOpenAI(
@@ -583,11 +583,11 @@ class NutritionBot:
             api_key=api_key, # API key for authentication
             temperature=0  # Controls randomness in responses; 0 ensures deterministic results
         )
-        print("Got client...")
+        st.write("Got client...")
 
         # Define tools available to the chatbot, such as web search
         tools = [agentic_rag]
-        print("Got tools...")
+        st.write("Got tools...")
 
         # Define the system prompt to set the behavior of the chatbot
         system_prompt = """You are a caring and knowledgeable Medical Support Agent, specializing in nutrition disorder-related guidance. Your goal is to provide accurate, empathetic, and tailored nutritional recommendations while ensuring a seamless customer experience.
@@ -603,7 +603,7 @@ class NutritionBot:
                           Your primary goal is to help customers make informed nutrition decisions that align with their health conditions and personal preferences.
 
         """
-        print("Got system prompt...")
+        st.write("Got system prompt...")
 
         # Build the prompt template for the agent
         prompt = ChatPromptTemplate.from_messages([
@@ -611,15 +611,15 @@ class NutritionBot:
             ("human", "{input}"),  # Placeholder for human input
             ("placeholder", "{agent_scratchpad}")  # Placeholder for intermediate reasoning steps
         ])
-        print("Got user prompt...")
+        st.write("Got user prompt...")
 
         # Create an agent capable of interacting with tools and executing tasks
         agent = create_tool_calling_agent(self.client, tools, prompt)
-        print("Got agent...")
+        st.write("Got agent...")
 
         # Wrap the agent in an executor to manage tool interactions and execution flow
         self.agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-        print("Got executor...")
+        st.write("Got executor...")
 
 
     def store_customer_interaction(self, user_id: str, message: str, response: str, metadata: Dict = None):
@@ -685,7 +685,7 @@ class NutritionBot:
 
         # Retrieve relevant past interactions for context
         relevant_history = self.get_relevant_history(user_id, query)
-        print("Got history...")
+        st.write("Got history...")
 
         # Build a context string from the relevant history
         context = "Previous relevant interactions:\n"
@@ -695,7 +695,7 @@ class NutritionBot:
             context += "---\n"
 
         # Print context for debugging purposes
-        print("Context: ", context)
+        st.write("Context: ", context)
 
         # Prepare a prompt combining past context and the current query
         prompt = f"""
@@ -710,7 +710,7 @@ class NutritionBot:
 
         # Generate a response using the agent
         response = self.agent_executor.invoke({"input": prompt})
-        print("Got response...")
+        st.write("Got response...")
 
         # Store the current interaction for future reference
         self.store_customer_interaction(
@@ -719,7 +719,7 @@ class NutritionBot:
             response=response["output"],
             metadata={"type": "support_query"}
         )
-        print("Got output...")
+        st.write("Got output...")
 
 
         # Return the chatbot's response
@@ -787,17 +787,17 @@ def nutrition_disorder_streamlit():
 
             # Check if input is safe based on allowed statuses
             if filtered_result in ["safe", "safe S7", "safe S6"]: # Blanks #3, #4, #5: Fill in with allowed safe statuses (e.g., "safe", "unsafe S7", "unsafe S6")
-                print("Got a safe input...")
+                st.write("Got a safe input...")
                 try:
                     if 'chatbot' not in st.session_state:
-                        print("In not in st.session_state...")
+                        st.write("In not in st.session_state...")
                         st.session_state.chatbot = NutritionBot() # Blank #6: Fill in with the chatbot class initialization (e.g., NutritionBot)
                     else:
-                        print("In st.session_state...")
+                        st.write("In st.session_state...")
 
-                    print("Getting response...")
+                    st.write("Getting response...")
                     response = st.session_state.chatbot.handle_customer_query(st.session_state.user_id, user_query) 
-                    print(f"Got response: {response}")
+                    st.write(f"Got response: {response}")
                     # Blank #7: Fill in with the method to handle queries (e.g., handle_customer_query)
                     st.write(response)
                     st.session_state.chat_history.append({"role": "assistant", "content": response})