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app.py

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+
+# Import necessary libraries
+import os  # Interacting with the operating system (reading/writing files)
+import chromadb  # High-performance vector database for storing/querying dense vectors
+from dotenv import load_dotenv  # Loading environment variables from a .env file
+import json  # Parsing and handling JSON data
+
+# LangChain imports
+from langchain_core.documents import Document  # Document data structures
+from langchain_core.runnables import RunnablePassthrough  # LangChain core library for running pipelines
+from langchain_core.output_parsers import StrOutputParser  # String output parser
+from langchain.prompts import ChatPromptTemplate  # Template for chat prompts
+from langchain.chains.query_constructor.base import AttributeInfo  # Base classes for query construction
+from langchain.retrievers.self_query.base import SelfQueryRetriever  # Base classes for self-querying retrievers
+from langchain.retrievers.document_compressors import LLMChainExtractor, CrossEncoderReranker  # Document compressors
+from langchain.retrievers import ContextualCompressionRetriever  # Contextual compression retrievers
+
+# LangChain community & experimental imports
+from langchain_community.vectorstores import Chroma  # Implementations of vector stores like Chroma
+from langchain_community.document_loaders import PyPDFDirectoryLoader, PyPDFLoader  # Document loaders for PDFs
+from langchain_community.cross_encoders import HuggingFaceCrossEncoder  # Cross-encoders from HuggingFace
+from langchain_experimental.text_splitter import SemanticChunker  # Experimental text splitting methods
+from langchain.text_splitter import (
+    CharacterTextSplitter,  # Splitting text by characters
+    RecursiveCharacterTextSplitter  # Recursive splitting of text by characters
+)
+from langchain_core.tools import tool
+from langchain.agents import create_tool_calling_agent, AgentExecutor
+from langchain_core.prompts import ChatPromptTemplate
+
+# LangChain OpenAI imports
+from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI  # OpenAI embeddings and models
+from langchain.embeddings.openai import OpenAIEmbeddings  # OpenAI embeddings for text vectors
+
+# LlamaParse & LlamaIndex imports
+from llama_parse import LlamaParse  # Document parsing library
+from llama_index.core import Settings, SimpleDirectoryReader  # Core functionalities of the LlamaIndex
+
+# LangGraph import
+from langgraph.graph import StateGraph, END, START  # State graph for managing states in LangChain
+
+# Pydantic import
+from pydantic import BaseModel  # Pydantic for data validation
+
+# Typing imports
+from typing import Dict, List, Tuple, Any, TypedDict  # Python typing for function annotations
+
+# Other utilities
+import numpy as np  # Numpy for numerical operations
+from groq import Groq
+from mem0 import MemoryClient
+import streamlit as st
+from datetime import datetime
+
+#====================================SETUP=====================================#
+# Fetch secrets from Hugging Face Spaces
+api_key = config.get("API_KEY")
+endpoint = config.get("OPENAI_API_BASE")
+llama_api_key = os.environ['LLAMA_API_KEY']
+MEM0_api_key = os.environ['mem0']
+
+# Initialize the OpenAI embedding function for Chroma
+embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
+    api_base=endpoint, # Complete the code to define the API base endpoint
+    api_key=api_key, # Complete the code to define the API key
+    model_name='text-embedding-ada-002' # This is a fixed value and does not need modification
+)
+
+# This initializes the OpenAI embedding function for the Chroma vectorstore, using the provided endpoint and API key.
+
+# Initialize the OpenAI Embeddings
+embedding_model = OpenAIEmbeddings(
+    openai_api_base=endpoint,
+    openai_api_key=api_key,
+    model='text-embedding-ada-002'
+)
+
+
+# Initialize the Chat OpenAI model
+llm = ChatOpenAI(
+    openai_api_base=endpoint,
+    openai_api_key=api_key,
+    model="gpt-4o-mini",
+    streaming=False
+)
+# This initializes the Chat OpenAI model with the provided endpoint, API key, deployment name, and a temperature setting of 0 (to control response variability).
+
+# set the LLM and embedding model in the LlamaIndex settings.
+Settings.llm = llm # Complete the code to define the LLM model
+Settings.embedding = embedding_model # Complete the code to define the embedding model
+
+#================================Creating Langgraph agent======================#
+
+class AgentState(TypedDict):
+    query: str  # The current user query
+    expanded_query: str  # The expanded version of the user query
+    context: List[Dict[str, Any]]  # Retrieved documents (content and metadata)
+    response: str  # The generated response to the user query
+    precision_score: float  # The precision score of the response
+    groundedness_score: float  # The groundedness score of the response
+    groundedness_loop_count: int  # Counter for groundedness refinement loops
+    precision_loop_count: int  # Counter for precision refinement loops
+    feedback: str
+    query_feedback: str
+    groundedness_check: bool
+    loop_max_iter: int
+
+def expand_query(state):
+    """
+    Expands the user query to improve retrieval of nutrition disorder-related information.
+
+    Args:
+        state (Dict): The current state of the workflow, containing the user query.
+
+    Returns:
+        Dict: The updated state with the expanded query.
+    """
+    print("---------Expanding Query---------")
+    system_message = """
+    You are a domain expert assisting in answering questions related to Nutritional Disorders.
+    Perform query expansion on the question received. If there are multiple common ways of phrasing a user question \
+    or common synonyms for key words in the question, make sure to return multiple versions \
+    of the query with the different phrasings.
+
+    If the query has multiple parts, split them into separate simpler queries. This is the only case where you can generate more than 3 queries.
+
+    If there are acronyms or words you are not familiar with, do not try to rephrase them.
+
+    Return only 3 versions of the question as a list.
+    Generate only a list of questions. Do not mention anything before or after the list.
+
+    Question:
+    {question}
+    """
+
+    expand_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Expand this query: {query} using the feedback: {query_feedback}")
+
+    ])
+
+    chain = expand_prompt | llm | StrOutputParser()
+    expanded_query = chain.invoke({"query": state['query'], "query_feedback":state["query_feedback"]})
+    print("expanded_query", expanded_query)
+    state["expanded_query"] = expanded_query
+    return state
+
+
+# Initialize the Chroma vector store for retrieving documents
+vector_store = Chroma(
+    collection_name="nutritional_hypotheticals",
+    persist_directory="./nutritional_db",
+    embedding_function=embedding_model
+
+)
+
+# Create a retriever from the vector store
+retriever = vector_store.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 3}
+)
+
+def retrieve_context(state):
+    """
+    Retrieves context from the vector store using the expanded or original query.
+
+    Args:
+        state (Dict): The current state of the workflow, containing the query and expanded query.
+
+    Returns:
+        Dict: The updated state with the retrieved context.
+    """
+    print("---------retrieve_context---------")
+    query = state['expanded_query']  # Complete the code to define the key for the expanded query
+    #print("Query used for retrieval:", query)  # Debugging: Print the query
+
+    # Retrieve documents from the vector store
+    docs = retriever.invoke(query)
+    print("Retrieved documents:", docs)  # Debugging: Print the raw docs object
+
+    # Extract both page_content and metadata from each document
+    context= [
+        {
+            "content": doc.page_content,  # The actual content of the document
+            "metadata": doc.metadata  # The metadata (e.g., source, page number, etc.)
+        }
+        for doc in docs
+    ]
+    state['context'] = context  # Complete the code to define the key for storing the context
+    print("Extracted context with metadata:", context)  # Debugging: Print the extracted context
+    #print(f"Groundedness loop count: {state['groundedness_loop_count']}")
+    return state
+
+
+
+def craft_response(state: Dict) -> Dict:
+    """
+    Generates a response using the retrieved context, focusing on nutrition disorders.
+
+    Args:
+        state (Dict): The current state of the workflow, containing the query and retrieved context.
+
+    Returns:
+        Dict: The updated state with the generated response.
+    """
+    print("---------craft_response---------")
+    system_message = """You are a helpful domain expert in nutritional disorders, that is well versed with crafting responses using the information \
+        you have gathered from various sources. You have to summarize the information you have gathered and present it in a coherent manner.
+        Add citations to the source using numbers in the response and end the response with the list of sources links used in the response."""
+
+    response_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Query: {query}\nContext: {context}\n\nfeedback: {feedback}")
+    ])
+
+    chain = response_prompt | llm
+    response = chain.invoke({
+        "query": state['query'],
+        "context": "\n".join([doc["content"] for doc in state['context']]),
+        "feedback": "How to improve the query for better results" # add feedback to the prompt
+    })
+    state['response'] = response
+    print("intermediate response: ", response)
+
+    return state
+
+
+
+def score_groundedness(state: Dict) -> Dict:
+    """
+    Checks whether the response is grounded in the retrieved context.
+
+    Args:
+        state (Dict): The current state of the workflow, containing the response and context.
+
+    Returns:
+        Dict: The updated state with the groundedness score.
+    """
+    print("---------check_groundedness---------")
+    system_message = '''You are an expert in evaluating if a response is grounded in the provided context.
+    You will be given a context and a response.
+    Your task is to evaluate the groundedness of the response based on the context.
+    Return a score between 0 and 1, where 1 means the response is fully grounded in the context and 0 means the response is not grounded in the context.
+    Return only the score as a float.'''
+
+    groundedness_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Context: {context}\nResponse: {response}\n\nGroundedness score:")
+    ])
+
+    chain = groundedness_prompt | llm | StrOutputParser()
+    groundedness_score = float(chain.invoke({
+        "context": "\n".join([doc["content"] for doc in state['context']]),
+        "response": state['response'] # Complete the code to define the response
+    }))
+    print("groundedness_score: ", groundedness_score)
+    state['groundedness_loop_count'] += 1
+    print("#########Groundedness Incremented###########")
+    state['groundedness_score'] = groundedness_score
+
+    return state
+
+
+
+def check_precision(state: Dict) -> Dict:
+    """
+    Checks whether the response precisely addresses the user’s query.
+
+    Args:
+        state (Dict): The current state of the workflow, containing the query and response.
+
+    Returns:
+        Dict: The updated state with the precision score.
+    """
+    print("---------check_precision---------")
+    system_message = '''You are an expert in evaluating if a response is precise and directly addresses the user's query.
+    You will be given a user query and a response.
+    Your task is to evaluate how well the response answers the user's query.
+    Return a score between 0 and 1, where 1 means the response is perfectly precise and directly answers the query, and 0 means the response is not at all precise or relevant to the query.
+    Return only the score as a float.'''
+
+    precision_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Query: {query}\nResponse: {response}\n\nPrecision score:")
+    ])
+
+    chain = precision_prompt | llm | StrOutputParser() # Complete the code to define the chain of processing
+    precision_score = float(chain.invoke({
+        "query": state['query'],
+        "response":state['response'] # Complete the code to access the response from the state
+    }))
+    state['precision_score'] = precision_score
+    print("precision_score:", precision_score)
+    state['precision_loop_count'] +=1
+    print("#########Precision Incremented###########")
+    return state
+
+
+
+def refine_response(state: Dict) -> Dict:
+    """
+    Suggests improvements for the generated response.
+
+    Args:
+        state (Dict): The current state of the workflow, containing the query and response.
+
+    Returns:
+        Dict: The updated state with response refinement suggestions.
+    """
+    print("---------refine_response---------")
+
+    system_message = '''You are an expert in providing constructive feedback on a generated response.
+    You will be given a user query and a response.
+    Your task is to identify potential gaps, ambiguities, or missing details in the response and suggest improvements to enhance accuracy and completeness.
+    Do not rewrite the response, only provide suggestions for improvement.'''
+
+    refine_response_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Query: {query}\nResponse: {response}\n\n"
+                 "What improvements can be made to enhance accuracy and completeness?")
+    ])
+
+    chain = refine_response_prompt | llm| StrOutputParser()
+
+    # Store response suggestions in a structured format
+    feedback = f"Previous Response: {state['response']}\nSuggestions: {chain.invoke({'query': state['query'], 'response': state['response']})}"
+    print("feedback: ", feedback)
+    print(f"State: {state}")
+    state['feedback'] = feedback
+    return state
+
+
+
+def refine_query(state: Dict) -> Dict:
+    """
+    Suggests improvements for the expanded query.
+
+    Args:
+        state (Dict): The current state of the workflow, containing the query and expanded query.
+
+    Returns:
+        Dict: The updated state with query refinement suggestions.
+    """
+    print("---------refine_query---------")
+    system_message = '''You are an expert in providing constructive feedback on a generated expanded query.
+    You will be given the original user query and the expanded query.
+    Your task is to identify missing details, specific keywords, or scope refinements that can enhance search precision.
+    Do not rewrite the expanded query, only provide structured suggestions for improvement.'''
+
+    refine_query_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Original Query: {query}\nExpanded Query: {expanded_query}\n\n"
+                 "What improvements can be made for a better search?")
+    ])
+
+    chain = refine_query_prompt | llm | StrOutputParser()
+
+    # Store refinement suggestions without modifying the original expanded query
+    query_feedback = f"Previous Expanded Query: {state['expanded_query']}\nSuggestions: {chain.invoke({'query': state['query'], 'expanded_query': state['expanded_query']})}"
+    print("query_feedback: ", query_feedback)
+    print(f"Groundedness loop count: {state['groundedness_loop_count']}")
+    state['query_feedback'] = query_feedback
+    return state
+
+
+
+def should_continue_groundedness(state):
+  """Decides if groundedness is sufficient or needs improvement."""
+  print("---------should_continue_groundedness---------")
+  print("groundedness loop count: ", state['groundedness_loop_count'])
+  if state['groundedness_score'] >= 0.7:  # Complete the code to define the threshold for groundedness
+      print("Moving to precision")
+      return "check_precision"
+  else:
+      if state["groundedness_loop_count"] > state['loop_max_iter']:
+        return "max_iterations_reached"
+      else:
+        print(f"---------Groundedness Score Threshold Not met. Refining Response-----------")
+        return "refine_response"
+
+
+def should_continue_precision(state: Dict) -> str:
+    """Decides if precision is sufficient or needs improvement."""
+    print("---------should_continue_precision---------")
+    print("precision loop count: ", state['precision_loop_count'])
+    if state['precision_score'] >= 0.7:  # Threshold for precision
+        return "pass"  # Complete the workflow
+    else:
+        if state["precision_loop_count"] > state['loop_max_iter']:  # Maximum allowed loops
+            return "max_iterations_reached"
+        else:
+            print(f"---------Precision Score Threshold Not met. Refining Query-----------")  # Debugging
+            return "refine_query"  # Refine the query
+
+
+
+
+def max_iterations_reached(state: Dict) -> Dict:
+    """Handles the case when the maximum number of iterations is reached."""
+    print("---------max_iterations_reached---------")
+    """Handles the case when the maximum number of iterations is reached."""
+    response = "I'm unable to refine the response further. Please provide more context or clarify your question."
+    state['response'] = response
+    return state
+
+
+
+from langgraph.graph import END, StateGraph, START
+
+def create_workflow() -> StateGraph:
+    """Creates the updated workflow for the AI nutrition agent."""
+    workflow = StateGraph(AgentState)  # Complete the code to define the initial state of the agent
+
+    # Add processing nodes
+    workflow.add_node("expand_query", expand_query)         # Step 1: Expand user query. Complete with the function to expand the query
+    workflow.add_node("retrieve_context", retrieve_context) # Step 2: Retrieve relevant documents. Complete with the function to retrieve context
+    workflow.add_node("craft_response", craft_response)     # Step 3: Generate a response based on retrieved data. Complete with the function to craft a response
+    workflow.add_node("score_groundedness", score_groundness)  # Step 4: Evaluate response grounding. Complete with the function to score groundedness
+    workflow.add_node("refine_response", refine_response)   # Step 5: Improve response if it's weakly grounded. Complete with the function to refine the response
+    workflow.add_node("check_precision", check_precision)   # Step 6: Evaluate response precision. Complete with the function to check precision
+    workflow.add_node("refine_query", refine_query)         # Step 7: Improve query if response lacks precision. Complete with the function to refine the query
+    workflow.add_node("max_iterations_reached", max_iterations_reached)  # Step 8: Handle max iterations. Complete with the function to handle max iterations
+
+    # Main flow edges
+    workflow.add_edge(START, "expand_query")
+    workflow.add_edge("expand_query", "retrieve_context")
+    workflow.add_edge("retrieve_context", "craft_response")
+    workflow.add_edge("craft_response", "score_groundedness")
+
+    # Conditional edges based on groundedness check
+    workflow.add_conditional_edges(
+        "score_groundedness",
+        should_continue_groundedness,  # Use the conditional function
+        {
+            "check_precision": check_precision,  # If well-grounded, proceed to precision check.
+            "refine_response": refine_response,  # If not, refine the response.
+            "max_iterations_reached": max_iterations_reached  # If max loops reached, exit.
+        }
+    )
+
+    workflow.add_edge("refine_response", "craft_response")  # Refined responses are reprocessed.
+
+    # Conditional edges based on precision check
+    workflow.add_conditional_edges(
+        "check_precision",
+        should_continue_precision,  # Use the conditional function
+        {
+            "pass": END,              # If precise, complete the workflow.
+            "refine_query": "refine_query",  # If imprecise, refine the query.
+            "max_iterations_reached": "max_iterations_reached"  # If max loops reached, exit.
+        }
+    )
+
+    workflow.add_edge("refine_query", "expand_query")  # Refined queries go through expansion again.
+
+    workflow.add_edge("max_iterations_reached", END)
+
+    return workflow
+
+
+
+
+#=========================== Defining the agentic rag tool ====================#
+WORKFLOW_APP = create_workflow().compile()
+@tool
+def agentic_rag(query: str):
+    """
+    Runs the RAG-based agent with conversation history for context-aware responses.
+
+    Args:
+        query (str): The current user query.
+
+    Returns:
+        Dict[str, Any]: The updated state with the generated response and conversation history.
+    """
+    # Initialize state with necessary parameters
+    inputs = {
+        "query": query,  # Current user query
+        "expanded_query": "",  # Complete the code to define the expanded version of the query
+        "context": [],  # Retrieved documents (initially empty)
+        "response": "",  # Complete the code to define the AI-generated response
+        "precision_score": 0.0,  # Complete the code to define the precision score of the response
+        "groundedness_score": 0.0,  # Complete the code to define the groundedness score of the response
+        "groundedness_loop_count": 0,  # Complete the code to define the counter for groundedness loops
+        "precision_loop_count": 0,  # Complete the code to define the counter for precision loops
+        "feedback": "",  # Complete the code to define the feedback
+        "query_feedback": "",  # Complete the code to define the query feedback
+        "loop_max_iter": 3  # Complete the code to define the maximum number of iterations for loops
+    }
+
+    output = WORKFLOW_APP.invoke(inputs)
+
+    return output
+
+
+#================================ Guardrails ===========================#
+llama_guard_client = Groq(api_key=llama_api_key)
+# Function to filter user input with Llama Guard
+def filter_input_with_llama_guard(user_input, model="meta-llama/llama-guard-4-12b"):
+    """
+    Filters user input using Llama Guard to ensure it is safe.
+
+    Parameters:
+    - user_input: The input provided by the user.
+    - model: The Llama Guard model to be used for filtering (default is "llama-guard-3-8b").
+
+    Returns:
+    - The filtered and safe input.
+    """
+    try:
+        # Create a request to Llama Guard to filter the user input
+        response = llama_guard_client.chat.completions.create(
+            messages=[{"role": "user", "content": user_input}],
+            model=model,
+        )
+        # Return the filtered input
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        print(f"Error with Llama Guard: {e}")
+        return None
+
+
+#============================= Adding Memory to the agent using mem0 ===============================#
+
+class NutritionBot:
+    def __init__(self):
+        """
+        Initialize the NutritionBot class, setting up memory, the LLM client, tools, and the agent executor.
+        """
+
+        # Initialize a memory client to store and retrieve customer interactions
+        self.memory = MemoryClient(api_key=userdata.get("mem0"))  # Complete the code to define the memory client API key
+
+        # Initialize the OpenAI client using the provided credentials
+        self.client = ChatOpenAI(
+            model_name="gpt-4o-mini",  # Specify the model to use (e.g., GPT-4 optimized version)
+            api_key=config.get("API_KEY"),  # API key for authentication
+            endpoint = config.get("OPENAI_API_BASE"),
+            #openai_api_base = config.get("OPENAI_API_BASE"),
+            temperature=0  # Controls randomness in responses; 0 ensures deterministic results
+        )
+
+        # Define tools available to the chatbot, such as web search
+        tools = [agentic_rag]
+
+        # 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.
+                          Guidelines for Interaction:
+                          Maintain a polite, professional, and reassuring tone.
+                          Show genuine empathy for customer concerns and health challenges.
+                          Reference past interactions to provide personalized and consistent advice.
+                          Engage with the customer by asking about their food preferences, dietary restrictions, and lifestyle before offering recommendations.
+                          Ensure consistent and accurate information across conversations.
+                          If any detail is unclear or missing, proactively ask for clarification.
+                          Always use the agentic_rag tool to retrieve up-to-date and evidence-based nutrition insights.
+                          Keep track of ongoing issues and follow-ups to ensure continuity in support.
+                          Your primary goal is to help customers make informed nutrition decisions that align with their health conditions and personal preferences.
+
+        """
+
+        # Build the prompt template for the agent
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", system_prompt),  # System instructions
+            ("human", "{input}"),  # Placeholder for human input
+            ("placeholder", "{agent_scratchpad}")  # Placeholder for intermediate reasoning steps
+        ])
+
+        # Create an agent capable of interacting with tools and executing tasks
+        agent = create_tool_calling_agent(self.client, tools, prompt)
+
+        # Wrap the agent in an executor to manage tool interactions and execution flow
+        self.agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+
+    def store_customer_interaction(self, user_id: str, message: str, response: str, metadata: Dict = None):
+        """
+        Store customer interaction in memory for future reference.
+
+        Args:
+            user_id (str): Unique identifier for the customer.
+            message (str): Customer's query or message.
+            response (str): Chatbot's response.
+            metadata (Dict, optional): Additional metadata for the interaction.
+        """
+        if metadata is None:
+            metadata = {}
+
+        # Add a timestamp to the metadata for tracking purposes
+        metadata["timestamp"] = datetime.now().isoformat()
+
+        # Format the conversation for storage
+        conversation = [
+            {"role": "user", "content": message},
+            {"role": "assistant", "content": response}
+        ]
+
+        # Store the interaction in the memory client
+        self.memory.add(
+            conversation,
+            user_id=user_id,
+            output_format="v1.1",
+            metadata=metadata
+        )
+
+
+    def get_relevant_history(self, user_id: str, query: str) -> List[Dict]:
+        """
+        Retrieve past interactions relevant to the current query.
+
+        Args:
+            user_id (str): Unique identifier for the customer.
+            query (str): The customer's current query.
+
+        Returns:
+            List[Dict]: A list of relevant past interactions.
+        """
+        return self.memory.search(
+            query=query,  # Search for interactions related to the query
+            user_id=user_id,  # Restrict search to the specific user
+            limit=5  # Complete the code to define the limit for retrieved interactions
+        )
+
+
+    def handle_customer_query(self, user_id: str, query: str) -> str:
+        """
+        Process a customer's query and provide a response, taking into account past interactions.
+
+        Args:
+            user_id (str): Unique identifier for the customer.
+            query (str): Customer's query.
+
+        Returns:
+            str: Chatbot's response.
+        """
+
+        # Retrieve relevant past interactions for context
+        relevant_history = self.get_relevant_history(user_id, query)
+
+        # Build a context string from the relevant history
+        context = "Previous relevant interactions:\n"
+        for memory in relevant_history:
+            context += f"Customer: {memory['memory']}\n"  # Customer's past messages
+            context += f"Support: {memory['memory']}\n"  # Chatbot's past responses
+            context += "---\n"
+
+        # Print context for debugging purposes
+        print("Context: ", context)
+
+        # Prepare a prompt combining past context and the current query
+        prompt = f"""
+        Context:
+        {context}
+
+        Current customer query: {query}
+
+        Provide a helpful response that takes into account any relevant past interactions.
+        """
+
+        # Generate a response using the agent
+        response = self.agent_executor.invoke({"input": prompt})
+
+        # Store the current interaction for future reference
+        self.store_customer_interaction(
+            user_id=user_id,
+            message=query,
+            response=response["output"],
+            metadata={"type": "support_query"}
+        )
+
+        # Return the chatbot's response
+        return response['output']
+
+
+#=====================User Interface using streamlit ===========================#
+def nutrition_disorder_streamlit():
+    """
+    A Streamlit-based UI for the Nutrition Disorder Specialist Agent.
+    """
+    st.title("Nutrition Disorder Specialist")
+    st.write("Ask me anything about nutrition disorders, symptoms, causes, treatments, and more.")
+    st.write("Type 'exit' to end the conversation.")
+
+    # Initialize session state for chat history and user_id if they don't exist
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = []
+    if 'user_id' not in st.session_state:
+        st.session_state.user_id = None
+
+    # Login form: Only if user is not logged in
+    if st.session_state.user_id is None:
+        with st.form("login_form", clear_on_submit=True):
+            user_id = st.text_input("Please enter your name to begin:")
+            submit_button = st.form_submit_button("Login")
+            if submit_button and user_id:
+                st.session_state.user_id = user_id
+                st.session_state.chat_history.append({
+                    "role": "assistant",
+                    "content": f"Welcome, {user_id}! How can I help you with nutrition disorders today?"
+                })
+                st.session_state.login_submitted = True  # Set flag to trigger rerun
+        if st.session_state.get("login_submitted", False):
+            st.session_state.pop("login_submitted")
+            st.rerun()
+    else:
+        # Display chat history
+        for message in st.session_state.chat_history:
+            with st.chat_message(message["role"]):
+                st.write(message["content"])
+
+        # Chat input with custom placeholder text
+        user_query = st.chat_input("Type your question here or 'exit' to end)...")  # Blank #1: Fill in the chat input prompt (e.g., "Type your question here (or 'exit' to end)...")
+        if user_query:
+            if user_query.lower() == "exit":
+                st.session_state.chat_history.append({"role": "user", "content": "exit"})
+                with st.chat_message("user"):
+                    st.write("exit")
+                goodbye_msg = "Goodbye! Feel free to return if you have more questions about nutrition disorders."
+                st.session_state.chat_history.append({"role": "assistant", "content": goodbye_msg})
+                with st.chat_message("assistant"):
+                    st.write(goodbye_msg)
+                st.session_state.user_id = None
+                st.rerun()
+                return
+
+            st.session_state.chat_history.append({"role": "user", "content": user_query})
+            with st.chat_message("user"):
+                st.write(user_query)
+
+            # Filter input using Llama Guard
+            filtered_result = filter_input_with_llama_guard(user_query)  # Blank #2: Fill in with the function name for filtering input (e.g., filter_input_with_llama_guard)
+            filtered_result = filtered_result.replace("\n", " ")  # Normalize the result
+
+            # Check if input is safe based on allowed statuses
+            if filtered_result in ["safe", "unsafe S6", "unsafe S7"]:  # Blanks #3, #4, #5: Fill in with allowed safe statuses (e.g., "safe", "unsafe S7", "unsafe S6")
+                try:
+                    if 'chatbot' not in st.session_state:
+                        st.session_state.chatbot = NutritionBot()  # Blank #6: Fill in with the chatbot class initialization (e.g., NutritionBot)
+                    response = st.session_state.chatbot.handle_customer_query(st.session_state.user_id, user_query)
+                    # 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})
+                except Exception as e:
+                    error_msg = f"Sorry, I encountered an error while processing your query. Please try again. Error: {str(e)}"
+                    st.write(error_msg)
+                    st.session_state.chat_history.append({"role": "assistant", "content": error_msg})
+            else:
+                inappropriate_msg = "I apologize, but I cannot process that input as it may be inappropriate. Please try again."
+                st.write(inappropriate_msg)
+                st.session_state.chat_history.append({"role": "assistant", "content": inappropriate_msg})
+
+if __name__ == "__main__":
+    nutrition_disorder_streamlit()