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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_openai import ChatOpenAI
+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 = os.environ.get("API_KEY") or config.get("API_KEY")
+endpoint = os.environ.get("OPENAI_API_BASE") or config.get("OPENAI_API_BASE")
+llama_api_key = os.environ.get("GROQ_API_KEY") or config2.get("LLAMA_KEY")
+MEM0_api_key = os.environ.get("MEM0_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
+Settings.embedding = 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: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Expands the user query to improve retrieval of nutrition-disorder information.
+
+    Args:
+        state: Workflow state containing at least 'query' and 'query_feedback'.
+
+    Returns:
+        Workflow state with an additional 'expanded_query' key.
+    """
+    s: AgentState = state
+
+    print("---------Expanding Query---------")
+    system_message = '''You are an assistant that reformulates vague or short user questions into detailed, domain-specific queries related to nutrition disorders.
+
+Examples:
+- Input: "What about iron?"
+  Expanded: "What are the common symptoms and treatments for iron deficiency anemia?"
+
+- Input: "Diets for gut issues?"
+  Expanded: "What dietary recommendations are effective for managing irritable bowel syndrome and promoting gut microbiome health?"
+
+- Input: "Sugar"
+  Expanded: "What are the risks of high sugar intake in diabetic patients and how can it be managed nutritionally?"
+'''
+    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
+
+
+chroma_client = chromadb.PersistentClient(path="./combined")
+
+vector_store = Chroma(
+    client=chroma_client,          # <- pass the client you just made
+    collection_name="combined",
+    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.metadata.get("original_content", doc.page_content),
+        "metadata": doc.metadata
+    }
+    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):
+    """
+    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 AI assistant trained to support healthcare providers in retrieving
+accurate and relevant information related to nutrition disorders. Your responses must strictly adhere to the
+retrieved context, which is extracted from the Nutritional Medical Reference or similar trusted sources.
+
+Do not speculate or introduce external knowledge. Focus only on symptoms, diagnoses, treatment plans, or
+other clinical details found in the context. If the context does not contain enough information to answer
+accurately, clearly state that. Aim for clarity, factual grounding, and relevance to the user's query.'''
+
+    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": state.get('feedback', 'No feedback provided') # add feedback to the prompt
+    })
+    state['response'] = response
+    print("intermediate response: ", response)
+
+    return state
+
+
+
+def score_groundedness(state):
+    """
+    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 evaluating whether an AI-generated response is grounded in the retrieved context
+provided from nutritional health documents. The context includes evidence and facts, and your task is to
+assign a groundedness score between 0 and 1, where:
+
+- 1.0 means the response is fully supported by the context,
+- 0.0 means the response is entirely unsupported.
+
+Be strict: if even a part of the response is not traceable to the context, reduce the score. Provide only
+the numeric score.'''
+
+    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 assessing whether an AI-generated response precisely answers the user's query,
+especially within the domain of nutritional health and disorders. Provide a precision score between 0 and 1:
+
+- 1.0: The response fully and directly answers the query with clear relevance.
+- 0.0: The response is vague, unrelated, or fails to address the query.
+
+Only return a numeric score.'''
+
+    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 assistant helping to improve AI-generated answers related to nutritional disorders.
+Evaluate the response and suggest constructive improvements to enhance accuracy, specificity, and completeness.
+Do not rewrite the response. Instead, point out what is vague, missing, or could be better explained.
+Focus on clinical terminology, nutritional details, and relevance to the user query.'''
+
+    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 retrieval assistant helping to improve search queries related to nutrition disorders.
+Analyze the original and expanded queries and provide suggestions to increase search precision.
+Focus on identifying missing clinical terms, relevant nutritional keywords, or clarifying the scope of the query.
+Do not rewrite the query. Provide suggestions only.'''
+
+    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'])
+
+    # Threshold logic: groundedness score should be at least 0.8
+    if state['groundedness_score'] >= 0.8:
+        print("Moving to precision")
+        return "check_precision"
+    else:
+        # Allow a maximum of 2 refinement loops
+        if state['groundedness_loop_count'] >  state['loop_max_iter']:
+            print("Maximum groundedness iterations reached")
+            return "max_iterations_reached"
+        else:
+            print("---------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'])
+
+    # Threshold for acceptable precision score
+    if state['precision_score'] >= 0.8:
+        return "pass"  # Complete the workflow
+    else:
+        # Check if maximum refinement attempts have been reached
+        if state['precision_loop_count'] > state['loop_max_iter']:
+            return "max_iterations_reached"
+        else:
+            print("---------Precision Score Threshold Not met. Refining Query-----------")
+            return "refine_query"
+
+
+
+
+def max_iterations_reached(state: Dict) -> Dict:
+
+    """Handles the case where max iterations are reached."""
+    print("---------max_iterations_reached---------")
+    state['response'] = "We need more context to provide an accurate answer."
+
+    return state
+
+
+
+from langgraph.graph import END, StateGraph, START
+
+def create_workflow() -> StateGraph:
+    """Creates the updated workflow for the AI nutrition agent."""
+    workflow = StateGraph(dict)  # Workflow state is a dictionary
+
+    # 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_groundedness)   # 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")
+
+    # Groundedness logic
+    workflow.add_conditional_edges(
+        "score_groundedness",
+        should_continue_groundedness,
+        {
+            "check_precision": "check_precision",
+            "refine_response": "refine_response",
+            "max_iterations_reached": "max_iterations_reached"
+        }
+    )
+
+    # Edge to reprocess refined response
+    workflow.add_edge("refine_response", "craft_response")
+
+    # Precision logic
+    workflow.add_conditional_edges(
+        "check_precision",
+        should_continue_precision,
+        {
+            "pass": END,
+            "refine_query": "refine_query",
+            "max_iterations_reached": "max_iterations_reached"
+        }
+    )
+
+    # Edge to re-expand refined query and reenter flow
+    workflow.add_edge("refine_query", "expand_query")
+    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) -> Dict[str, Any]:
+    """
+    Runs the RAG-based agent with conversation history for context-aware responses.
+    """
+    if not query or not isinstance(query, str):
+        return {"error": "Invalid or empty query provided"}
+    inputs = {
+        "query": query,
+        "expanded_query": "", #Initialized as an empty string since the expand_query function will populate this field with the reformulated query based on the original query
+        "context": [], # Retrieved documents (initially empty)
+        "response": "", #Initialized as an empty string since the craft_response function will generate the AI response and store it here
+        "precision_score": 0.0, #Initialized as 0.0 since the check_precision function will compute and assign a precision score between 0 and 1.
+        "groundedness_score": 0.0, #Initialized as 0.0 since the score_groundedness function will compute and assign a groundedness score between 0 and 1.
+        "groundedness_loop_count": 0, #Initialized as 0 to track the number of groundedness refinement loops, incremented in score_groundedness.
+        "precision_loop_count": 0, #Initialized as 0 to track the number of precision refinement loops, incremented in check_precision.
+        "feedback": "", #Initialized as an empty string since the refine_response function will populate this with suggestions for improving the response.
+        "query_feedback": "", #Initialized as an empty string since the refine_query function will populate this with suggestions for improving the expanded query.
+        "loop_max_iter": 5 
+    }
+    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 "meta-llama/llama-guard-4-12bb").
+
+    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=os.environ.get("MEM0_API_KEY")) # 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=os.environ.get("API_KEY"),  # API key for authentication
+            openai_api_base = os.environ.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
+        if st.session_state.get("login_submitted", False):
+            st.session_state.pop("login_submitted")
+            st.rerun()
+    else:
+        for message in st.session_state.chat_history:
+            with st.chat_message(message["role"]):
+                st.write(message["content"])
+
+        # === Filled Blanks ===
+        user_query = st.chat_input("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)
+
+            filtered_result = filter_input_with_llama_guard(user_query)
+            filtered_result = filtered_result.replace("\n", " ")
+
+            if filtered_result in ["safe", "unsafe S7", "unsafe S6"]:
+                try:
+                    if 'chatbot' not in st.session_state:
+                        st.session_state.chatbot = NutritionBot()
+                    response = st.session_state.chatbot.handle_customer_query(
+                        st.session_state.user_id, user_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()