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app.py

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+import os
+import numpy as np
+import streamlit as st
+from datetime import datetime
+from typing import Any, Dict, List, Tuple, TypedDict
+
+# LangChain imports
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.tools import tool
+from langchain_core.documents import Document
+
+from langchain.agents import (
+    AgentExecutor,
+    create_tool_calling_agent,
+    create_openai_tools_agent,
+    initialize_agent,
+    AgentType
+)
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain.retrievers.document_compressors import LLMChainExtractor, CrossEncoderReranker
+from langchain.chains.query_constructor.base import AttributeInfo
+
+# LangChain Community imports
+from langchain_community.document_loaders import PyPDFDirectoryLoader, PyPDFLoader
+from langchain_community.vectorstores import Chroma
+from langchain_community.cross_encoders import HuggingFaceCrossEncoder
+
+# LangChain OpenAI specific imports
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+# Misc imports
+from langgraph.graph import END, StateGraph, START
+from pydantic import BaseModel, Field, validator
+import chromadb
+from tqdm import tqdm
+from llama_index.core.settings import Settings
+from groq import Groq  # Llama Guard client for filtering user input
+from mem0 import MemoryClient
+
+# Fix numpy float type for compatibility
+np.float_ = np.float64
+#====================================
+# Environment setup
+api_key = os.getenv("OPENAI_API_KEY")
+endpoint = os.getenv("OPENAI_API_BASE")
+memo_api_key = os.getenv('mem0')
+
+# Initialize the OpenAI Embeddings
+embedding_model = OpenAIEmbeddings(
+    openai_api_base=endpoint,  # Fill in the endpoint
+    openai_api_key=api_key,  # Fill in the API key
+    model='text-embedding-ada-002'                 # Fill in the model name
+)
+
+# Initialize LLM
+llm = ChatOpenAI(
+    openai_api_base=endpoint,  # Fill in the endpoint
+    openai_api_key=api_key,  # Fill in the API key
+    model="gpt-4o-mini",  # Fill in the deployment name (e.g., gpt-4o-mini)
+    streaming=False)
+
+# Configure settings
+Settings.llm = llm
+Settings.embedding = embedding_model
+#====================================
+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  # Feedback from the user
+    query_feedback: str  # Feedback specifically related to the query
+    groundedness_check: bool  # Indicator for groundedness check
+    loop_max_iter: int  # Maximum iterations for loops
+#====================================
+def expand_query(state):
+    print("---------Expanding Query---------")
+
+    system_message = """
+      You are a domain expert assisting in answering questions related to medical reference documentation.
+      Convert the user query into more specific and domain-related phrasing that a Nutrition Disorder Specialist would understand.
+      Expand the query by considering the use of appropriate medical terminology, synonyms, and various common ways to phrase the query.
+
+      Guidelines:
+      If the query has multiple distinct parts, break them into separate, simpler queries.
+      If there are common synonyms or alternative phrasing for key terms, provide multiple versions of the query.
+      Do not generate more than three queries, except when the query involves multiple separate parts (in which case, you can generate more than three).
+      Do not attempt to rephrase unfamiliar acronyms or terms. Leave them as is.
+      Return only a list of up to three queries. Do not include anything before or after the list.
+    """
+
+    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='Nutrition',  # Complete the code to define the collection name
+    persist_directory='./nutritional_db',  # Complete the code to define the directory for persistence
+    embedding_function=embedding_model  # Complete the code to define the embedding function
+)
+
+# Create a retriever from the vector store
+retriever = vector_store.as_retriever(
+    search_type='similarity',  # Complete the code to define the search type
+    search_kwargs={'k': 5}  # Complete the code to define the number of results to retrieve
+)
+#====================================
+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 = """
+    Ensure the information is grounded in the context, avoid speculation, and prioritize clarity.
+    You are a knowledgeable and empathetic medical assistant specializing in nutritional disorders.
+    Given the retrieved context, generate a precise, informative, and concise response that directly addresses the user's query.
+    Ensure the information is fully grounded in the provided context, and avoid introducing speculative or unsupported content.
+    Focus on clarity and accuracy, ensuring the user receives helpful and relevant advice regarding nutritional disorders.
+    """
+
+    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['feedback'] # 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 evaluator for Retrieval-Augmented Generation (RAG) systems. Your task is to assess the GROUNDEDNESS of a response.
+      Given an answer and its retrieved context, determine whether the response is based on or supported by the provided context.
+      Respond with:
+      1.0 if the answer is grounded in the context
+      0.0 if the answer is not grounded in the context
+      The output must be a float: either 1.0 or 0.0. Do not include any explanation.
+    """
+
+    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 evaluator for Retrieval-Augmented Generation (RAG) systems. Your task is to assess the PRECISION of a response.
+    Given a query and a response, determine whether the response precisely addresses the user's query without including unrelated or irrelevant information.
+    Respond with:
+    1.0 if the response is precise
+    0.0 if the response is not precise
+    Your answer must be a float: either 1.0 or 0.0. Do not include any explanation.
+    """
+
+    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 evaluator for medical responses. Given the generated response, your task is to identify areas for improvement.
+    Provide feedback on any gaps, ambiguities, or missing details in the response.
+    Ensure that the suggestions focus on:
+    Factual grounding: Ensure the information is scientifically accurate and well-supported by evidence.
+    Logic and coherence: Assess the clarity and flow of the response. Ensure the response makes sense and is easy to follow.
+    Completeness: Identify any missing details or important context that should be included.
+    Empathy and tone: If the response is intended for a medical or patient-facing audience, ensure the tone is supportive, clear, and empathetic.
+
+    After reviewing the response, provide detailed suggestions for improvement in the following format:
+    1. [Description of Issue] - [Suggested Improvement].
+    2. [Description of Issue] - [Suggested Improvement].
+    (Continue providing numbered suggestions as needed.)
+
+    Make sure to provide constructive, actionable suggestions that can enhance the response.
+    """
+
+    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 query refinement, helping to improve search precision for medical topics.
+    Given the original and expanded queries, your task is to suggest improvements that can enhance search effectiveness.
+
+    Areas to focus on:
+    1. **Missing Details**: Identify any essential details or context that could improve the specificity of the query.
+    2. **Keywords**: Recommend more relevant or precise keywords that are specific to the topic (e.g., nutrition disorders, medical terminology).
+    3. **Scope Refinement**: Suggest ways to narrow or broaden the query to improve the relevance of the results (e.g., focus on specific conditions, age groups, etc.).
+    4. **Clarity**: Ensure the query is clear, concise, and free from ambiguity to help improve search engine interpretation.
+    5. **Synonym Usage**: Suggest any common synonyms or alternate phrases for key terms that could improve search recall.
+
+    After reviewing the original and expanded queries, provide your suggestions in a clear and actionable format, such as:
+    1. [Description of Issue] - [Suggested Improvement].
+    2. [Description of Issue] - [Suggested Improvement].
+    (Continue providing numbered suggestions as needed.)
+
+    Your goal is to make the query more precise, comprehensive, and search-friendly to enhance the retrieval of relevant information.
+    """
+
+    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:  # 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:  # 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
+#====================================
+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_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")
+
+    # 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
+#====================================
+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,  # Complete the code to define the precision score of the response
+        "groundedness_score": 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)
+
+    # Extract the AI response from the output
+    response = output.get("response", "Error: No response generated.")
+
+    # Check if the response is an AIMessage, then extract its content
+    if isinstance(response, str):
+      return response.strip()
+    elif hasattr(response, "content"):
+        return response.content.strip()
+    else:
+      # Handle unexpected response types if necessary
+      return str(response).strip()
+#====================================
+# Retrieve the Llama API key from user data
+groq_api_key = os.getenv('Groq') # Complete the code to define the key name for retrieving the API key
+
+# Initialize the Llama Guard client with the API key
+llama_guard_client = Groq(api_key=groq_api_key) # Complete the code to provide the API key for the Llama Guard client
+#====================================
+# Function to filter user input with Llama Guard
+def filter_input_with_llama_guard(user_input, model="llama-guard-3-8b"):
+    """
+    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
+        llama_response = llama_guard_client.chat.completions.create(
+            messages=[{"role": "user", "content": user_input}],
+            model=model,
+        )
+        # Return the filtered input
+        return llama_response.choices[0].message.content.strip()
+    except Exception as e:
+        print(f"Error with Llama Guard: {e}")
+        return None
+#====================================
+class NutritionBot:
+    def __init__(self):
+        """
+        Initialize the NutritionBot class with memory, LLM client, tools, and the agent executor.
+        """
+
+        # Memory to store/retrieve customer interactions
+        self.memory = MemoryClient(api_key=os.getenv("Mem0"))
+
+        # LLM setup
+        self.llm = ChatOpenAI(
+            model="gpt-4o-mini",
+            openai_api_key=os.getenv("API_KEY"),
+            openai_api_base=os.getenv("OPENAI_API_BASE"),
+            temperature=0,
+            streaming=False
+        )
+
+        # 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
+        ])
+
+        # Tool setup
+        self.tools = [agentic_rag]
+
+        # Agent initialization
+        self.agent_executor = initialize_agent(
+            tools=self.tools,
+            llm=self.llm,
+            prompt=prompt,
+            agent=AgentType.OPENAI_FUNCTIONS,
+            verbose=True,
+            handle_parsing_errors=True,
+            return_intermediate_steps=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"{memory}\n---\n"
+        print(f"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.
+        """
+        try:
+            response = self.agent_executor.invoke({"input": prompt})
+        except Exception as e:
+            print(f"An error occurred while invoking the agent executor: {e}")
+            return "I'm sorry, something went wrong while processing your request."
+
+        self.store_customer_interaction(
+            user_id=user_id,
+            message=query,
+            response=response["output"],
+            metadata={"type": "support_query"}
+        )
+
+        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 user_id
+    if 'user_id' not in st.session_state:
+        st.session_state.user_id = None
+
+    # Define per-user chat history key
+    user_chat_key = f"chat_history_{st.session_state.user_id}" if st.session_state.user_id else "chat_history_temp"
+
+    if user_chat_key not in st.session_state:
+        st.session_state[user_chat_key] = []
+
+    # Login form
+    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
+                user_chat_key = f"chat_history_{user_id}"
+                st.session_state[user_chat_key] = [{
+                    "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:
+        # Display chat history
+        for message in st.session_state[user_chat_key]:
+            with st.chat_message(message["role"]):
+                st.write(message["content"])
+
+        # Chat input
+        user_query = st.chat_input("Type your question here or 'exit' to end")
+        if user_query:
+            if user_query.lower() == "exit":
+                st.session_state[user_chat_key].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[user_chat_key].append({"role": "assistant", "content": goodbye_msg})
+                with st.chat_message("assistant"):
+                    st.write(goodbye_msg)
+                st.session_state.pop(user_chat_key, None)
+                st.session_state.user_id = None
+                st.rerun()
+                return
+
+            st.session_state[user_chat_key].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)
+            filtered_result = filtered_result.replace("\n", " ")
+
+            # Validate safe input
+            if filtered_result in ['safe', 'unsafe S6', 'unsafe S7']:
+                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[user_chat_key].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[user_chat_key].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[user_chat_key].append({"role": "assistant", "content": inappropriate_msg})
+
+if __name__ == "__main__":
+    nutrition_disorder_streamlit()