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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 = os.environ['api_key']
+endpoint = os.environ['OPENAI_API_BASE']
+# api_version = os.environ['AZURE_OPENAI_APIVERSION']
+model_name = os.environ['CHATGPT_MODEL']
+emb_key = os.environ['EMB_MODEL_KEY']
+emb_endpoint = os.environ['EMB_DEPLOYMENT']
+llama_api_key = os.environ['LLAMA_GUARD_API_KEY']
+mem0_api_key = os.environ['mem0_api_key']
+
+# Initialize the OpenAI embedding function for Chroma
+embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
+    api_base=endpoint, 
+    api_key=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 Azure endpoint and API key.
+
+# Initialize the Azure OpenAI Embeddings
+embedding_model = OpenAIEmbeddings(
+    openai_api_base=endpoint,
+    openai_api_key=api_key, 
+    model='text-embedding-ada-002'
+)
+# This initializes the Azure OpenAI embeddings model using the specified endpoint, API key, and model name.
+
+
+# Initialize the Azure 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):
+    print("State at the start of 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 helpful research assistant that is well versed in Nutritional Disorders.
+        Return an expanded user query based on the user's input query. The expanded query should be designed to improve retrieval of the most relevant information. 
+        Use the feedback if provided to craft the expanded query.
+    '''
+
+    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):
+    print("State at the start of 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.
+    """
+    query = state['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
+    state['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
+    ]
+
+    print("Extracted context with metadata:", state['context'])  # Debugging: Print the extracted context
+    return state
+
+
+
+def craft_response(state: Dict) -> Dict:
+    print("State at the start of 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 an expert at condensing information. Your task is to extract relevant information for a given query and provide a grounded and highly precise 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": state["feedback"] if state["feedback"] else "No feedback provided."  # Add feedback to the prompt # add feedback to the prompt
+    })
+    state['response'] = response
+    print("intermediate response: ", response)
+
+    return state
+
+
+def score_groundedness(state: Dict) -> Dict:
+    print("State at the start of 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 to guide the evaluation
+    system_message = '''You are a groundedness evaluator. Your task is to assess how well the given response aligns with the provided context. 
+    - A grounded response is one that is accurate, directly supported by the context, and avoids speculation.
+    - A response should not include information that cannot be verified or inferred from the context.
+
+    Instructions:
+    - Assign a score between 0.0 and 1.0, where:
+        - 1.0: Fully grounded (entirely supported by the context).
+        - 0.5: Partially grounded (some elements are supported, but others are speculative).
+        - 0.0: Not grounded (contains speculative or unsupported information).
+    - Provide only the numerical groundedness score as the output.'''
+
+    # Define the prompt template for evaluating groundedness
+    groundedness_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Context: {context}\nResponse: {response}\n\nGroundedness score:")
+    ])
+
+    # Chain to compute groundedness score
+    chain = groundedness_prompt | llm | StrOutputParser()
+    groundedness_score = float(chain.invoke({
+        "context": "\n".join([doc["content"] for doc in state['context']]),  # Combine document content
+        "response": state['response']  # Use the response from the state
+    }))
+
+    print("groundedness_score: ", groundedness_score)
+    state['groundedness_loop_count'] += 1
+    print("######### Groundedness Loop Count Incremented ###########")
+    state['groundedness_score'] = groundedness_score
+    print("groundedness_score: ", state['groundedness_score'])
+
+    return state
+
+
+def check_precision(state: Dict) -> Dict:
+
+    print("State at the start of check_precision:", state)
+    """
+    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 for evaluating precision
+    system_message = '''You are a precision evaluator. Your task is to assess how well the given response directly and fully addresses the user's query.
+    
+    Instructions:
+    - A precise response is one that:
+        - Directly answers the user’s query without unnecessary or unrelated information.
+        - Fully addresses all aspects of the query.
+        - Avoids vague or overly general statements.
+    - Assign a precision score between 0.0 and 1.0:
+        - 1.0: Fully precise (direct, complete, and relevant to the query).
+        - 0.5: Partially precise (addresses the query but is incomplete or includes some irrelevant information).
+        - 0.0: Not precise (fails to address the query or contains mostly irrelevant information).
+    - Provide only the numerical precision score as the output.'''
+
+    # Define the prompt template for evaluating precision
+    precision_prompt = ChatPromptTemplate.from_messages([
+        ("system", system_message),
+        ("user", "Query: {query}\nResponse: {response}\n\nPrecision score:")
+    ])
+
+    # Chain to compute precision score
+    chain = precision_prompt | llm | StrOutputParser()
+    precision_score = float(chain.invoke({
+        "query": state['query'],
+        "response": state['response']
+    }))
+    
+    # Update the state with precision score
+    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:
+    print("State at the start of refine_response:", state)
+
+    """
+    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 a constructive feedback evaluator. Your task is to analyze the provided response and identify potential gaps, ambiguities, or missing details. Your feedback should help improve the response for accuracy, clarity, and completeness.
+
+Instructions:
+- Do not rewrite the response.
+- Focus on identifying the following:
+    - Are there any gaps in the information provided?
+    - Is the response ambiguous or unclear in any part?
+    - Are there any details missing that are relevant to fully addressing the context or query?
+- Provide actionable and constructive suggestions for improvement.
+- Avoid criticism without offering specific recommendations.
+
+Your output should be written as a list of feedback points, with each suggestion clearly and concisely stated.'''
+
+    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:
+    print("State at the start of refine_query:", state)
+    """
+    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 a query refinement assistant. Your task is to analyze the original query and its expanded version to suggest specific improvements that can enhance search precision and relevance.
+
+Instructions:
+- Do not replace or rewrite the expanded query. Instead, provide structured suggestions for improvement.
+- Focus on identifying:
+    - Missing details or specific keywords that could make the query more precise.
+    - Scope refinements to narrow or broaden the query if needed.
+    - Ambiguities or redundancies that can be clarified or removed.
+- Ensure your suggestions are actionable and presented in a clear, concise, and structured format.
+- Avoid general or vague feedback; provide specific recommendations.
+
+Your output should be a list of suggestions that can improve the expanded query without modifying it directly.'''
+
+    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):
+    print("State at the start of should_continue_groundedness:", state)
+    """Decides if groundedness is sufficient or needs improvement."""
+    print("---------should_continue_groundedness---------")
+    print("groundedness loop count: ", state['groundedness_loop_count'])
+
+    # Check if groundedness score meets the required threshold
+    if state['groundedness_score'] >= 0.8:  # Threshold for groundedness
+        print("Moving to precision")
+        return "check_precision"  # Proceed to precision checking
+    else:
+        # Check if the maximum number of iterations has been reached
+        if state['groundedness_loop_count'] >= state['loop_max_iter']:
+            return "max_iterations_reached"  # Stop refinement if max iterations reached
+        else:
+            print(f"---------Groundedness Score Threshold Not Met. Refining Response-----------")
+            return "refine_response"  # Continue refining the response
+
+def should_continue_precision(state: Dict) -> str:
+    print("State at the start of should_continue_precision:", state)
+    
+    """Decides if precision is sufficient or needs improvement."""
+    print("---------should_continue_precision---------")
+    print("precision loop count: ", state['precision_loop_count'])
+
+    # Check if the precision score meets the required threshold
+    if state['precision_score'] >= 0.8:  # Threshold for precision
+        return "pass"  # Complete the workflow
+    else:
+        # Check if the maximum number of iterations has been reached
+        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
+
+from langgraph.graph import StateGraph, START, END
+from typing import Callable
+
+
+def create_workflow() -> StateGraph:
+    """Creates the updated workflow for the AI nutrition agent."""
+    # Initialize workflow with the `AgentState` schema
+    workflow = StateGraph(state_schema=AgentState)
+
+    # Add processing nodes
+    workflow.add_node("expand_query", expand_query)             # Step 1: Expand the user query
+    workflow.add_node("retrieve_context", retrieve_context)     # Step 2: Retrieve relevant documents
+    workflow.add_node("craft_response", craft_response)         # Step 3: Generate a response based on retrieved data
+    workflow.add_node("score_groundedness", score_groundedness) # Step 4: Evaluate response grounding
+    workflow.add_node("refine_response", refine_response)       # Step 5: Improve response if it's weakly grounded
+    workflow.add_node("check_precision", check_precision)       # Step 6: Evaluate response precision
+    workflow.add_node("refine_query", refine_query)             # Step 7: Improve query if response lacks precision
+    workflow.add_node("max_iterations_reached", max_iterations_reached)  # Step 8: Handle max iterations gracefully
+
+    # Main flow edges
+    workflow.add_edge(START, "expand_query")                    # Start with expanding the query
+    workflow.add_edge("expand_query", "retrieve_context")       # After expansion, retrieve context/documents
+    workflow.add_edge("retrieve_context", "craft_response")     # Generate a response based on retrieved context
+    workflow.add_edge("craft_response", "score_groundedness")   # Evaluate the response for 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 by crafting a new response
+
+    # 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)           # Max iterations lead to an exit point
+
+    return workflow
+
+
+
+#=========================== Defining the agentic rag tool ====================#
+WORKFLOW_APP = create_workflow().compile()
+
+# Define the tool
+@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="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
+        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 ===============================#
+
+# NutritionBot class
+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="mock_memory_api_key")  # Replace with actual API key
+
+        # Initialize the OpenAI client using the provided credentials
+        self.client = ChatOpenAI(
+            model_name="gpt-4",  # Specify the model to use (e.g., GPT-4)
+            api_key="mock_openai_api_key"  # Replace with actual API key
+        )
+
+        # Define tools available to the chatbot, including agentic_rag
+        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."""
+
+        # 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 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.
+        """
+        # Use the agentic_rag tool to process the query
+        try:
+            # Call the agentic_rag tool directly
+            result = agentic_rag(query)
+            response = result.get("response", "I'm sorry, I couldn't generate a response.")
+            return response
+        except Exception as e:
+            return f"An error occurred while processing your query: {str(e)}"
+
+
+#=====================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("You: ").strip()  # 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 S7", "unsafe S6"]:  # 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.andle_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()