app.py

# 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 OpenAIEmbeddings, ChatOpenAI
from langchain_openai import ChatOpenAI
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
# Space will need your token to request hardware: set it as a Secret !

# Retrieve the OPENAI API Key
api_key = os.environ.get("API_KEY")
# Retrieve the OPENAI API Base
endpoint = os.environ.get("OPENAI_API_BASE")
# Retrieve the Mem0 API key
m0_api_key = os.environ.get("M0_API_KEY")
# Retrieve the Llama API key
llama_api_key = os.environ.get("LLAMA_API_KEY")

# Initialize the OpenAI embedding function for Chroma
embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
    api_base=endpoint, # API base endpoint
    api_key=api_key, # API key
    model_name='text-embedding-ada-002' # embedding model
)

# This initializes the OpenAI embedding function for the Chroma vectorstore, using the provided endpoint and API key.

# Initialize the OpenAI Embeddings
embedding_model = OpenAIEmbeddings(
    openai_api_base=endpoint,
    openai_api_key=api_key,
    model='text-embedding-ada-002'
)


# Initialize the Chat OpenAI model
llm = ChatOpenAI(
    openai_api_base=endpoint,
    openai_api_key=api_key,
    model="gpt-4o-mini",
    streaming=False
)
# This initializes the Chat OpenAI model with the provided endpoint, API key, deployment name, and a temperature setting of 0 (to control response variability).

# set the LLM and embedding model in the LlamaIndex settings.
Settings.llm = llm # Define the LLM model
Settings.embedding = embedding_model # 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: AgentState) -> AgentState:
    """
    Expands the user query to improve retrieval of nutrition disorder-related information using few-shot prompting.

    Args:
        state (Dict): The current state of the workflow, containing the user query.

    Returns:
        Dict: The updated state with the expanded query.
    """
    system_message = '''
    You are a domain expert assisting in answering questions related to nutrition disorders.
    Convert the user query into something that a nutritionist would understand. Use domain related words.
    Perform query expansion on the question received. If there are multiple common ways of phrasing a user question \
    or common synonyms for key words in the question, make sure to return multiple versions of the query with the different phrasings.
    In case you have a valid and not empty feedback, you should use that feedback to improve the expanded query.

    If the query has multiple parts, split them into separate simpler queries. This is the only case where you can generate more than 1 query.

    If there are acronyms or words you are not familiar with, do not try to rephrase them.

    Return only 1 version of the query that expands the original user query to improve the retrieval of nutrition disorder-related information.
    Do not mention anything before or after the query.
    '''

    expand_prompt = ChatPromptTemplate.from_messages([
        ("system", system_message),
        ("user", "Expand this query: {query} using the feedback: {query_feedback}")
    ])

    chain = expand_prompt | llm | StrOutputParser()
    state['expanded_query'] = chain.invoke({"query": state['query'], "query_feedback":state["query_feedback"]})
    return state

# Initialize the Chroma vector store for retrieving documents
vector_store = Chroma(
    collection_name="semantic_chunks",
    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': 5} 
)

def retrieve_context(state: AgentState) -> AgentState:
    """
    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:
    """
    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 domain expert assisting in answering questions related to nutrition disorders.
    Your goal is to provide accurate, helpful, and concise responses based on the provided context, which includes medical documents about nutritional disorders.
    This context will begin with the token Context: and will finish before the token feedback:.
    In case you have a valid and not empty feedback, you should use that feedback to improve your answer.
    This feedback will begin with the token feedback:.

    When crafting your response:
    1. Select only the relevant context to answer the question.
    2. User questions will begin with the token Query: and will finish before the token Context:.
    3. Use the feedback provided, if it exists, to improve the accuracy of your response.

    Please adhere to the following guidelines:
    - Your response should only be about the question asked and nothing else.
    - Answer only using the context provided.
    - Do not mention anything about the context or the feedback in your final answer.
    '''

    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 evaluator, and you are tasked with rating AI-generated responses to questions posed by users.
    You need to assess whether the provided response is grounded in the provided context.

    Please act as an impartial judge and evaluate the quality of the provided response, which attempts to answer a user question based on a provided context.
    You will be presented with a context used by the AI model to generate the response and an AI-generated response to the question.
    In the input, the context provided will begin with Context: and ends before the token Response:, while the AI-generated response will begin with Response:.

    Evaluation criteria:

    Score on a scale from 1 to 5:

    - 5 = Fully grounded: All facts are clearly and directly supported by the conext provided.
    - 4 = Mostly grounded: Minor inference but closely tied to the context provided.
    - 3 = Partially grounded: Some facts supported by the context provided, others assumed by the AI model.
    - 2 = Weakly grounded: Most facts are not clearly supported by the context provided and generated by the AI model.
    - 1 = Not grounded: Contradicts or is unrelated to the context provided.

    Respond with a single number from 1 to 5 only.
    '''

    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"] #
    }))
    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 = '''
    Given the user query and the AI-generated response, verify if the AI-generated response precisely addresses the user’s query.
    In the input, the user query will begin with Query: and ends before the token Response:, while the AI generated response will begin with Response:.

    Give verdict as 1 if the AI-generated response addresses the user’s query and 0 if not.

    DO NOT output anything else before or after the veredict integer value of 1 or 0.
    '''

    precision_prompt = ChatPromptTemplate.from_messages([
        ("system", system_message),
        ("user", "Query: {query}\nResponse: {response}\n\nPrecision score:")
    ])

    chain = precision_prompt | llm | StrOutputParser()
    precision_score = float(chain.invoke({
        "query": state['query'],
        "response": state['response']
    }))
    state['precision_score'] = precision_score
    print("precision_score:", precision_score)
    state['precision_loop_count'] += 1
    print("#########Precision Incremented###########")
    return state

def refine_response(state: Dict) -> Dict:
    """
    Suggests improvements for the generated response.

    Args:
        state (Dict): The current state of the workflow, containing the query and response.

    Returns:
        Dict: The updated state with response refinement suggestions.
    """
    print("---------refine_response---------")

    system_message = '''
    You are an expert in reviewing AI-generated responses, and your task is to provide constructive feedback on the following response to help improve its accuracy, clarity, and completeness.
    In the input, the user query will begin with Query: and ends before the token Response:, while the AI-generated response will begin with Response:.
    You need to identify potential gaps, unsupported claims, ambiguous language, or missing details in the response.

    Do not rewrite the response; only suggest improvements in a concise, bullet-point format when possible, to enhance accuracy and completeness.
    '''

    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 a query optimization expert, and your task is to provide constructive suggestions to improve the expanded query.
    Take the user original query as a reference and focus on improve the expanded query identifying missing keywords, refining the scope, resolving ambiguities, and increasing precision for better information retrieval.
    In the input, the user original query will begin with Original Query:, while the expanded query will begin with Expanded Query:.

    Do not replace the expanded query; instead, suggest improvements in a structured and actionable format such as bullet points or brief explanations.
    '''

    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'] >= 4:  # 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'] == 1:  # 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: AgentState) -> AgentState:
    """Handles the case where max iterations are 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(AgentState)

    # Add processing nodes
    workflow.add_node("expand_query", expand_query)         # Step 1: Expand 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.

    # Main flow edges
    workflow.add_edge(START, "expand_query")
    workflow.add_edge("expand_query", "retrieve_context")
    workflow.add_edge("retrieve_context", "craft_response")
    workflow.add_edge("craft_response", "score_groundedness")

    # Conditional edges based on groundedness check
    workflow.add_conditional_edges(
        "score_groundedness",
        should_continue_groundedness,  # Use the conditional function
        {
            "check_precision": "check_precision",  # If well-grounded, proceed to precision check.
            "refine_response": "refine_response",  # If not, refine the response.
            "max_iterations_reached": "max_iterations_reached"  # If max loops reached, exit.
        }
    )

    workflow.add_edge("refine_response", "craft_response")  # Refined responses are reprocessed.

    # Conditional edges based on precision check
    workflow.add_conditional_edges(
        "check_precision",
        should_continue_precision,  # Use the conditional function
        {
            "pass": END,              # If precise, complete the workflow.
            "refine_query": "refine_query",  # If imprecise, refine the query.
            "max_iterations_reached": "max_iterations_reached"  # If max loops reached, exit.
        }
    )

    workflow.add_edge("refine_query", "expand_query") # Refined queries go through expansion again.
    workflow.add_edge("max_iterations_reached", END)

    return workflow


#=========================== Defining the agentic rag tool ====================#
WORKFLOW_APP = create_workflow().compile()

@tool
def agentic_rag(query: str):
    """
    Runs the RAG-based agent with conversation history for context-aware responses.

    Args:
        query (str): The current user query.

    Returns:
        Dict[str, Any]: The updated state with the generated response and conversation history.
    """
    # Initialize state with necessary parameters
    inputs = {
        "query": query,
        "expanded_query": "",
        "context": [],
        "response": "",
        "precision_score": 0,
        "groundedness_score": 0,
        "groundedness_loop_count": 0,
        "precision_loop_count": 0,
        "feedback": "",
        "query_feedback": "",
        "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-4-12b").

    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=m0_api_key) # 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)
            openai_api_key= api_key, # API key for authentication
            openai_api_base= endpoint,
            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  # 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("You might try asking questions like:")
    st.write("- In what ways can increasing dietary fiber help alleviate symptoms of functional bowel disorders?")
    st.write("- What dietary changes can help reduce the risk of diabetes mellitus in those suffering from obesity?")
    st.write("- What are some effective dietary changes to help lower high cholesterol levels?")
    st.write("Type 'exit' to end the conversation.")

    # Initialize session state for chat history and user_id if they don't exist
    if 'chat_history' not in st.session_state:
        st.session_state.chat_history = []
    if 'user_id' not in st.session_state:
        st.session_state.user_id = None

    # Login form: Only if user is not logged in
    if st.session_state.user_id is None:
        with st.form("login_form", clear_on_submit=True):
            user_id = st.text_input("Please enter your name to begin:")
            submit_button = st.form_submit_button("Login")
            if submit_button and user_id:
                st.session_state.user_id = user_id
                st.session_state.chat_history.append({
                    "role": "assistant",
                    "content": f"Welcome, {user_id}! How can I help you with nutrition disorders today?"
                })
                st.session_state.login_submitted = True  # Set flag to trigger rerun
        if st.session_state.get("login_submitted", False):
            st.session_state.pop("login_submitted")
            st.rerun()
    else:
        # Display chat history
        for message in st.session_state.chat_history:
            with st.chat_message(message["role"]):
                st.write(message["content"])

        # Chat input with custom placeholder text
        user_query = st.chat_input("Type your question here (or 'exit' to end)...")  # 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)  # Fill in with the function name for filtering input (e.g., filter_input_with_llama_guard)
            filtered_result = filtered_result.replace("\n", " ")  # Normalize the result

            # Check if input is safe based on allowed statuses
            if filtered_result in ["safe", "unsafe S6", "unsafe S7"]:  # Blanks #3, #4, #5: Fill in with allowed safe statuses (e.g., "safe", "unsafe S7", "unsafe S6")
                try:
                    if 'chatbot' not in st.session_state:
                        st.session_state.chatbot = NutritionBot()  # Fill in with the chatbot class initialization (e.g., NutritionBot)
                    response = st.session_state.chatbot.handle_customer_query(st.session_state.user_id, user_query)
                    # 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()