# 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_community.embeddings import OpenAIEmbeddings # 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['AZURE_OPENAI_KEY'] endpoint = os.environ['AZURE_OPENAI_ENDPOINT'] model_name = os.environ['CHATGPT_MODEL'] api_version = os.environ['AZURE_OPENAI_APIVERSION'] emb_key = os.environ['EMB_MODEL_KEY'] emb_endpoint = os.environ['EMB_DEPLOYMENT'] llamaparse_api_key = os.environ['LLAMA_KEY'] groq_api_key = os.environ['GROQ_API_KEY'] MEM0_api_key = os.environ['MEM0_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 # Initialize the OpenAI embedding function for Chroma embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction( api_base=endpoint, # Complete the code to define the API base endpoint api_key=api_key, # Complete the code to define the API key api_type='azure', # This is a fixed value and does not need modification api_version=api_version, # This is a fixed value and does not need modification 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 = AzureOpenAIEmbeddings( azure_endpoint=emb_endpoint, # Complete the code to define the Azure endpoint api_key=emb_key, # Complete the code to define the API key api_version='2023-05-15', # This is a fixed value and does not need modification model='text-embedding-ada-002' ) # Complete the code to define the model name # This initializes the Azure OpenAI embeddings model using the specified endpoint, API key, and model name. # Initialize the Azure Chat OpenAI model llm = AzureChatOpenAI( azure_endpoint=endpoint, # Complete the code to define the Azure endpoint api_key=api_key, # Complete the code to provide the API key api_version=api_version, # This is a fixed value and does not need modification azure_deployment=model_name, # Complete the code to define the Azure deployment name temperature=0 # Complete the code to set the temperature for response variability ) # This initializes the Azure Chat OpenAI model with the provided endpoint, API key, deployment name, and a temperature setting of 0 (to control response variability). # set the LLM and embedding model in the LlamaIndex settings. Settings.llm = llm # Complete the code to define the LLM model Settings.embedding = embedding_model # Complete the code to define the embedding model #================================Creating Langgraph agent======================# class AgentState(TypedDict): query: str # The current user query expanded_query: str # The expanded version of the user query context: List[Dict[str, Any]] # Retrieved documents (content and metadata) response: str # The generated response to the user query precision_score: float # The precision score of the response groundedness_score: float # The groundedness score of the response groundedness_loop_count: int # Counter for groundedness refinement loops precision_loop_count: int # Counter for precision refinement loops feedback: str query_feedback: str groundedness_check: bool loop_max_iter: int def expand_query(state): """ Expands the user query to improve retrieval of nutrition disorder-related information. Args: state (Dict): The current state of the workflow, containing the user query. Returns: Dict: The updated state with the expanded query. """ system_message = """ You are a helpful and harmless AI assistant. Your task is to expand the user's query related to nutrition disorders to improve information retrieval. If there are multiple common ways of phrasing a user's query or common synonyms for key words in the question, make sure to return multiple versions of the query with the different phrasings. If the query has multiple parts, split them into separate simpler queries. This is the only case where you can generate more than 3 queries. If there are acronyms or words you are not familiar with, do not try to rephrase them. Return only 3 versions of the question as a list. Generate only a list of questions. Do not mention anything before or after the list. **Guidelines for Query Expansion:** 1. **Retain Original Intent:** Ensure the expanded query accurately reflects the user's original information need. Avoid introducing new topics or shifting the focus. 2. **Add Relevant Keywords:** Introduce keywords and phrases commonly associated with nutritional disorders that are relevant to the user's query. This might include symptoms, causes, risk factors, diagnostic terms, treatment approaches, or related conditions. 3. **Consider Query Feedback:** If query feedback is available, incorporate it to refine the query further. Address any ambiguities or missing information highlighted in the feedback. 4. **Conciseness and Clarity:** Keep the expanded query concise and easy to understand. Avoid overly complex or lengthy phrases. 5. **Focus on Nutritional Aspects:** The expanded query should prioritize aspects related to nutrition, diet, and dietary habits. 6. **Medical Accuracy:** While not a medical professional, strive for medical accuracy by using terminology and concepts consistent with established nutritional science. 7. **Output format:** Return only 3 versions of the expanded queries as a list and do not mention anything before or after the list. **Example:** **User Query:** "What are the effects of vitamin D deficiency?" **Query Feedback:** "Focus on bone health." **Expanded Query:** "[ 'How does vitamin D deficiency affect bone health in children, adults, and the elderly?', 'What are the long-term effects of chronic vitamin D deficiency on bone density and fracture risk?', 'How does vitamin D deficiency contribute to osteoporosis, rickets, and other bone-related disorders?' ]" **Remember:** Your goal is to create an expanded query that helps retrieve the most relevant and accurate information about nutrition disorders from a knowledge base. """ expand_prompt = ChatPromptTemplate.from_messages([ ("system", system_message), ("user", "Expand this query: {query} using the feedback: {query_feedback}") ]) chain = expand_prompt | llm | StrOutputParser() expanded_query = chain.invoke({"query": state['query'], "query_feedback":state["query_feedback"]}) print("expanded_query", expanded_query) state["expanded_query"] = expanded_query return state # Initialize the Chroma vector store for retrieving documents vector_store = Chroma( collection_name="nutritional_hypotheticals", persist_directory="./nutritional_db", embedding_function=embedding_model ) # Create a retriever from the vector store retriever = vector_store.as_retriever( search_type='similarity', search_kwargs={'k': 3} ) def retrieve_context(state): """ Retrieves context from the vector store using the expanded or original query. Args: state (Dict): The current state of the workflow, containing the query and expanded query. Returns: Dict: The updated state with the retrieved context. """ print("---------retrieve_context---------") query = state['expanded_query'] # Complete the code to define the key for the expanded query #print("Query used for retrieval:", query) # Debugging: Print the query # Retrieve documents from the vector store docs = retriever.invoke(query) print("Retrieved documents:", docs) # Debugging: Print the raw docs object # Extract both page_content and metadata from each document context= [ { "content": doc.page_content, # The actual content of the document "metadata": doc.metadata # The metadata (e.g., source, page number, etc.) } for doc in docs ] state['context'] = context # Complete the code to define the key for storing the context print("Extracted context with metadata:", context) # Debugging: Print the extracted context #print(f"Groundedness loop count: {state['groundedness_loop_count']}") return state def craft_response(state: Dict) -> Dict: """ Generates a response using the retrieved context, focusing on nutrition disorders. Args: state (Dict): The current state of the workflow, containing the query and retrieved context. Returns: Dict: The updated state with the generated response. """ print("---------craft_response---------") system_message = """ You are an helpful AI assisstant specializing in nutrition disorders,vitamin and mineral deficiency. Your job is to answer user query based on the provided Context Use the following guidelines: - If you're unsure about something, ask for clarification - Only answer the question based on the provided Context - Use the feedback if provided to refine your answer """ #- If you do not know the answer based on the provided Context you must respond with "I do not have the answer based on my context." 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 AI assistant tasked with evaluating the groundedness of responses based on the provided context. Your goal is to determine if the response aligns with and is supported by the given context. Guidelines for scoring: Give the response a score of one decimal point between 0.0 and 1.0 based on the following criteria: - 1.0 **: The response is entirely supported by the context. - 0.0 **: The response is entirely unsupported by the context, or response no support from the context. Evaluate the given response against the provided context and return a **groundedness_score** based on the above criteria. Stricly just return the groundedness_score and do not explain your response. ''' 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 AI assistant tasked with evaluating the precision of response based on the user’s query. Your goal is to determine if the precisely addresses the user’s query. Given user's query and response, verify if the response precisely addresses the user query. Guidelines for scoring: Give the response a score of one decimal point between 0.0 and 1.0 based on the following criteria: - 1.0 **: The response is precisely addressing the user's query. - 0.0 **: The response, by no means, address the user's query. Evaluate the given response against the user's query and return a **precision_score** based on the above criteria. Stricly just return the precision_score and do not explain your response. """ 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 = ''' Given following query and response what improvements can be made to enhance accuracy and completeness of the generated 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 = ''' Given following query and expanded_query that is generated for improve search result, your task is to provide improvements that can be made to expanded_query and enhance search precision. ''' 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'] >= 0.9: # 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'] >= 0.9: # Threshold for precision return "pass" # Complete the workflow else: if state['precision_loop_count'] > state['loop_max_iter']: # Maximum allowed loops return "max_iterations_reached" else: print(f"---------Precision Score Threshold Not met. Refining Query-----------") # Debugging return "refine_query" # Refine the query def max_iterations_reached(state: Dict) -> Dict: """Handles the case when the maximum number of iterations is reached.""" print("---------max_iterations_reached---------") """Handles the case when the maximum number of iterations is reached.""" response = "I'm unable to refine the response further. Please provide more context or clarify your question." state['response'] = response return state from langgraph.graph import END, StateGraph, START def create_workflow() -> StateGraph: """Creates the updated workflow for the AI nutrition agent.""" workflow = StateGraph(AgentState) # Complete the code to define the initial state of the agent # Add processing nodes workflow.add_node("expand_query", expand_query) # Step 1: Expand user query. Complete with the function to expand the query workflow.add_node("retrieve_context", retrieve_context) # Step 2: Retrieve relevant documents. Complete with the function to retrieve context workflow.add_node("craft_response", craft_response) # Step 3: Generate a response based on retrieved data. Complete with the function to craft a response workflow.add_node("score_groundedness", score_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. Use the node name "check_precision" "refine_response": "refine_response", # If not, refine the response. "max_iterations_reached": "max_iterations_reached" # If max loops reached, exit. } ) workflow.add_edge("refine_response", "craft_response") # Refined responses are reprocessed. # Conditional edges based on precision check workflow.add_conditional_edges( "check_precision", should_continue_precision, # Use the conditional function { "pass": END, # If precise, complete the workflow. "refine_query": "refine_query", # If imprecise, refine the query. "max_iterations_reached": "max_iterations_reached" # If max loops reached, exit. } ) workflow.add_edge("refine_query", "expand_query") # Refined queries go through expansion again. workflow.add_edge("max_iterations_reached", END) return workflow #=========================== Defining the agentic rag tool ====================# WORKFLOW_APP = create_workflow().compile() @tool def agentic_rag(query: str): """ Runs the RAG-based agent with conversation history for context-aware responses. Args: query (str): The current user query. Returns: Dict[str, Any]: The updated state with the generated response and conversation history. """ # Initialize state with necessary parameters inputs = { "query": query, # Current user query "expanded_query": "", # Complete the code to define the expanded version of the query "context": [], # Retrieved documents (initially empty) "response": "", # Complete the code to define the AI-generated response "precision_score": 0.0, # Complete the code to define the precision score of the response "groundedness_score": 0.0, # Complete the code to define the groundedness score of the response "groundedness_loop_count": 3, # Complete the code to define the counter for groundedness loops "precision_loop_count": 3, # 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 # 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=MEM0_api_key) # Complete the code to define the memory client API key # Initialize the Azure OpenAI client using the provided credentials self.client = AzureChatOpenAI( model_name= model_name, # Specify the model to use (e.g., GPT-4 optimized version) api_key= api_key, # API key for authentication azure_endpoint= endpoint, # Endpoint URL for Azure OpenAI api_version= api_version, # API version being used temperature= 0 # Controls randomness in responses; 0 ensures deterministic results ) # Define tools available to the chatbot, such as web search tools = [agentic_rag] # Define the system prompt to set the behavior of the chatbot system_prompt = """You are a caring and knowledgeable Medical Support Agent, specializing in nutrition disorder-related guidance. Your goal is to provide accurate, empathetic, and tailored nutritional recommendations while ensuring a seamless customer experience. Guidelines for Interaction: Maintain a polite, professional, and reassuring tone. Show genuine empathy for customer concerns and health challenges. Reference past interactions to provide personalized and consistent advice. Engage with the customer by asking about their food preferences, dietary restrictions, and lifestyle before offering recommendations. Ensure consistent and accurate information across conversations. If any detail is unclear or missing, proactively ask for clarification. Always use the agentic_rag tool to retrieve up-to-date and evidence-based nutrition insights. Keep track of ongoing issues and follow-ups to ensure continuity in support. Your primary goal is to help customers make informed nutrition decisions that align with their health conditions and personal preferences. """ # Build the prompt template for the agent prompt = ChatPromptTemplate.from_messages([ ("system", system_prompt), # System instructions ("human", "{input}"), # Placeholder for human input ("placeholder", "{agent_scratchpad}") # Placeholder for intermediate reasoning steps ]) # Create an agent capable of interacting with tools and executing tasks agent = create_tool_calling_agent(self.client, tools, prompt) # Wrap the agent in an executor to manage tool interactions and execution flow self.agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) def store_customer_interaction(self, user_id: str, message: str, response: str, metadata: Dict = None): """ Store customer interaction in memory for future reference. Args: user_id (str): Unique identifier for the customer. message (str): Customer's query or message. response (str): Chatbot's response. metadata (Dict, optional): Additional metadata for the interaction. """ if metadata is None: metadata = {} # Add a timestamp to the metadata for tracking purposes metadata["timestamp"] = datetime.now().isoformat() # Format the conversation for storage conversation = [ {"role": "user", "content": message}, {"role": "assistant", "content": response} ] # Store the interaction in the memory client self.memory.add( conversation, user_id=user_id, output_format="v1.1", metadata=metadata ) def get_relevant_history(self, user_id: str, query: str) -> List[Dict]: """ Retrieve past interactions relevant to the current query. Args: user_id (str): Unique identifier for the customer. query (str): The customer's current query. Returns: List[Dict]: A list of relevant past interactions. """ return self.memory.search( query=query, # Search for interactions related to the query user_id=user_id, # Restrict search to the specific user limit=5 # Complete the code to define the limit for retrieved interactions ) def handle_customer_query(self, user_id: str, query: str) -> str: """ Process a customer's query and provide a response, taking into account past interactions. Args: user_id (str): Unique identifier for the customer. query (str): Customer's query. Returns: str: Chatbot's response. """ # Retrieve relevant past interactions for context relevant_history = self.get_relevant_history(user_id, query) # Build a context string from the relevant history context = "Previous relevant interactions:\n" for memory in relevant_history: context += f"Customer: {memory['memory']}\n" # Customer's past messages context += f"Support: {memory['memory']}\n" # Chatbot's past responses context += "---\n" # Print context for debugging purposes print("Context: ", context) # Prepare a prompt combining past context and the current query prompt = f""" Context: {context} Current customer query: {query} Provide a helpful response that takes into account any relevant past interactions. """ # Generate a response using the agent response = self.agent_executor.invoke({"input": prompt}) # Store the current interaction for future reference self.store_customer_interaction( user_id=user_id, message=query, response=response["output"], metadata={"type": "support_query"} ) # Return the chatbot's response return response['output'] #=====================User Interface using streamlit ===========================# def nutrition_disorder_streamlit(): """ A Streamlit-based UI for the Nutrition Disorder Specialist Agent. """ st.title("Nutrition Disorder Specialist") st.write("Ask me anything about nutrition disorders, symptoms, causes, treatments, and more.") st.write("Type 'exit' to end the conversation.") # Initialize session state for chat history and user_id if they don't exist if 'chat_history' not in st.session_state: st.session_state.chat_history = [] if 'user_id' not in st.session_state: st.session_state.user_id = None # Login form: Only if user is not logged in if st.session_state.user_id is None: with st.form("login_form", clear_on_submit=True): user_id = st.text_input("Please enter your name to begin:") submit_button = st.form_submit_button("Login") if submit_button and user_id: st.session_state.user_id = user_id st.session_state.chat_history.append({ "role": "assistant", "content": f"Welcome, {user_id}! How can I help you with nutrition disorders today?" }) st.session_state.login_submitted = True # Set flag to trigger rerun if st.session_state.get("login_submitted", False): st.session_state.pop("login_submitted") st.rerun() else: # Display chat history for message in st.session_state.chat_history: with st.chat_message(message["role"]): st.write(message["content"]) # Chat input with custom placeholder text user_query = st.chat_input("Type your question here (or 'exit' to end)...") # Blank #1: Fill in the chat input prompt (e.g., "Type your question here (or 'exit' to end)...") if user_query: if user_query.lower() == "exit": st.session_state.chat_history.append({"role": "user", "content": "exit"}) with st.chat_message("user"): st.write("exit") goodbye_msg = "Goodbye! Feel free to return if you have more questions about nutrition disorders." st.session_state.chat_history.append({"role": "assistant", "content": goodbye_msg}) with st.chat_message("assistant"): st.write(goodbye_msg) st.session_state.user_id = None st.rerun() return st.session_state.chat_history.append({"role": "user", "content": user_query}) with st.chat_message("user"): st.write(user_query) # Filter input through Llama Guard - returns "SAFE" or "UNSAFE" filtered_result = filter_input_with_llama_guard(user_query) # Call function to filter input filtered_result = filtered_result.replace("\n", " ") # Normalize the result st.write(filtered_result) # Check if input is safe based on allowed statuses # We are by passing some cases like "S6" and "S7" so that it can work effectively. if filtered_result in ["safe", "unsafe S7", "unsafe S6"]: try: if 'chatbot' not in st.session_state: st.session_state.chatbot = NutritionBot() # Blank #6: Fill in with the chatbot class initialization (e.g., NutritionBot) response = st.session_state.chatbot.handle_customer_query(st.session_state.user_id, user_query) # Blank #7: Fill in with the method to handle queries (e.g., handle_customer_query) st.write(response) st.session_state.chat_history.append({"role": "assistant", "content": response}) except Exception as e: error_msg = f"Sorry, I encountered an error while processing your query. Please try again. Error: {str(e)}" st.write(error_msg) st.session_state.chat_history.append({"role": "assistant", "content": error_msg}) else: inappropriate_msg = "I apologize, but I cannot process that input as it may be inappropriate. Please try again." st.write(inappropriate_msg) st.session_state.chat_history.append({"role": "assistant", "content": inappropriate_msg}) if __name__ == "__main__": nutrition_disorder_streamlit()