app.py

######################## WRITE YOUR CODE HERE  #########################
# 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
from langchain.memory import ConversationSummaryBufferMemory

from langchain_openai import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma


from langchain_community.utilities.sql_database import SQLDatabase
from langchain_community.agent_toolkits import create_sql_agent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.tools import tool
from langchain.agents import create_tool_calling_agent, AgentExecutor

# 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"]
llama_api_key = os.environ['LLAMA_API_KEY']
mem0_api_key = os.environ['mem0']

# 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
    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 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 # 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 climate-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 domain expert assisting in answering questions related to climate-related information.
Convert the user query into something that a climate professional 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.

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.

Question:
{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="climateBot",
    persist_directory="./climateBot_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)

    return state



def craft_response(state: Dict) -> Dict:
    """
    Generates a response using the retrieved context, focusing on climate solutions.

    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 smart climate specialist. Use the context and feedback to respond to the query.
    The answer you provide must come from the context and feedback provided.
    If information provided is not enough to answer the query response with 'I don't know the answer. Not in my records'''

    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 = '''Your task is to judge the groundedness of the response based on the context.
    For each statement you must return verdict as 1 if the response is completely grounded in the context and 0 if the response is completely hallucinated.'''

    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 = '''Given the query, response and context verify if the context was useful in arriving at the given answer.
    Give precision score of "1" if useful and "0" if it was not useful.'''

    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 = '''Your task is to refine the AI generated response by improving the accuracy and completeness of the response based on the contexxt.'''

    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 = '''Your task is to refine the expanded query to improve the precision of the AI generated response.'''

    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'] >= 1:  # 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']>=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: 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 climate 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. Complete with the function to refine the query
    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": END  # 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": END  # 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": 5,
        "precision_loop_count": 5,
        "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 ClimateBot:
    def __init__(self):
        """
        Initialize the NutritionBot class, setting up memory, the LLM client, tools, and the agent executor.
        """


#====================================SETUP=====================================#
# Fetch secrets from Hugging Face Spaces
api_key = os.environ["API_KEY"]
endpoint = os.environ["OPENAI_API_BASE"]
llama_api_key = os.environ['GROQ_API_KEY']
mem0_api_key = os.environ['mem0']

# 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
    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 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 # 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 climate-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 domain expert assisting in answering questions related to climate-related information.
Convert the user query into something that a climate professional 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.

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.

Question:
{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="climateBot",
    persist_directory="./climateBot_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


    # 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

    return state



def craft_response(state: Dict) -> Dict:
    """
    Generates a response using the retrieved context, focusing on climate solutions.

    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 smart climate specialist. Use the context and feedback to respond to the query.
    The answer you provide must come from the context and feedback provided.
    If information provided is not enough to answer the query response with 'I don't know the answer. Not in my records'''

    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 = '''Your task is to judge the groundedness of the response based on the context.
    For each statement you must return verdict as 1 if the response is completely grounded in the context and 0 if the response is completely hallucinated.'''

    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 = '''Given the query, response and context verify if the context was useful in arriving at the given answer.
    Give precision score of "1" if useful and "0" if it was not useful.'''

    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 = '''Your task is to refine the AI generated response by improving the accuracy and completeness of the response based on the contexxt.'''

    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 = '''Your task is to refine the expanded query to improve the precision of the response.'''

    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'] >= 1:  # 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']>=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: 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)

    # 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. Complete with the function to refine the query
    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": END  # 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": END  # 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": 5,
        "precision_loop_count": 5,
        "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 ClimateBot:
    def __init__(self):
        """
        Initialize the ClimateBot 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=userdata.get("mem0_api_key"))  # Complete the code to define the memory client API key
        self.memory = MemoryClient(api_key=mem0_api_key)
        # Initialize the OpenAI client using the provided credentials
        self.client = ChatOpenAI(
            model_name="gpt-4o-mini",  # Specify the model to use (e.g., GPT-4 optimized version)
            api_key=os.environ["API_KEY"],  # API key for authentication
            openai_api_base = os.environ["OPENAI_API_BASE"],
            temperature=0  # Controls randomness in responses; 0 ensures deterministic results
        )

        # Define tools available to the chatbot, such as web search
        tools = [agentic_rag]

        # Define the system prompt to set the behavior of the chatbot
        system_prompt = """You are a caring and knowledgeable Climate Agent, specializing in climate-related guidance. Your goal is to provide accurate, empathetic, and tailored climate related solutions while ensuring a seamless customer experience.
                          Guidelines for Interaction:
                          Maintain a polite, professional, and reassuring tone.
                          Show genuine empathy for customer concerns and climate challenges.
                          Reference past interactions to provide personalized and consistent advice.
                          Engage with the customer by asking about their company size, industry, and location 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 climate insights.
                          Keep track of ongoing issues and follow-ups to ensure continuity in support.
                          Your primary goal is to help customers make informed climate impact decisions that align with their company size, industry and location.

        """

        # 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 climate_streamlit():
    """
    A Streamlit-based UI for the Climate Specialist Agent.
    """
    st.title("Climate Specialist")
    st.write("Ask me anything about climate change, causes, impact, solutions 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 climate solutions 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): ", key="chat_input")  # 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 climate change."
                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 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 = ClimateBot()  # Blank #6: Fill in with the chatbot class initialization (e.g., ClimateBot)
                    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__":
    climate_streamlit()