# import os  
# import streamlit as st  
# import google.generativeai as genai  
# from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI  
# from langchain_community.document_loaders import Docx2txtLoader   
# from langchain.text_splitter import RecursiveCharacterTextSplitter  
# from langchain_community.vectorstores import Chroma  
# from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder  
# from langchain_core.messages import HumanMessage, SystemMessage  
# from langchain.chains import create_history_aware_retriever, create_retrieval_chain 
# from langchain.chains.combine_documents import create_stuff_documents_chain 
# from langchain.embeddings import HuggingFaceEmbeddings
# from bert_score import score
# from sklearn.metrics import f1_score
# import pysqlite3
# import sys
# sys.modules['sqlite3'] = pysqlite3

# # Retrieve Google API Key
# GOOGLE_API_KEY = "AIzaSyAytkzRS0Xp0pCyo6WqKJ4m1o330bF-gPk"
# if not GOOGLE_API_KEY:
#     raise ValueError("Gemini API key not found. Please set it in the .env file.")
# os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY

# # Streamlit configuration
# st.set_page_config(page_title="College Data Chatbot", layout="centered")
# st.title("PreCollege Chatbot GEMINI+ HuggingFace Embeddings")

# # Initialize LLM and embeddings
# llm = ChatGoogleGenerativeAI(
#     model="gemini-1.5-pro-latest",
#     temperature=0.2,
#     max_tokens=None,
#     timeout=None,
#     max_retries=2,
# )
# embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

# # Load vector store
# def load_preprocessed_vectorstore():
#     try:
#         loader = Docx2txtLoader("./Updated_structred_aman.docx")
#         documents = loader.load()

#         text_splitter = RecursiveCharacterTextSplitter(
#             separators=["\n\n", "\n", ". ", " ", ""],
#             chunk_size=3000, 
#             chunk_overlap=1000
#         )
#         document_chunks = text_splitter.split_documents(documents)

#         vector_store = Chroma.from_documents(
#             embedding=embeddings,
#             documents=document_chunks,
#             persist_directory="./data32"
#         )
#         return vector_store
#     except Exception as e:
#         st.error(f"Error creating vector store: {e}")
#         return None

# # Evaluation Metrics
# def calculate_recall_at_k(retrieved_docs, relevant_docs, k=5):
#     retrieved_top_k = retrieved_docs[:k]
#     relevant_in_top_k = len(set(retrieved_top_k).intersection(set(relevant_docs)))
#     total_relevant = len(relevant_docs)
#     return relevant_in_top_k / total_relevant if total_relevant > 0 else 0.0

# def calculate_bertscore(generated_responses, reference_responses):
#     P, R, F1 = score(generated_responses, reference_responses, lang="en", rescale_with_baseline=True)
#     return {"precision": P.mean().item(), "recall": R.mean().item(), "f1": F1.mean().item()}

# def calculate_f1_score(generated_response, relevant_text):
#     generated_tokens = set(generated_response.split())
#     relevant_tokens = set(relevant_text.split())
#     intersection = generated_tokens.intersection(relevant_tokens)
    
#     precision = len(intersection) / len(generated_tokens) if len(generated_tokens) > 0 else 0
#     recall = len(intersection) / len(relevant_tokens) if len(relevant_tokens) > 0 else 0
    
#     if precision + recall > 0:
#         f1 = 2 * (precision * recall) / (precision + recall)
#     else:
#         f1 = 0.0
#     return f1

# # Context Retriever Chain
# def get_context_retriever_chain(vector_store):
#     retriever = vector_store.as_retriever()
#     prompt = ChatPromptTemplate.from_messages([
#         MessagesPlaceholder(variable_name="chat_history"),
#         ("human", "{input}"),
#         ("system", """Given a chat history and the latest user question, 
#         reformulate it as a standalone question without using chat history. 
#         Do NOT answer it, just reformulate.""")
#     ])
#     return create_history_aware_retriever(llm, retriever, prompt)

# def get_conversational_chain(retriever_chain):
#     prompt = ChatPromptTemplate.from_messages([
#         ("system", """Hello! I'm your PreCollege AI assistant. I'll guide you through your JEE Mains journey.
#         To get started, share your JEE Mains rank and preferred engineering branches or colleges."""),
#         MessagesPlaceholder(variable_name="chat_history"),
#         ("human", "{input}")
#     ])
#     stuff_documents_chain = create_stuff_documents_chain(llm, prompt)
#     return create_retrieval_chain(retriever_chain, stuff_documents_chain)

# def get_response(user_query):
#     retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
#     conversation_rag_chain = get_conversational_chain(retriever_chain)
    
#     formatted_chat_history = []
#     for message in st.session_state.chat_history:
#         if isinstance(message, HumanMessage):
#             formatted_chat_history.append({"author": "user", "content": message.content})
#         elif isinstance(message, SystemMessage):
#             formatted_chat_history.append({"author": "assistant", "content": message.content})
    
#     response = conversation_rag_chain.invoke({
#         "chat_history": formatted_chat_history,
#         "input": user_query
#     })
    
#     return response['answer']

# # Initialize vector store and metrics
# st.session_state.vector_store = load_preprocessed_vectorstore()
# if "metrics" not in st.session_state:
#     st.session_state.metrics = {"recall_at_5": [], "bert_scores": [], "f1_scores": []}

# # Initialize chat history
# if "chat_history" not in st.session_state:
#     st.session_state.chat_history = [
#         {"author": "assistant", "content": "Hello, I am Precollege. How can I help you?"}
#     ]

# # Main app logic
# if st.session_state.get("vector_store") is None:
#     st.error("Failed to load preprocessed data. Ensure the data exists in './data' directory.")
# else:
#     with st.container():
#         for message in st.session_state.chat_history:
#             if message["author"] == "assistant":
#                 with st.chat_message("system"):
#                     st.write(message["content"])
#             elif message["author"] == "user":
#                 with st.chat_message("human"):
#                     st.write(message["content"])

#     with st.container():
#         with st.form(key="chat_form", clear_on_submit=True):
#             user_query = st.text_input("Type your message here...", key="user_input")
#             submit_button = st.form_submit_button("Send")

#         if submit_button and user_query:
#             # Get response
#             response = get_response(user_query)
#             st.session_state.chat_history.append({"author": "user", "content": user_query})
#             st.session_state.chat_history.append({"author": "assistant", "content": response})

#             # Dummy relevant docs for metrics demonstration
#             retrieved_docs = ["doc1", "doc2", "doc3"]  # Replace with actual IDs from retriever
#             relevant_docs = ["doc1", "doc4"]  # Replace with ground truth IDs
#             recall_at_5 = calculate_recall_at_k(retrieved_docs, relevant_docs)
#             st.session_state.metrics["recall_at_5"].append(recall_at_5)

#             # Dummy reference and relevant text
#             reference_response = "Gold-standard answer here."
#             bert_scores = calculate_bertscore([response], [reference_response])
#             st.session_state.metrics["bert_scores"].append(bert_scores["f1"])

#             f1_score_value = calculate_f1_score(response, "Relevant text here")
#             st.session_state.metrics["f1_scores"].append(f1_score_value)

#             # Display evaluation metrics
#             st.write("Evaluation Metrics:")
#             st.write(f"Recall@5: {recall_at_5:.2f}")
#             st.write(f"BERTScore F1: {bert_scores['f1']:.2f}")
#             st.write(f"Faithfulness F1: {f1_score_value:.2f}")

#             st.rerun()



import os  
import streamlit as st  
import google.generativeai as genai  
from langchain_google_genai import ChatGoogleGenerativeAI  
from langchain_community.document_loaders import Docx2txtLoader   
from langchain.text_splitter import RecursiveCharacterTextSplitter  
from langchain_community.vectorstores import Chroma  
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder  
from langchain_core.messages import HumanMessage, SystemMessage  
from langchain.chains import create_history_aware_retriever, create_retrieval_chain 
from langchain.chains.combine_documents import create_stuff_documents_chain 
from langchain.embeddings import HuggingFaceEmbeddings
import pysqlite3
import sys
sys.modules['sqlite3'] = pysqlite3

# Set the Google API key
GOOGLE_API_KEY = "AIzaSyCvkV4v4NPnPE2TcDGpIaJx56OIf_vUCnU"
if not GOOGLE_API_KEY:
    raise ValueError("Gemini API key not found. Please set it in the .env file.")

os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY

# Streamlit app configuration
st.set_page_config(page_title="College Data Chatbot", layout="centered")
st.title("PreCollege Chatbot GEMINI+ HuggingFace Embeddings")

# Initialize the Google Gemini LLM
llm = ChatGoogleGenerativeAI(
    model="gemini-1.5-pro-latest",
    temperature=0.2, 
    max_tokens=None,
    timeout=None,
    max_retries=2,
)

# Initialize embeddings using HuggingFace
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

def load_preprocessed_vectorstore():
    """Loads documents, splits them, and creates a Chroma vector store."""
    try:
        loader = Docx2txtLoader("./Updated_structred_aman.docx")
        documents = loader.load()

        text_splitter = RecursiveCharacterTextSplitter(
            separators=["\n\n", "\n", ". ", " ", ""],
            chunk_size=3000, 
            chunk_overlap=1000
        )
        
        document_chunks = text_splitter.split_documents(documents)

        vector_store = Chroma.from_documents(
            embedding=embeddings,
            documents=document_chunks,
            persist_directory="./data32"
        )
        return vector_store
    except Exception as e:
        st.error(f"Error creating vector store: {e}")
        return None

def get_context_retriever_chain(vector_store):
    """Creates a history-aware retriever chain."""
    retriever = vector_store.as_retriever()

    # Define the prompt for the retriever chain
    prompt = ChatPromptTemplate.from_messages([
        MessagesPlaceholder(variable_name="chat_history"),
        ("human", "{input}"),
        ("system", """Given the chat history, context, and the latest user question, formulate a standalone question 
that can be understood without the chat history. Use the context to provide a relevant answer if possible.
If the question is beyond the scope of the context, return:
'This question is beyond the scope of the available information. Please contact your mentor for further assistance.'
""")
    ])

    retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
    return retriever_chain

def get_conversational_chain(retriever_chain):
    """Creates a conversational chain using the retriever chain."""
    prompt = ChatPromptTemplate.from_messages([
        ("system", """Hello! I'm your PreCollege AI assistant, here to help you with your JEE Mains journey. 
Please provide your JEE Mains rank and preferred engineering branches or colleges, 
and I'll give you tailored advice based on our verified database. 
Note: I will only provide information that is available within our database to ensure accuracy. Let's get started!
\n\n{context}"""),
        MessagesPlaceholder(variable_name="chat_history"),
        ("human", "{input}")
    ])

    stuff_documents_chain = create_stuff_documents_chain(llm, prompt)
    return create_retrieval_chain(retriever_chain, stuff_documents_chain)

def get_response(user_query):
    """Gets a response from the conversational RAG chain."""
    retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
    conversation_rag_chain = get_conversational_chain(retriever_chain)
    
    formatted_chat_history = []
    for message in st.session_state.chat_history:
        if isinstance(message, HumanMessage):
            formatted_chat_history.append({"author": "user", "content": message.content})
        elif isinstance(message, SystemMessage):
            formatted_chat_history.append({"author": "assistant", "content": message.content})
    
    response = conversation_rag_chain.invoke({
        "chat_history": formatted_chat_history,
        "input": user_query
    })
    
    return response['answer']

# Load the preprocessed vector store from the local directory
if "vector_store" not in st.session_state:
    st.session_state.vector_store = load_preprocessed_vectorstore()

# Initialize chat history if not present
if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

# Main app logic
if st.session_state.vector_store is None:
    st.error("Failed to load preprocessed data. Please ensure the data exists in './data32' directory.")
else:
    # Display chat history
    with st.container():
        for message in st.session_state.chat_history:
            if message.get("author") == "assistant":
                with st.chat_message("assistant"):
                    st.write(message.get("content"))
            elif message.get("author") == "user":
                with st.chat_message("user"):
                    st.write(message.get("content"))

    # Add user input box below the chat
    if user_query := st.chat_input("Type your message here..."):
        # Append user query to chat history
        st.session_state.chat_history.append({"author": "user", "content": user_query})

        # Get bot response
        response = get_response(user_query)

        # Append response to chat history
        st.session_state.chat_history.append({"author": "assistant", "content": response})

        # Display response
        with st.chat_message("assistant"):
            st.write(response)