import eventlet
eventlet.monkey_patch()


from dotenv import load_dotenv
from flask import Flask, request, render_template
from flask_cors import CORS
from flask_socketio import SocketIO, emit

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain.retrievers.document_compressors import EmbeddingsFilter
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers import EnsembleRetriever
from langchain_community.vectorstores import FAISS
from langchain_groq import ChatGroq
from langchain import hub
import pickle
import os



# Load environment variables
load_dotenv(".env")
USER_AGENT = os.getenv("USER_AGENT")
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
SECRET_KEY = os.getenv("SECRET_KEY")
SESSION_ID_DEFAULT = "abc123"


# Set environment variables
os.environ['USER_AGENT'] = USER_AGENT
os.environ["GROQ_API_KEY"] = GROQ_API_KEY
os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
os.environ["TOKENIZERS_PARALLELISM"] = 'true'

# Initialize Flask app and SocketIO with CORS
app = Flask(__name__)
CORS(app)
socketio = SocketIO(app, cors_allowed_origins="*")
app.config['SESSION_COOKIE_SECURE'] = True  # Use HTTPS
app.config['SESSION_COOKIE_HTTPONLY'] = True
app.config['SECRET_KEY'] = SECRET_KEY

# Initialize Pinecone index and BM25 encoder
pinecone_index = initialize_pinecone("uae-national-library-and-archives-vectorstore")
bm25 = BM25Encoder().load("./UAE-NLA.json")


old_embed_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

# Initialize models and retriever
embed_model = HuggingFaceEmbeddings(model_name="Alibaba-NLP/gte-multilingual-base", model_kwargs={"trust_remote_code":True})
retriever = PineconeHybridSearchRetriever(
    embeddings=embed_model, 
    sparse_encoder=bm25, 
    index=pinecone_index, 
    top_k=50, 
    alpha=0.5
)

# Initialize LLM
llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0, max_tokens=1024, max_retries=2)

# Contextualization prompt and retriever
contextualize_q_system_prompt = """Given a chat history and the latest user question \
which might reference context in the chat history, formulate a standalone question \
which can be understood without the chat history. Do NOT answer the question, \
just reformulate it if needed and otherwise return it as is.
"""
contextualize_q_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", contextualize_q_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}")
    ]
)
history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)

# QA system prompt and chain
qa_system_prompt = """You are a highly skilled information retrieval assistant. Use the following context to answer questions effectively. \
If you don't know the answer, simply state that you don't know. \
Your answer should be in {language} language. \
Provide answers in proper HTML format and keep them concise. \
When responding to queries, follow these guidelines: \
    1. Provide Clear Answers: \
        - Ensure the response directly addresses the query with accurate and relevant information.\
    2. Include Detailed References: \
        - Links to Sources: Include URLs to credible sources where users can verify information or explore further. \
        - Reference Sites: Mention specific websites or platforms that offer additional information. \
        - Downloadable Materials: Provide links to any relevant downloadable resources if applicable. \
    
    3. Formatting for Readability: \
        - The answer should be in a proper HTML format with appropriate tags. \
        - For arabic language response align the text to right and convert numbers also.
        - Double check if the language of answer is correct or not.
        - Use bullet points or numbered lists where applicable to present information clearly. \
        - Highlight key details using bold or italics. \
        - Provide proper and meaningful abbreviations for urls. Do not include naked urls. \
    
    4. Organize Content Logically: \
        - Structure the content in a logical order, ensuring easy navigation and understanding for the user. \
        
{context}
"""
qa_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", qa_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}")
    ]
)
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)

# Retrieval and Generative (RAG) Chain
rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)

# Chat message history storage
store = {}

def clean_temporary_data():
    store.clear()

def get_session_history(session_id: str) -> BaseChatMessageHistory:
    if session_id not in store:
        store[session_id] = ChatMessageHistory()
    return store[session_id]

# Conversational RAG chain with message history
conversational_rag_chain = RunnableWithMessageHistory(
    rag_chain,
    get_session_history,
    input_messages_key="input",
    history_messages_key="chat_history",
    language_message_key="language",
    output_messages_key="answer",
)

# Function to handle WebSocket connection
@socketio.on('connect')
def handle_connect():
    print(f"Client connected: {request.sid}")
    emit('connection_response', {'message': 'Connected successfully.'})

# Function to handle WebSocket disconnection
@socketio.on('disconnect')
def handle_disconnect():
    print(f"Client disconnected: {request.sid}")
    clean_temporary_data()

# Function to handle WebSocket messages
@socketio.on('message')
def handle_message(data):
    question = data.get('question')
    language = data.get('language')
    if "en" in language:
        language = "English"
    else:
        language = "Arabic"
    session_id = data.get('session_id', SESSION_ID_DEFAULT)
    try:
        for chunk in conversational_rag_chain.stream(
                {"input": question, 'language': language},
                config={"configurable": {"session_id": session_id}},
            ):
            emit('response', chunk['answer'], room=request.sid)
    except Exception as e:
        print(f"Error during message handling: {e}")
        emit('response', {"error": "An error occurred while processing your request."}, room=request.sid)


# Home route
@app.route("/")
def index_view():
    return render_template('chat.html') 

# Main function to run the app
if __name__ == '__main__':
    socketio.run(app, debug=True)