import subprocess 
import os
import torch
from dotenv import load_dotenv
from langchain_community.vectorstores import Qdrant
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from qdrant_client import QdrantClient, models
from langchain_openai import ChatOpenAI
import gradio as gr
import logging
from typing import List, Tuple, Generator
from dataclasses import dataclass
from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain_huggingface.llms import HuggingFacePipeline
from langchain_cerebras import ChatCerebras
from queue import Queue
from threading import Thread
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEndpoint

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class Message:
    role: str
    content: str
    timestamp: str

class ChatHistory:
    def __init__(self):
        self.messages: List[Message] = []
    
    def add_message(self, role: str, content: str):
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.messages.append(Message(role=role, content=content, timestamp=timestamp))
    
    def get_formatted_history(self, max_messages: int = 10) -> str:
        recent_messages = self.messages[-max_messages:] if len(self.messages) > max_messages else self.messages
        formatted_history = "\n".join([
            f"{msg.role}: {msg.content}" for msg in recent_messages
        ])
        return formatted_history
    
    def clear(self):
        self.messages = []

# Load environment variables and setup
load_dotenv()

HF_TOKEN = os.getenv("HF_TOKEN")
C_apikey = os.getenv("C_apikey")
OPENAPI_KEY = os.getenv("OPENAPI_KEY")

if not HF_TOKEN:
    logger.error("HF_TOKEN is not set in the environment variables.")
    exit(1)

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

try:
    client = QdrantClient(
        url=os.getenv("QDRANT_URL"),
        api_key=os.getenv("QDRANT_API_KEY"),
        prefer_grpc=False
    )
except Exception as e:
    logger.error("Failed to connect to Qdrant.")
    exit(1)

collection_name = "mawared"

try:
    client.create_collection(
        collection_name=collection_name,
        vectors_config=models.VectorParams(
            size=384,
            distance=models.Distance.COSINE
        )
    )
except Exception as e:
    if "already exists" not in str(e):
        logger.error(f"Error creating collection: {e}")
        exit(1)

db = Qdrant(
    client=client,
    collection_name=collection_name,
    embeddings=embeddings,
)

retriever = db.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 5}
)

# llm = ChatCerebras(
#     model="llama-3.3-70b",
#     api_key=C_apikey,
#     streaming=True
# )

repo_id = "meta-llama/Llama-3.3-70B-Instruct"

llm = HuggingFaceEndpoint(
    repo_id=repo_id,
    max_new_tokens=8192,
    temperature=0.1,
    huggingfacehub_api_token=HF_TOKEN,
    streaming=True,
    
)

template = """
You are a Friendly assistant specializing in the Mawared HR System.
Your role is to provide precise and contextually relevant answers based on the retrieved context and chat history.
Your top priority is user experience and satisfaction, only answer questions based on Mawared HR system and ignore everything else.

Key Responsibilities:

Use the given chat history and retrieved context to craft accurate and detailed responses.
If necessary, ask specific and targeted clarifying questions to gather more information.
Present step-by-step instructions in a clear, numbered format when applicable.
If you think you will not be able to provide a clear answer based on the user question , ask a clariifying question and ask for more details.

Previous Conversation: {chat_history}
Retrieved Context: {context}
Current Question: {question}
Answer:
"""

prompt = ChatPromptTemplate.from_template(template)

def create_rag_chain(chat_history: str):
    chain = (
        {
            "context": retriever,
            "question": RunnablePassthrough(),
            "chat_history": lambda x: chat_history
        }
        | prompt
        | llm
        | StrOutputParser()
    )
    return chain

chat_history = ChatHistory()

def process_stream(stream_queue: Queue, history: List[List[str]]) -> Generator[List[List[str]], None, None]:
    """Process the streaming response and update the chat interface"""
    current_response = ""
    
    while True:
        chunk = stream_queue.get()
        if chunk is None:  # Signal that streaming is complete
            break
            
        current_response += chunk
        new_history = history.copy()
        new_history[-1][1] = current_response  # Update the assistant's message
        yield new_history


def ask_question_gradio(question: str, history: List[List[str]]) -> Generator[tuple, None, None]:
    try:
        if history is None:
            history = []
            
        chat_history.add_message("user", question)
        formatted_history = chat_history.get_formatted_history()
        rag_chain = create_rag_chain(formatted_history)
        
        # Update history with user message and empty assistant message
        history.append([question, ""])  # User message
        
        # Create a queue for streaming responses
        stream_queue = Queue()
        
        # Function to process the stream in a separate thread
        def stream_processor():
            try:
                for chunk in rag_chain.stream(question):
                    stream_queue.put(chunk)
                stream_queue.put(None)  # Signal completion
            except Exception as e:
                logger.error(f"Streaming error: {e}")
                stream_queue.put(None)
        
        # Start streaming in a separate thread
        Thread(target=stream_processor).start()
        
        # Yield updates to the chat interface
        response = ""
        for updated_history in process_stream(stream_queue, history):
            response = updated_history[-1][1]
            yield "", updated_history
        
        # Add final response to chat history
        chat_history.add_message("assistant", response)
        
    except Exception as e:
        logger.error(f"Error during question processing: {e}")
        if not history:
            history = []
        history.append([question, "An error occurred. Please try again later."])
        yield "", history

def clear_chat():
    chat_history.clear()
    return [], ""

# Gradio Interface
with gr.Blocks(theme='Hev832/Applio') as iface:
    gr.Image("Image.jpg", width=750, height=300, show_label=False, show_download_button=False)
    gr.Markdown("# Mawared HR Assistant 2.6.4")
    gr.Markdown('### Instructions')
    gr.Markdown("Ask a question about MawaredHR and get a detailed answer, if you get an error try again with same prompt, its an Api issue and we are working on it 😀")
    
    chatbot = gr.Chatbot(
        height=750,
        show_label=False,
        bubble_full_width=False,
    )
    
    with gr.Row():
        with gr.Column(scale=20):
            question_input = gr.Textbox(
                label="Ask a question:",
                placeholder="Type your question here...",
                show_label=False
            )
        with gr.Column(scale=4):
            with gr.Row():
                with gr.Column():
                    send_button = gr.Button("Send", variant="primary", size="sm")
                    clear_button = gr.Button("Clear Chat", size="sm")
    
    # Handle both submit events (Enter key and Send button)
    submit_events = [question_input.submit, send_button.click]
    for submit_event in submit_events:
        submit_event(
            ask_question_gradio,
            inputs=[question_input, chatbot],
            outputs=[question_input, chatbot]
        )
    
    clear_button.click(
        clear_chat,
        outputs=[chatbot, question_input]
    )

if __name__ == "__main__":
    iface.launch()