import gradio as gr
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA
from transformers import AutoConfig, AutoTokenizer, pipeline, AutoModelForCausalLM
from langchain_community.document_loaders import DirectoryLoader
import torch
import re
import transformers
import spaces
import requests
from urllib.parse import urlencode

# Initialize embeddings and ChromaDB
model_name = "sentence-transformers/all-mpnet-base-v2"
device = "cuda" if torch.cuda.is_available() else "cpu"
model_kwargs = {"device": device}
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)

loader = DirectoryLoader('./example', glob="**/*.pdf", recursive=True, use_multithreading=True)
docs = loader.load()
vectordb = Chroma.from_documents(documents=docs, embedding=embeddings, persist_directory="companies_db")
books_db = Chroma(persist_directory="./companies_db", embedding_function=embeddings)
books_db_client = books_db.as_retriever()

# Initialize the model and tokenizer
model_name = "stabilityai/stablelm-zephyr-3b"

model_config = transformers.AutoConfig.from_pretrained(model_name, max_new_tokens=1024)
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    config=model_config,
    device_map=device,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

query_pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    return_full_text=True,
    torch_dtype=torch.float16,
    device_map=device,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    top_k=50,
    max_new_tokens=256
)

llm = HuggingFacePipeline(pipeline=query_pipeline)

books_db_client_retriever = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=books_db_client,
    verbose=True
)

# OAuth Configuration
TENANT_ID = '2b093ced-2571-463f-bc3e-b4f8bcb427ee'
CLIENT_ID = '2a7c884c-942d-49e2-9e5d-7a29d8a0d3e5'
CLIENT_SECRET = 'EOF8Q~kKHCRgx8tnlLM-H8e93ifetxI6x7sU6bGW'
REDIRECT_URI = 'https://sanjeevbora-chatbot.hf.space/'
AUTH_URL = f"https://login.microsoftonline.com/2b093ced-2571-463f-bc3e-b4f8bcb427ee/oauth2/v2.0/authorize"
TOKEN_URL = f"https://login.microsoftonline.com/2b093ced-2571-463f-bc3e-b4f8bcb427ee/oauth2/v2.0/token"

params = {
    'client_id': CLIENT_ID,
    'response_type': 'code',
    'redirect_uri': REDIRECT_URI,
    'response_mode': 'query',
    'scope': 'User.Read',
    'state': '12345'  # Optional state parameter
}

# Construct the login URL
login_url = f"{AUTH_URL}?{urlencode(params)}"

# Gradio interface
def show_login_button():
    return f'<a href="{login_url}" target="_blank">Click here to login with Microsoft</a>'

# Dummy function to simulate token validation (you will replace this with actual validation)
def is_logged_in(token):
    # Check if the token exists (or check if it's valid)
    return token is not None

# Gradio interface
def check_login(status):
    # If logged in, show the chatbot interface, otherwise show login link
    if status:
        return gr.update(visible=True), gr.update(visible=True)
    else:
        return gr.update(visible=False), gr.update(visible=False)
    
# Function to exchange authorization code for access token
def exchange_code_for_token(auth_code):
    data = {
        'grant_type': 'authorization_code',
        'client_id': CLIENT_ID,
        'client_secret': CLIENT_SECRET,
        'code': auth_code,
        'redirect_uri': REDIRECT_URI
    }
    
    response = requests.post(TOKEN_URL, data=data)
    
    if response.status_code == 200:
        token_data = response.json()
        access_token = token_data.get('access_token')
        return access_token
    else:
        return None

def login_user(auth_code):
    # Exchange the authorization code for an access token
    token = exchange_code_for_token(auth_code)
    
    if token:
        return token
    else:
        return None

# Function to retrieve answer using the RAG system
@spaces.GPU(duration=60)
def test_rag(query):
    books_retriever = books_db_client_retriever.run(query)
    
    # Extract the relevant answer using regex
    corrected_text_match = re.search(r"Helpful Answer:(.*)", books_retriever, re.DOTALL)
    
    if corrected_text_match:
        corrected_text_books = corrected_text_match.group(1).strip()
    else:
        corrected_text_books = "No helpful answer found."
    
    return corrected_text_books

# Define the Gradio interface
def chat(query, history=None):
    if history is None:
        history = []
    if query:
        answer = test_rag(query)
        history.append((query, answer))
    return history, ""  # Clear input after submission

# Function to clear input text
def clear_input():
    return "",  # Return empty string to clear input field

with gr.Blocks() as interface:
    gr.Markdown("## RAG Chatbot")
    gr.Markdown("Please log in to continue.")
    
    # Custom HTML to show login link
    login_link = gr.HTML(f'<a href="{login_url}" target="_blank">Click here to login with Microsoft</a>')

    # Login button to simulate the login process
    login_button = gr.Button("Login")
    
    # Components for chat (initially hidden)
    input_box = gr.Textbox(label="Enter your question", placeholder="Type your question here...", visible=False)
    submit_btn = gr.Button("Submit", visible=False)
    chat_history = gr.Chatbot(label="Chat History", visible=False)

    # Handle login button click
    login_button.click(
        login_user, 
        inputs=[], 
        outputs=[login_button],  # You can also update the UI to show login status
        queue=False
    ).then(
        lambda token: check_login(is_logged_in(token)), 
        inputs=[], 
        outputs=[input_box, submit_btn]
    )

    # Input submission and chat handling
    submit_btn.click(chat, inputs=[input_box, chat_history], outputs=[chat_history, input_box])

interface.launch()