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

# 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"

# bnb_config = transformers.BitsAndBytesConfig(
#     load_in_4bit=True,
#     bnb_4bit_quant_type='nf4',
#     bnb_4bit_use_double_quant=True,
#     bnb_4bit_compute_dtype=torch.bfloat16
# )

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,
    # quantization_config=bnb_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
)

# Function to retrieve answer using the RAG system
@spaces.GPU(duration=120)
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

# Gradio interface
with gr.Blocks() as interface:
    gr.Markdown("## RAG Chatbot")
    gr.Markdown("Ask a question and get answers based on retrieved documents.")
    
    input_box = gr.Textbox(label="Enter your question", placeholder="Type your question here...")
    submit_btn = gr.Button("Submit")
    # clear_btn = gr.Button("Clear")
    chat_history = gr.Chatbot(label="Chat History")
    
    submit_btn.click(chat, inputs=[input_box, chat_history], outputs=[chat_history, input_box])
    # clear_btn.click(clear_input, outputs=input_box)

interface.launch()