import shutil
import gradio as gr
from helper_functions import print_results
from HybridRetriever import HybridRetriever
from ChatEngine import ChatEngine
from configs import *

import warnings
warnings.simplefilter("ignore", UserWarning)
warnings.simplefilter("ignore", FutureWarning)

from llama_index.retrievers.bm25 import BM25Retriever 
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core import VectorStoreIndex, Document
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
from llama_index.core.node_parser import SentenceSplitter
import fitz

# Configuration settings
llm = HuggingFaceLLM(model_name=MODEL_NAME, 
                      tokenizer_name=MODEL_NAME, 
                      system_prompt=SYSTEM_PROMPT, 
                      context_window=CONTEXT_WINDOW, 
                      generate_kwargs={"temperature": TEMPERATURE},
                      device_map=DEVICE)
embedding = HuggingFaceEmbedding(model_name=EMBEDDING_NAME, 
                                 device=DEVICE, 
                                 trust_remote_code=True) 

Settings.llm = llm
Settings.embed_model = embedding

# Define the directory to save uploaded files
UPLOAD_DIR = "./data"

def process_file(file):
    file_extension = file.name.split(".")[-1].lower()

    pdf_document = fitz.open(file.name, filetype=file_extension)
    
    text = ""
    for page_num in range(pdf_document.page_count):
        page = pdf_document.load_page(page_num)
        text += page.get_text("text")
    
    pdf_document.close()
    
    return Document(text=text)

def process_and_respond(file, question):
    documents = [process_file(file)]

    text_splitter = SentenceSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)

    vector_index = VectorStoreIndex.from_documents(
        documents, transformations=[text_splitter], embed_model=Settings.embed_model, show_progress=True
    )

    bm25_retriever = BM25Retriever(nodes=documents, similarity_top_k=TOP_K, tokenizer=text_splitter.split_text)
    vector_retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=TOP_K)
    hybrid_retriever = HybridRetriever(bm25_retriever=bm25_retriever, vector_retriever=vector_retriever)

    chat_engine = ChatEngine(hybrid_retriever)

    response = chat_engine.ask_question(question, llm)

    shutil.rmtree(UPLOAD_DIR)

    return response


with gr.Blocks() as demo:
    gr.Markdown("## Chat with Your Documents!")

    text_input = gr.Textbox(label="Ask a Question:")
    file_uploader = gr.File(label="Upload a File:")
    response_box = gr.Markdown()

    submit_button = gr.Button("Submit")

    submit_button.click(
        fn=process_and_respond,
        inputs=[file_uploader, text_input],
        outputs=response_box
    )

demo.launch()