import gradio as gr
from llama_index.core import VectorStoreIndex, Document
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.huggingface import HuggingFaceLLM
import csv
from docx import Document as DocxDocument
import fitz
import os
import torch
from HybridRetriever import HybridRetriever
from ChatEngine import ChatEngine
from llama_index.retrievers.bm25 import BM25Retriever 
from llama_index.core.retrievers import VectorIndexRetriever

lm_list = {
    "google/gemma-2-9b-it": "google/gemma-2-9b-it",
    "mistralai/Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3"
}

query_engine = None

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

    if file_extension == 'txt':
        with open(file.name, 'r', encoding='utf-8') as f:
            text = f.read()

    elif file_extension == 'csv':
        with open(file.name, 'r', encoding='utf-8') as f:
            reader = csv.reader(f)
            text = '\n'.join(','.join(row) for row in reader)

    elif file_extension == 'pdf':
        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()

    elif file_extension == 'docx':
        docx_document = DocxDocument(file.name)
        text = ""
        for paragraph in docx_document.paragraphs:
            text += paragraph.text + "\n"

    return [Document(text=text)]

def handle_file_upload(file, llm_name, question):
    global query_engine
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    llm = HuggingFaceLLM(model_name=llm_name)

    documents = process_file(file)

    text_splitter = SentenceSplitter(chunk_size=512, chunk_overlap=10)
    Settings.embed_model = HuggingFaceEmbedding(model_name="nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
    Settings.text_splitter = text_splitter
    index = VectorStoreIndex.from_documents(
        documents, transformations=[text_splitter], embed_model=Settings.embed_model
    )
    
    bm25_retriever = BM25Retriever(nodes=documents, similarity_top_k=2, tokenizer=text_splitter.split_text)
    vector_retriever = VectorIndexRetriever(index=index, similarity_top_k=2)
    hybrid_retriever = HybridRetriever(bm25_retriever=bm25_retriever, vector_retriever=vector_retriever)
    chat_engine = ChatEngine(hybrid_retriever)
    response = chat_engine.ask_question(question, llm)
    return response

def document_qa(file_upload, llm_choice, question_input):
    response = handle_file_upload(file_upload, llm_choice, question_input)
    return response


llm_choice = gr.Dropdown(choices=list(lm_list.values()), label="Choose LLM")
file_upload = gr.File(label="Upload Document")
question_input = gr.Textbox(label="Enter your question")

gr.Interface(
    fn=document_qa,
    inputs=[file_upload, llm_choice, question_input],
    outputs=gr.Textbox(label="Answer"),
    title="Document Question Answering",
    description="Upload a document and choose a language model to get answers.",
    allow_flagging=False
).launch()