import os
import torch
import streamlit as st
from streamlit_chat import message
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.llms import HuggingFacePipeline
from langchain.document_loaders import PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from constants import CHROMA_SETTINGS

st.set_page_config(layout="centered")

checkpoint = "MBZUAI/LaMini-T5-738M"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(
    checkpoint, 
    device_map="auto",
    torch_dtype=torch.float32
)

@st.cache_resource
def data_ingestion(filepath):
    loader = PDFMinerLoader(filepath)
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    texts = text_splitter.split_documents(documents)

    def embedding_function(text):
        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(model.device)
        with torch.no_grad():
            embeddings = model.encoder(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
        return embeddings

    db = Chroma.from_documents(texts, persist_directory="db", embedding_function=embedding_function)
    db.persist()
    db = None


@st.cache_resource
def llm_pipeline():
    pipe = pipeline(
        'text2text-generation',
        model=model,
        tokenizer=tokenizer,
        max_length=256,
        do_sample=True,
        temperature=0.3,
        top_p=0.95
    )
    local_llm = HuggingFacePipeline(pipeline=pipe)
    return local_llm

@st.cache_resource
def qa_llm():
    llm = llm_pipeline()
    def embedding_function(text):
        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(model.device)
        with torch.no_grad():
            embeddings = model.encoder(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
        return embeddings

    db = Chroma(persist_directory="db", embedding_function=embedding_function)
    retriever = db.as_retriever()
    qa = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )
    return qa

def process_answer(instruction):
    qa = qa_llm()
    generated_text = qa(instruction)
    answer = generated_text['result']
    return answer

def display_conversation(history):
    for i in range(len(history["generated"])):
        message(history["past"][i], is_user=True, key=str(i) + "_user")
        message(history["generated"][i], key=str(i))

def main():
    st.markdown("<h1 style='text-align: center;'>Chat with your PDF</h1>", unsafe_allow_html=True)
    st.markdown("<h2 style='text-align: center;'>Upload your PDF</h2>", unsafe_allow_html=True)
    uploaded_file = st.file_uploader("", type=["pdf"])

    if uploaded_file is not None:
        # Ensure the 'docs' directory exists
        if not os.path.exists("docs"):
            os.makedirs("docs")

        filepath = "docs/" + uploaded_file.name
        with open(filepath, "wb") as temp_file:
            temp_file.write(uploaded_file.read())

        with st.spinner('Embeddings are creating...'):
            data_ingestion(filepath)
        st.success('Embeddings are created successfully!')

        user_input = st.text_input("", key="input")

        if "generated" not in st.session_state:
            st.session_state["generated"] = ["I am ready to help you"]
        if "past" not in st.session_state:
            st.session_state["past"] = ["Hey there!"]

        if user_input:
            answer = process_answer({'query': user_input})
            st.session_state["past"].append(user_input)
            st.session_state["generated"].append(answer)

        display_conversation(st.session_state)
if __name__ == "__main__":
    main()