from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import openai
from langchain.chat_models import ChatOpenAI
from langchain.callbacks import get_openai_callback
from PyPDF2 import PdfReader

def process_text(text):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )

    chunks = text_splitter.split_text(text)

    embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

    knowledgeBase=FAISS.from_texts(chunks,embeddings)

    return knowledgeBase

def summarizer(pdf):
    if pdf is not None:
        pdf_reader=PdfReader(pdf)
        text = ""

        for page in pdf_reader.pages:
            text += page.extract_text() or ""

        knowledgeBase = process_text(text)

        query="Summarize the content of the uploaded PDF file in 10-15 sentences."

        if query:
            docs=knowledgeBase.similarity_search(query)

            OpenAIModel = "gpt-3.5-turbo-16k"
            llm = ChatOpenAI(model=OpenAIModel, temperature=0.7)

            chain=load_qa_chain(llm, chain_type='stuff')

            with get_openai_callback() as cost:
                response=chain.run(input_documents=docs, question=query)
                print(cost)
                return response