from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores.faiss import FAISS
from langchain import OpenAI, Cohere
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.docstore.document import Document
from langchain.vectorstores import FAISS, VectorStore
import docx2txt
from typing import List, Dict, Any
import re
import numpy as np
from io import StringIO
from io import BytesIO
import streamlit as st
from prompts import STUFF_PROMPT
from pypdf import PdfReader
from openai.error import AuthenticationError
import pptx

@st.experimental_memo()
def parse_docx(file: BytesIO) -> str:
    text = docx2txt.process(file)
    # Remove multiple newlines
    text = re.sub(r"\n\s*\n", "\n\n", text)
    return text


@st.experimental_memo()
def parse_pdf(file: BytesIO) -> List[str]:
    pdf = PdfReader(file)
    output = []
    for page in pdf.pages:
        text = page.extract_text()
        # Merge hyphenated words
        text = re.sub(r"(\w+)-\n(\w+)", r"\1\2", text)
        # Fix newlines in the middle of sentences
        text = re.sub(r"(?<!\n\s)\n(?!\s\n)", " ", text.strip())
        # Remove multiple newlines
        text = re.sub(r"\n\s*\n", "\n\n", text)

        output.append(text)

    return output


@st.experimental_memo()
def parse_txt(file: BytesIO) -> str:
    text = file.read().decode("utf-8")
    # Remove multiple newlines
    text = re.sub(r"\n\s*\n", "\n\n", text)
    return text

@st.experimental_memo()
def parse_pptx(file: BytesIO) -> str:
    
    ppt_file = pptx.Presentation(file)

    string_data = ""
    
    for slide in ppt_file.slides:
        for shape in slide.shapes:
            if shape.has_text_frame:
                string_data += shape.text_frame.text + '\n'
    return string_data

@st.experimental_memo()
def parse_csv(uploaded_file):
    # To read file as bytes:
    #bytes_data = uploaded_file.getvalue()
    #st.write(bytes_data)

    # To convert to a string based IO:
    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
    #st.write(stringio)

    # To read file as string:
    string_data = stringio.read()
    #st.write(string_data)

    # Can be used wherever a "file-like" object is accepted:
    # dataframe = pd.read_csv(uploaded_file)
    return string_data


@st.cache(allow_output_mutation=True)
def text_to_docs(text: str) -> List[Document]:
    """Converts a string or list of strings to a list of Documents
    with metadata."""
    if isinstance(text, str):
        # Take a single string as one page
        text = [text]
    page_docs = [Document(page_content=page) for page in text]

    # Add page numbers as metadata
    for i, doc in enumerate(page_docs):
        doc.metadata["page"] = i + 1

    # Split pages into chunks
    doc_chunks = []

    for doc in page_docs:
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=800,
            separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
            chunk_overlap=0,
        )
        chunks = text_splitter.split_text(doc.page_content)
        for i, chunk in enumerate(chunks):
            doc = Document(
                page_content=chunk, metadata={"page": doc.metadata["page"], "chunk": i}
            )
            # Add sources a metadata
            doc.metadata["source"] = f"{doc.metadata['page']}-{doc.metadata['chunk']}"
            doc_chunks.append(doc)
    return doc_chunks


@st.cache(allow_output_mutation=True, show_spinner=False)
def embed_docs(docs: List[Document]) -> VectorStore:
    """Embeds a list of Documents and returns a FAISS index"""

    if not st.session_state.get("OPENAI_API_KEY"):
        raise AuthenticationError(
            "Enter your OpenAI API key in the sidebar. You can get a key at https://platform.openai.com/account/api-keys."
        )
    else:
        # Embed the chunks
        embeddings = OpenAIEmbeddings(openai_api_key=st.session_state.get("OPENAI_API_KEY"))  # type: ignore
        index = FAISS.from_documents(docs, embeddings)

        return index


@st.cache(allow_output_mutation=True)
def search_docs(index: VectorStore, query: str) -> List[Document]:
    """Searches a FAISS index for similar chunks to the query
    and returns a list of Documents."""

    # Search for similar chunks
    docs = index.similarity_search(query, k=5)
    return docs


@st.cache(allow_output_mutation=True)
def get_answer(docs: List[Document], query: str) -> Dict[str, Any]:
    """Gets an answer to a question from a list of Documents."""

    # Get the answer
    chain = load_qa_with_sources_chain(OpenAI(temperature=0, openai_api_key=st.session_state.get("OPENAI_API_KEY")), chain_type="stuff", prompt=STUFF_PROMPT)  # type: ignore

    answer = chain(
        {"input_documents": docs, "question": query}, return_only_outputs=True
    )
    return answer


@st.cache(allow_output_mutation=True)
def get_sources(answer: Dict[str, Any], docs: List[Document]) -> List[Document]:
    """Gets the source documents for an answer."""

    # Get sources for the answer
    source_keys = [s for s in answer["output_text"].split("SOURCES: ")[-1].split(", ")]

    source_docs = []
    for doc in docs:
        if doc.metadata["source"] in source_keys:
            source_docs.append(doc)

    return source_docs


def wrap_text_in_html(text: str) -> str:
    """Wraps each text block separated by newlines in <p> tags"""
    if isinstance(text, list):
        # Add horizontal rules between pages
        text = "\n<hr/>\n".join(text)
    return "".join([f"<p>{line}</p>" for line in text.split("\n")])