from fastapi import FastAPI

# from transformers import pipeline
from txtai.embeddings import Embeddings
from txtai.pipeline import Extractor
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

import pandas as pd
import sqlite3
import os

# NOTE - we configure docs_url to serve the interactive Docs at the root path
# of the app. This way, we can use the docs as a landing page for the app on Spaces.
app = FastAPI(docs_url="/")
# app = FastAPI()

# pipe = pipeline("text2text-generation", model="google/flan-t5-small")


# @app.get("/generate")
# def generate(text: str):
#     """
#     Using the text2text-generation pipeline from `transformers`, generate text
#     from the given input text. The model used is `google/flan-t5-small`, which
#     can be found [here](https://huggingface.co/google/flan-t5-small).
#     """
#     output = pipe(text)
#     return {"output": output[0]["generated_text"]}


def load_embeddings(
    domain: str = "",
    db_present: bool = True,
    path: str = "sentence-transformers/all-MiniLM-L6-v2",
    index_name: str = "index",
):
    # Create embeddings model with content support
    embeddings = Embeddings({"path": path, "content": True})

    # if Vector DB is not present
    if not db_present:
        return embeddings
    else:
        if domain == "":
            embeddings.load(index_name)  # change this later
        else:
            print(3)
            embeddings.load(f"{index_name}/{domain}")
        return embeddings


def _check_if_db_exists(db_path: str) -> bool:
    return os.path.exists(db_path)


def _text_splitter(doc):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=50,
        length_function=len,
    )
    return text_splitter.transform_documents(doc)


def _load_docs(path: str):
    load_doc = WebBaseLoader(path).load()
    doc = _text_splitter(load_doc)
    return doc


def _stream(dataset, limit, index: int = 0):
    for row in dataset:
        yield (index, row.page_content, None)
        index += 1

        if index >= limit:
            break


def _max_index_id(path):
    db = sqlite3.connect(path)

    table = "sections"
    df = pd.read_sql_query(f"select * from {table}", db)
    return {"max_index": df["indexid"].max()}


def _upsert_docs(doc, embeddings, vector_doc_path: str, db_present: bool):
    print(vector_doc_path)
    if db_present:
        print(1)
        max_index = _max_index_id(f"{vector_doc_path}/documents")
        print(max_index)
        embeddings.upsert(_stream(doc, 500, max_index["max_index"]))
        print("Embeddings done!!")
        embeddings.save(vector_doc_path)
        print("Embeddings done - 1!!")
    else:
        print(2)
        embeddings.index(_stream(doc, 500, 0))
        embeddings.save(vector_doc_path)
        max_index = _max_index_id(f"{vector_doc_path}/documents")
        print(max_index)
    # check
    # max_index = _max_index_id(f"{vector_doc_path}/documents")
    # print(max_index)
    return max_index


# def prompt(question):
#     return f"""Answer the following question using only the context below. Say 'no answer' when the question can't be answered.
#             Question: {question}
#             Context: """


# def search(query, question=None):
#     # Default question to query if empty
#     if not question:
#         question = query

#     return extractor([("answer", query, prompt(question), False)])[0][1]


# @app.get("/rag")
# def rag(question: str):
#     # question = "what is the document about?"
#     answer = search(question)
#     # print(question, answer)
#     return {answer}


# @app.get("/index")
# def get_url_file_path(url_path: str):
#     embeddings = load_embeddings()
#     doc = _load_docs(url_path)
#     embeddings, max_index = _upsert_docs(doc, embeddings)
#     return max_index


@app.get("/index/{domain}/")
def get_domain_file_path(domain: str, file_path: str):
    print(domain, file_path)
    print(os.getcwd())
    bool_value = _check_if_db_exists(db_path=f"{os.getcwd()}\index\{domain}\documents")
    print(bool_value)
    if bool_value:
        embeddings = load_embeddings(domain=domain, db_present=bool_value)
        print(embeddings)
        doc = _load_docs(file_path)
        max_index = _upsert_docs(
            doc=doc,
            embeddings=embeddings,
            vector_doc_path=f"index/{domain}",
            db_present=bool_value,
        )
        # print("-------")
    else:
        embeddings = load_embeddings(domain=domain, db_present=bool_value)
        doc = _load_docs(file_path)
        max_index = _upsert_docs(
            doc=doc,
            embeddings=embeddings,
            vector_doc_path=f"index/{domain}",
            db_present=bool_value,
        )
    # print("Final - output : ", max_index)
    return "Executed Successfully!!"