import os
# import dotenv
import openai
import pinecone
from langchain.document_loaders import Docx2txtLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import hashlib
from time import sleep
from helper import append_file
import json

## Read the environment variables
# dotenv.load_dotenv('.env')
openai.api_key = os.getenv('OPENAI_API_KEY')
embedding_model = os.getenv('EMBEDDING_ENGINE')
debug_mode = os.getenv('DEBUG')
file_path = os.getenv('GAME_DOCS_FOLDER')
file_name = os.getenv('GAME_DOCS_FILE')
game_index = os.getenv('GAME_ID_INDEX')

pinecone_api_key = os.getenv('PINECONE_API_KEY')
pinecone_env = os.getenv('PINECONE_REGION')
pinecone_index = os.getenv('PINECONE_INDEX')
pinecone.init(
    api_key=pinecone_api_key,  
    environment=pinecone_env 
)
# check if index_name' index already exists (only create index if not)
if pinecone_index not in pinecone.list_indexes():
    pinecone.create_index(pinecone_index, dimension=1536, metric="cosine", pods=1, pod_type="p1.x1")
    sleep(3)

vector_db = pinecone.Index(pinecone_index)


def perform_embedding(doclist):
    payload=list()
    m = hashlib.md5()  
    # convert file_name to unique ID
    m.update(file_name.encode('utf-8'))
    game_id = m.hexdigest()[:12]
    json_val = {"game_id":game_id, "game_file":file_name}
    append_file(f"{file_path}/{game_index}",json.dumps(json_val))

    for i in range(len(doclist)):
        unique_id = game_id + "-" + str(i)
        content = doclist[i].page_content
        content = content.encode(encoding='ASCII',errors='ignore').decode()
        response = openai.Embedding.create(model=embedding_model, input=content)
        metadata = {'game_id': game_id, 'split_count': i, 'text': content}
        vector = response['data'][0]['embedding']
        payload.append((unique_id, vector, metadata))

    return payload


def load_split_document():
    loader = Docx2txtLoader(file_path + "/" + file_name)
    word_doc_data = loader.load()    
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    docs = text_splitter.split_documents(word_doc_data)
    if debug_mode == 'True':
        print("Total count of splits created: " + str(len(docs)))
    return docs

def upload_game_docs():
    docs = load_split_document()
    payload = perform_embedding(docs)
    vector_db.upsert(payload)

if __name__ == '__main__':
    upload_game_docs()