import os
import pinecone

from database import *
import pandas as pd

from sentence_transformers import SentenceTransformer

from tqdm import tqdm

database_url = "sqlite:///jlm.db"
engine, Session = init_db_stuff(database_url)

PINECONE_KEY = os.getenv("PINECONE_API_DEFAULT")
pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")
index = pinecone.Index("movies")

model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")

batch_size = 32

df = pd.read_sql("Select * from movies", engine)
df["combined_text"] = (
    df["title"]
    + ": "
    + df["overview"].fillna("")
    + " -  "
    + df["tagline"].fillna("")
    + " Genres:-  "
    + df["genres"].fillna("")
)

print(f'Length of Combined Text: {len(df["combined_text"].tolist())}')

for x in tqdm(range(0, len(df), batch_size)):
    to_send = []
    trakt_ids = df["trakt_id"][x : x + batch_size].tolist()
    sentences = df["combined_text"][x : x + batch_size].tolist()
    embeddings = model.encode(sentences)
    for idx, value in enumerate(trakt_ids):
        to_send.append((str(value), embeddings[idx].tolist()))
    index.upsert(to_send)