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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(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(
{
value: embeddings[idx].tolist()
})
print(to_send)
index.upsert(to_send)
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