import time

import pandas as pd
from sentence_transformers import SentenceTransformer
sentence_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2",cache_folder=r"C:\Users\HP\.cache\huggingface\hub\models--sentence-transformers--all-MiniLM-L6-v2")

def get_microseconds_list(length=3):
    # Get the current time in microseconds
    microseconds = int(time.time() * 1_000_000)
    
    # Create a list with three microseconds
    return [microseconds + i for i in range(length)]


def topic_sort(path1,query, path2='', path3='',isForCompetitorAnalysis=False):
  # sentence_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2",cache_folder=cache_dir)
  if isForCompetitorAnalysis==True:
      df=pd.read_csv(path1)
  else:
    df0 = pd.read_csv(path1)
    df1 = pd.read_csv(path2)
    df2 = pd.read_csv(path3)
    df = pd.concat([df0, df1, df2],axis=0)
    df = df.drop_duplicates("title")
  df = df.reset_index(drop=True)
  df = df.drop("index", axis = 1)
  title = df["title"]
  sentences = [query] + list(title)
  embeddings = sentence_model.encode(sentences)
  similarities = sentence_model.similarity(embeddings[0], embeddings)
  print(len(similarities[0]))
  df["similarity"] = similarities[0][1:]
  df = df.sort_values(by='similarity', ascending=False)
  df = df.reset_index(drop=True)
  df = df.head(30)
  df = df.sort_values(by=['comment_count','votes_count'], ascending=False)
  df = df.reset_index(drop=True)
  df = df.head(18)
  return df

reddit_services_names= ['Pain point analysis','Competitor analysis']