diff --git a/filtro.py b/filtro.py
deleted file mode 100755
index da0fbe7..0000000
--- a/filtro.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/env python3
-import requests
-import pandas as pd
-import numpy as np
-import os
-import csv
-
-MIN_MOVIES = 42  # Only keep relations for actors that have made more than this many movies
-
-#-----------------DOWNLOAD .GZ FILES FROM IMDB DATABASE-----------------#
-
-def download_url(url):
-  print("Downloading:", url)
-  file_name_start_pos = url.rfind("/") + 1
-  file_name = url[file_name_start_pos:]
-  if os.path.isfile(file_name):
-    print("Already downloaded: skipping")
-    return
-
-  r = requests.get(url, stream=True)
-  r.raise_for_status()
-  with open(file_name, 'wb') as f:
-    for chunk in r.iter_content(chunk_size=4096):
-      f.write(chunk)
-  return url
-
-urls = ["https://datasets.imdbws.com/name.basics.tsv.gz",
-        "https://datasets.imdbws.com/title.principals.tsv.gz",
-        "https://datasets.imdbws.com/title.basics.tsv.gz"]
-
-for url in urls:
-  download_url(url)
-
-os.makedirs("data", exist_ok=True) # Generate (recursively) folders, ignores the comand if they already exists
-
-#------------------------------FILTERING------------------------------#
-
-print("Filtering actors...")
-df_attori = pd.read_csv(
-  'name.basics.tsv.gz', sep='\t', compression='gzip',
-  usecols=['nconst', 'primaryName', 'primaryProfession'], # Considering only this columns
-  dtype={'primaryName': 'U', 'primaryProfession': 'U'}, # Both are unsigned integers
-  converters={'nconst': lambda x: int(x.lstrip("nm0"))}) # All actors starts with nm0, we are just cleaning the output
-df_attori.query('primaryProfession.str.contains("actor") or primaryProfession.str.contains("actress")', inplace=True)
-# A lot of actors/actresses do more than one job (director etc..), with this comand I take all the names that have the string "actor" or "actress" in their profession. In this way both someone who is classified as "actor" or as "actor, director" are taken into consideration
-
-print("Filtering films...")
-df_film = pd.read_csv(
-  'title.basics.tsv.gz', sep='\t', compression='gzip',
-  usecols=['tconst', 'primaryTitle', 'isAdult', 'titleType'], # Considering only this columns
-  dtype={'primaryTitle': 'U', 'titleType': 'U'}, # Both are unsigned integers
-  converters={'tconst': lambda x: int(x.lstrip("t0")), 'isAdult': lambda x: x != "0"}) # # All movies starts with t0, we are just cleaning the output. Then remove all adult movies
-df_film.query('not isAdult and titleType in ["movie", "tvSeries", "tvMovie", "tvMiniSeries"]',
-              inplace=True) # There are a lot of junk categories considered in IMDb, we are considering all the non Adult movies in this whitelist
-filtered_tconsts = df_film["tconst"].to_list()
-
-print("Filtering relations...")
-df_relazioni = pd.read_csv(
-  'title.principals.tsv.gz', sep='\t', compression='gzip',
-  usecols=['tconst', 'nconst','category'],  # Considering only this columns
-  dtype={'category': 'U'}, # Unsigned integer
-  converters={'nconst': lambda x: int(x.lstrip("nm0")), 'tconst': lambda x: int(x.lstrip("t0"))}) # Cleaning
-df_relazioni.query('(category == "actor" or category == "actress") and tconst in @filtered_tconsts', inplace=True)
-# Returns an array of unique actor ids (nconsts) and an array of how many times they appear (counts) => the number of movies they appear in
-nconsts, counts = np.unique(df_relazioni["nconst"].to_numpy(), return_counts=True)
-filtered_nconsts = nconsts[counts>=MIN_MOVIES]
-df_relazioni.query("nconst in @filtered_nconsts", inplace=True)
-
-# Now select only films and actors that have at lest a relation
-print("Re-filtering actors...")
-nconsts_with_relations = df_relazioni["nconst"].unique()
-df_attori.query("nconst in @nconsts_with_relations", inplace=True)
-print("Re-filtering films...")
-tconsts_with_relations = df_relazioni["tconst"].unique()
-df_film.query("tconst in @tconsts_with_relations", inplace=True)
-
-# Write the filtered files
-df_attori.to_csv('data/Attori.txt', sep='\t', quoting=csv.QUOTE_NONE, escapechar='\\', columns=['nconst', 'primaryName'], header=False, index=False)
-df_film.to_csv('data/FilmFiltrati.txt', sep='\t', quoting=csv.QUOTE_NONE, escapechar='\\', columns=['tconst', 'primaryTitle'], header=False, index=False)
-df_relazioni.to_csv('data/Relazioni.txt', sep='\t', quoting=csv.QUOTE_NONE, escapechar='\\', columns=['tconst', 'nconst'], header=False, index=False)
-
-# Takes about 1 min 30 s with MIN_MOVIES = 42 ----> kenobi with k=100 took 3m 48s
-# Takes about 3 min with MIN_MOVIES = 20 ----> kenobi with k=100 took 19m 34s