build_movie_recommender.py

def buildMovieRecommender(movie_names, vectorized_plots, top_k=10):
  retriever = L2Retriever(vectorized_plots.shape[1], use_norm=True, top_k=top_k, use_gpu=False)
  vectorized_norm = np.sum(vectorized_plots**2, axis=1).reshape((-1,1))
  
  def recommend(query):
    try:
      idx = retriever.predict(vectorized_plots, 
                              vectorized_plots[movie_names.index(query)], 
                              vectorized_norm)[0][1:]
      for i in idx:
        print(names[i])
    except ValueError:
      print("{} not found in movie db. Suggestions:")
      for i, name in enumerate(movie_names):
        if query.lower() in name.lower():
          print(i, name)
          
  return recommend