Rasa custom actions does not return lists in the shell

Question

I am making a Rasa chatbot that recommend movies ,with a dataset of 10,000 movies. there is 4 entities in the nlu and 4 slots for every one of them. the bot askes the user about the movie genre and the user is expected to answer something like (action,fantacy, ...) and in the actions file there is a preprocessing for the user input then the bot should ask about actors the user want to see in the movie, director and finaly the keywords. so every entity is saved in it's slot after that the bot should use these slots and put them in a list which will be added to the database and then use cosine similarty to compare the list with every row in the dataset and sort the output of the cosine similarty of every movie in the dataset and then print the first 10 movies that have the most similarity. the thing is i run the actions file on jupyter notebook and everything is fine, but when i try to put the code in the actions file it does not work.

and this is the database i am using in the codes: https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset

i hope someone help me fast and thank you all

#thats the code that run on jupyter notebook so smoothly
#and i want to run on Rasa actions file

import pandas as pd
import numpy as np
from ast import literal_eval

metadata = pd.read_csv("/Users/Mohamad Najeeb/Downloads/database_for_movies/movies_metadata.csv")
ratings = pd.read_csv("/Users/Mohamad Najeeb/Downloads/database_for_movies/ratings.csv")
credits = pd.read_csv("/Users/Mohamad Najeeb/Downloads/database_for_movies/credits.csv")
keywords = pd.read_csv("/Users/Mohamad Najeeb/Downloads/database_for_movies/keywords.csv")

metadata = metadata.iloc[0:10000,:]

keywords['id'] = keywords['id'].astype('int')
credits['id'] = credits['id'].astype('int')
metadata['id'] = metadata['id'].astype('int')

metadata = metadata.merge(credits, on='id')
metadata = metadata.merge(keywords, on='id')
metadata.shape

features = ['cast', 'crew', 'keywords', 'genres']
for feature in features:
    metadata[feature] = metadata[feature].apply(literal_eval)
    


def get_director(x):
    for i in x:
        if i['job'] == 'Director':
            return i['name']
    return np.nan
  

def get_list(x):
    if isinstance(x, list): 
        names = [i['name'] for i in x] 
        
        
        if len(names) > 3:
            names = names[:3]
        return names

    
    return []


metadata['director'] = metadata['crew'].apply(get_director)

features = ['cast', 'keywords', 'genres']
for feature in features:
    metadata[feature] = metadata[feature].apply(get_list)

def clean_data(x):
    if isinstance(x, list):
        return [str.lower(i.replace(" ", "")) for i in x] 
    else:
        
        if isinstance(x, str):
            return str.lower(x.replace(" ", ""))
        else:
            return ''
          

features = ['cast', 'keywords', 'director', 'genres']

for feature in features:
    metadata[feature] = metadata[feature].apply(clean_data)

    
def create_soup(x):
    return ' '.join(x['keywords']) + ' ' + ' '.join(x['cast']) + ' ' + x['director'] + ' ' + ' '.join(x['genres'])

metadata['soup'] = metadata.apply(create_soup, axis=1)

f= ['title', 'soup', 'cast', 'director', 'keywords', 'genres']


def get_genres():
    genres = input("What Movie Genre are you interested in (if multiple, please separate them with a comma)? [Type 'skip' to skip this question] ")
    genres = " ".join(["".join(n.split()) for n in genres.lower().split(',')])
    return genres

def get_actors():
    actors = input("Who are some actors within the genre that you love (if multiple, please separate them with a comma)? [Type 'skip' to skip this question] ")
    actors = " ".join(["".join(n.split()) for n in actors.lower().split(',')])
    return actors

def get_directors():
    directors = input("Who are some directors within the genre that you love (if multiple, please separate them with a comma)? [Type 'skip' to skip this question] ")
    directors = " ".join(["".join(n.split()) for n in directors.lower().split(',')])
    return directors

def get_keywords():
    keywords = input("What are some of the keywords that describe the movie you want to watch, like elements of the plot, whether or not it is about friendship, etc? (if multiple, please separate them with a comma)? [Type 'skip' to skip this question] ")
    keywords = " ".join(["".join(n.split()) for n in keywords.lower().split(',')])
    return keywords

def get_searchTerms():
    searchTerms = [] 
    genres = get_genres()
    if genres != 'skip':
        searchTerms.append(genres)

    actors = get_actors()
    if actors != 'skip':
        searchTerms.append(actors)

    directors = get_directors()
    if directors != 'skip':
        searchTerms.append(directors)

    keywords = get_keywords()
    if keywords != 'skip':
        searchTerms.append(keywords)
  
    return searchTerms

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def make_recommendation(metadata=metadata):
    new_row = metadata.iloc[-1,:].copy() 
  
  
    searchTerms = get_searchTerms()  
    new_row.iloc[-1] = " ".join(searchTerms) 
  
  
    metadata = metadata.append(new_row)
  
  
    count = CountVectorizer(stop_words='english')
    count_matrix = count.fit_transform(metadata['soup'])

 
    cosine_sim2 = cosine_similarity(count_matrix, count_matrix) 
 
    sim_scores = list(enumerate(cosine_sim2[-1,:]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)

  
    ranked_titles = []
    for i in range(1, 11):
        indx = sim_scores[i][0]
        ranked_titles.append([metadata['title'].iloc[indx], metadata['imdb_id'].iloc[indx]])
  
    return ranked_titles

make_recommendation()

#here is me trying to run it on Rasa actions #but it does not work as i expected

from typing import Any, Text, Dict, List from rasa_sdk import Action, Tracker from rasa_sdk.executor import CollectingDispatcher

import pandas as pd

import numpy as np

from ast import literal_eval

from sklearn.feature_extraction.text

import CountVectorizer from sklearn.metrics.pairwise

import cosine_similarity

metadata = pd.read_csv("data/movies_metadata.csv")

ratings = pd.read_csv("data/ratings.csv")

credits = pd.read_csv("data/credits.csv")

keywords = pd.read_csv("data/keywords.csv")

metadata = metadata.iloc[0:10000,:]

keywords['id'] = keywords['id'].astype('int')

credits['id'] = credits['id'].astype('int')

metadata['id'] = metadata['id'].astype('int')

metadata = metadata.merge(credits, on='id')

metadata = metadata.merge(keywords, on='id')

features = ['cast', 'crew', 'keywords', 'genres']

for feature in features:

metadata\[feature\] = metadata\[feature\].apply(literal_eval)

def get_director(x):

for i in x:

if i['job'] == 'Director':

    return i\['name'\]

return np.nan

def get_list(x):

if isinstance(x, list):

    names = \[i\['name'\] for i in x\]

    if len(names) > 3:
        names = names[:3]
    return names


return []

metadata['director'] = metadata['crew'].apply(get_director)

features = ['cast', 'keywords', 'genres']

for feature in features:

metadata\[feature\] = metadata\[feature\].apply(get_list)

def clean_data(x):

if isinstance(x, list):

return [str.lower(i.replace(" ", "")) for i in x] else:

    if isinstance(x, str):
        return str.lower(x.replace(" ", ""))
    else:
        return ''

features = ['cast', 'keywords', 'director', 'genres']

for feature in features:

metadata\[feature\] = metadata\[feature\].apply(clean_data)

def create_soup(x):

return ' '.join(x\['keywords'\]) + ' ' + ' '.join(x\['cast'\]) + ' ' + x\['director'\] + ' ' + ' '.join(x\['genres'\])

metadata['soup'] = metadata.apply(create_soup, axis=1)

class ActionMakeRecommendation(Action):

def name(self) -\> Text:

return

    "action_make_recommendation"

def run(self,
        dispatcher: CollectingDispatcher,
        tracker: Tracker,
        domain: Dict[Text, Any]
    ) -> List[Dict[Text, Any]]:
    
    searchtTerms= []
    
    #getting the features
    genres = " ".join(["".join(n.split()) for n in tracker.get_slot('genres').lower().split(',')])
    searchTerms.append(genres)
    
    actors = " ".join(["".join(n.split()) for n in tracker.get_slot('actors').lower().split(',')])
    searchTerms.append(actors)
    
    directors = " ".join(["".join(n.split()) for n in tracker.get_slot('directors').lower().split(',')])
    searchTerms.append(directors)
    
    keywords = " ".join(["".join(n.split()) for n in tracker.get_slot('keywords').lower().split(',')])
    searchTerms.append(keywords)
    
    new_row = metadata.iloc[-1,:].copy() 
    searchTerms = get_searchTerms()  
    new_row.iloc[-1] = " ".join(searchTerms) 
    
    metadata = metadata.append(new_row)


    count = CountVectorizer(stop_words='english')
    count_matrix = count.fit_transform(metadata['soup'])


    cosine_sim2 = cosine_similarity(count_matrix, count_matrix) 

    sim_scores = list(enumerate(cosine_sim2[-1,:]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)


    ranked_titles = []
    for i in range(1, 11):
        indx = sim_scores[i][0]
        ranked_titles.append([metadata['title'].iloc[indx], metadata['imdb_id'].iloc[indx]])

    dispatcher.utter_message(ranked_title)

`

#this is the domain file

version: "3.1"

intents:

• greet
• goodbye
• affirm
• deny
• mood_great
• mood_unhappy
• bot_challenge
• recommend_movies
• genres: use_entities: true
• actors: use_entities: true
• directors: use_entities: true
• keywords: use_entities: true

entities:

• genre
• actor
• director
• keyword

actions:

• action_make_recommendation

slots: genres: type: text mappings: - type: from_entity entity: genre

actors: type: text mappings: - type: from_entity entity: actor

directors: type: text mappings: - type: from_entity entity: director

keywords: type: text mappings: - type: from_entity entity: keyword

responses: utter_greet:

• text: "Hey! How are you?"

utter_cheer_up:

• text: "Here is something to cheer you up:" image: "https://i.imgur.com/nGF1K8f.jpg"

utter_did_that_help:

• text: "Did that help you?"

utter_happy:

• text: "Great, carry on!"

utter_goodbye:

• text: "Bye"

utter_iamabot:

• text: "I am a bot, powered by Rasa."

utter_genres_question:

• text: "What Movie Genre are you interested in??"

utter_actors_question:

• text: "Who are some actors within the genre that you love?"

utter_directors_question:

• text: "Who are some directors within the genre that you love?"

utter_keywords_question:

• text: "what keywords do you want to see in the movie?"

session_config: session_expiration_time: 0.0 carry_over_slots_to_new_session: true

''' #this is the nlu file

version: "3.1"

nlu:

• intent: greet examples: |

  • hey
  • hello
  • hi
  • hello there
  • good morning
  • good evening
  • moin
  • hey there
  • let's go
  • hey dude
  • goodmorning
  • goodevening
  • good afternoon

• intent: goodbye examples: |

  • cu
  • good by
  • cee you later
  • good night
  • bye
  • goodbye
  • have a nice day
  • see you around
  • bye bye
  • see you later

• intent: affirm examples: |

  • yes
  • y
  • indeed
  • of course
  • that sounds good
  • correct
  • okay
  • ok

• intent: deny examples: |

  • no
  • n
  • never
  • I don't think so
  • don't like that
  • no way
  • not really

• intent: mood_great examples: |

  • perfect
  • great
  • amazing
  • feeling like a king
  • wonderful
  • I am feeling very good
  • I am great
  • I am amazing
  • I am going to save the world
  • super stoked
  • extremely good
  • so so perfect
  • so good
  • so perfect

• intent: mood_unhappy examples: |

  • my day was horrible
  • I am sad
  • I don't feel very well
  • I am disappointed
  • super sad
  • I'm so sad
  • sad
  • very sad
  • unhappy
  • not good
  • not very good
  • extremly sad
  • so saad
  • so sad

• intent: bot_challenge examples: |

  • are you a bot?
  • are you a human?
  • am I talking to a bot?
  • am I talking to a human?

• intent: recommend_movies examples: |

  • recommend me a movie
  • i want to watch a movie
  • can you recommend me a movie

• intent: genres examples: |

  • [action](genre)
  • [thriller](genre) , [fantacy](genre)
  • [comedy](genre) , [scince fiction](genre) , [family](genre)
  • [adventure](genre) , [romance](genre) , [animation](genre) , [mystery](genre)

• lookup: genre examples: |

  • drama
  • crime
  • documentary
  • history
  • war

• intent: actors examples: |

  • [liamneeson](actor) , [johnturturro](actor) , [liamneeson](actor)
  • [jessicalange](actor)
  • [johnhurt](actor) [denzelwashington](actor)
  • [rheaperlman](actor)

• lookup: actor examples: |

  • john travolta
  • gene hackman
  • rene russo
  • sylvester stallone
  • mary steenburgen
  • george dzundza
  • christine cavanaugh
  • danny mann
  • susan sarandon
  • sean penn

• intent: directors examples: |

  • [kevin smith](director) , [frank marshall](director) , [carl franklin](director)
  • [david anspaugh](director) [iain softley](director)
  • [tom dicillo](director)

• lookup: director examples: |

  • carl franklin
  • john mctiernan
  • iain softley
  • christopher ashley
  • larry clark
  • tom dicillo
  • bryan spicer

• intent: keywords examples: |

  • [psycho path](keyword) [teenage girl](keywords) [kitchen](keyword) [world war ii](keywords)
  • [pilot](keyword) , [kingdom](keyword) , [chef](keyword)
  • [paris](keyword) [spy](keyword)
  • [revenge](keyword)

• lookup: keyword examples: |

  • stand-upcomedy
  • new york
  • sport
  • cops
  • gangster
  • detective
  • undercover
  • security camera
  • gold
  • birthday
  • mountain
  • airplane
  • hotel
  • fbi
  • law
  • bestfriend
  • village
  • magic
  • hostage
  • suicide
  • politics
  • prison
  • media
  • paris
  • cat
  • underdog
  • bride
  • taxi
  • engineer
  • hell
  • gentleman

i was expecting that if a code runs on the jupyter notebook it should be running on the rasa actions