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129 lines
4.1 KiB
129 lines
4.1 KiB
import csv
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import os
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from typing import List
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import numpy as np
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import pandas as pd
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from calculsIA import *
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def levenshtein_distance(s1, s2):
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if len(s1) < len(s2):
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return levenshtein_distance(s2, s1)
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if len(s2) == 0:
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return len(s1)
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previous_row = range(len(s2) + 1)
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for i, c1 in enumerate(s1):
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current_row = [i + 1]
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for j, c2 in enumerate(s2):
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insertions = previous_row[j + 1] + 1
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deletions = current_row[j] + 1
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substitutions = previous_row[j] + (c1 != c2)
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current_row.append(min(insertions, deletions, substitutions))
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previous_row = current_row
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return previous_row[-1]
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def find_closest_actor_name(input_name, actor_names):
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if input_name in actor_names:
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return input_name
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closest_name = None
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min_distance = float('inf')
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for i in range(len(actor_names)):
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actor_name = actor_names[i]
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distance = levenshtein_distance(input_name, actor_name)
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if distance < min_distance:
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min_distance = distance
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closest_name = actor_name
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return (closest_name)
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def getUniqueActorNames(filePath):
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# Lire le fichier TSV
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df = pd.read_csv(filePath, sep='\t')
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actor_names = df['primaryName'].tolist()
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return (actor_names)
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def saveUniqueActorsSorted(inputFilePath, outputFilePath):
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# Lire le fichier TSV
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df = pd.read_csv(inputFilePath, sep='\t')
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# Trier le DataFrame par 'primaryName' en ordre alphabétique
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df_sorted = df.sort_values(by='primaryName')
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# Écrire les données triées dans le fichier CSV
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with open(outputFilePath, mode='w', newline='', encoding='utf-8') as file:
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writer = csv.writer(file, delimiter='\t')
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# Écrire l'en-tête
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writer.writerow(['primaryName', 'nconst'])
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# Écrire chaque ligne du DataFrame trié dans le fichier CSV
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for index, row in df_sorted.iterrows():
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writer.writerow([row['primaryName'], row['nconst']])
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def ask_user_verification(actor_name):
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response = input(f"Est-ce que vous vouliez dire {actor_name}? (Oui/Non) ")
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return response.lower() in ['oui', 'o', 'yes', 'y']
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def add_actor(actor_names):
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user_input = input("Entrez le nom de l'acteur à ajouter : ")
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closest_name = find_closest_actor_name(user_input, actor_names)
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if ask_user_verification(closest_name):
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return closest_name
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else:
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user_choice = input("Voulez-vous réessayer avec un autre nom? (Oui/Non) ")
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if user_choice.lower() in ['oui', 'o', 'yes', 'y']:
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return add_actor(actor_names)
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else:
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return None
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def find_direct_actor_name(df, searchedValue):
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if 'primaryName' in df.columns:
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if searchedValue in df['primaryName'].values:
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return searchedValue
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return None
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if __name__ == "__main__":
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mustContinue = True
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if not os.path.exists("processedData/uniqueActorNames.tsv"):
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saveUniqueActorsSorted("processedData/actorsRatingsGroupedWithName.tsv", "processedData/uniqueActorNames.tsv")
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actor_names = getUniqueActorNames("processedData/uniqueActorNames.tsv")
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selectedActorNames = []
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print("Bienvenue dans MoviePrecog!")
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while mustContinue:
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print("1: Ajouter un acteur à la liste (4 acteurs / actrices requis)")
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print("2: Afficher la liste")
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print("3: Vider la liste")
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print("4: Lancer la prévision")
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print("5: Quitter")
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choice = input("Faites votre choix : ")
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if choice == '1':
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result = add_actor(actor_names)
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if result:
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selectedActorNames.append(result)
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print(f"{result} a été ajouté à la liste.")
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elif choice == '2':
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print("Liste des acteurs sélectionnés :")
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for actor in selectedActorNames:
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print(actor)
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elif choice == '3':
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selectedActorNames.clear()
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print("La liste a été vidée.")
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elif choice == '4':
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calculPrevision(selectedActorNames)
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elif choice == '5':
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print("Au revoir !")
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mustContinue = False
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else:
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print("Choix non valide, veuillez réessayer.")
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