109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
import pandas as pd
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import re
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import os
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.metrics.pairwise import cosine_similarity
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import numpy as np
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class LoadData:
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def __init__(self):
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self.data = None
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self.loaded_datasets = []
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def load_data(self):
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self.create_data()
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self.clean_data()
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num_rows = self.data.shape[0]
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print(f'{num_rows} titles loaded successfully.')
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return self.data
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def clean_text(self, text):
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if isinstance(text, str):
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cleaned = re.sub(r'[^\x00-\x7F]+', '', text)
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cleaned = cleaned.replace('#', '')
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cleaned = cleaned.replace('"', '')
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return cleaned.strip()
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return ''
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def load_dataset(self, dataset_path, stream):
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try:
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df = pd.read_csv(f'dataset/{dataset_path}')
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df['stream'] = stream
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if stream not in 'IMDB':
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df = df.drop(columns=['show_id', 'date_added', 'duration', 'rating'], errors='ignore')
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df = df.rename(columns={'listed_in': 'genres'})
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else:
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df = df.rename(columns={'releaseYear': 'release_year'})
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df = df.drop(columns=['numVotes', 'id','avaverageRating'], errors='ignore')
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self.loaded_datasets.append(stream)
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return df
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except FileNotFoundError:
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print(f'Warning: "{dataset_path}" not found. Skipping this dataset.')
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def create_data(self):
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print(f'Starting to read data ...')
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df_netflix = self.load_dataset('data_netflix.csv','Netflix')
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df_amazon = self.load_dataset('data_amazon.csv','Amazon')
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df_disney = self.load_dataset('data_disney.csv','Disney')
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df_imdb = self.load_dataset('data_imdb.csv','IMDB')
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dataframes = [df for df in [df_imdb, df_netflix, df_amazon, df_disney] if df is not None]
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if not dataframes:
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print("Error: No datasets loaded. Cannot create combined data.")
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return
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df_all = pd.concat(dataframes, ignore_index=True, sort=False)
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df_all = df_all.infer_objects(copy=False)
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self.data = df_all
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print(f'Data from {", ".join(self.loaded_datasets)} imported.')
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def clean_data(self):
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string_columns = self.data.select_dtypes(include=['object'])
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self.data[string_columns.columns] = string_columns.apply(lambda col: col.map(self.clean_text, na_action='ignore'))
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self.data = self.data[~self.data['title'].str.strip().isin(['', ':'])]
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print(f'Data cleaned')
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class UserData:
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def __init__(self):
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self.user_data = None
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def input(self):
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self.user_data = input("Which Movie or TV-Serie do you prefer: ")
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return self.user_data.lower()
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class Recommendations:
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def __init__(self):
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self.result = None
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def get_recommendations(self, user_data, title_data):
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if title_data is not None and not title_data.empty:
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self.results = "Här ska de komma rekommendationer"
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print(self.results)
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else:
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print("No data available to search.")
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def main():
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data_loader = LoadData()
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title_data = data_loader.load_data()
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user_data = UserData()
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user_input = user_data.input()
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recommendations = Recommendations()
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recommendations.get_recommendations(user_data, title_data)
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if __name__ == "__main__":
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main() |