Merge pull request #1 from jwradhe/Projekt

Projekt

README.md

@@ -1,3 +1,11 @@
+<<<<<<< HEAD
+# Python_AI_Projekt
+Projekt för Pythonkurs (AI)
+
+
+Autoencoders
+Scikit-learn: - K-nearest neighbors
+=======
 # Supervised Learning - Movie/TV-Show recommender
 
 ## Specification
@@ -51,3 +59,4 @@ I will use k-Nearest Neighbors (k-NN) alhorithm that can help me find other titl
      * get_recommendations 
 
 
+>>>>>>> b992895c329b4c147193b68b17c8181bb17fb220

main.py

@@ -0,0 +1,158 @@
+import pandas as pd
+import re
+import os
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+class LoadData:
+
+    def __init__(self):
+        self.data_file = 'data_movies_series.csv'
+        self.data = None
+        self.loaded_datasets = []
+
+    def check_data(self):
+        if os.path.isfile(self.data_file):
+            self.load_data()
+            return self.data
+        else:
+            self.create_data() 
+            if self.data is not None and not self.data.empty:
+                self.clean_data()
+                self.save_data()
+                num_rows = self.data.shape[0]
+                print(f'{num_rows} titles loaded successfully.')
+                return self.data
+            else:
+                print("Error: No data was created. Please check the dataset files.")
+                return None 
+
+    def clean_text(self, text):
+        if isinstance(text, str):
+            cleaned = re.sub(r'[^\x00-\x7F]+', '', text)
+            cleaned = cleaned.replace('#', '')
+            cleaned = cleaned.replace('"', '')
+            return cleaned.strip()
+        return '' 
+  
+    def clean_data(self):
+        string_columns = self.data.select_dtypes(include=['object'])
+        self.data[string_columns.columns] = string_columns.apply(lambda col: col.map(self.clean_text, na_action='ignore'))
+        self.data = self.data[~self.data['title'].str.strip().isin(['', ':'])]
+        print(f'Data cleaned successfully.')
+
+    def load_dataset(self, dataset_path, stream):
+        print(f'dataset/{dataset_path}')
+        try:
+            df = pd.read_csv(f'dataset/{dataset_path}')
+            df['stream'] = stream
+            if stream not in 'IMDB':
+                df = df.drop(columns=['show_id', 'date_added', 'duration', 'rating'], errors='ignore')
+                df = df.rename(columns={'listed_in': 'genres'})
+            else:
+                df = df.rename(columns={'releaseYear': 'release_year'})
+                df = df.drop(columns=['numVotes', 'id','avaverageRating'], errors='ignore')
+            self.loaded_datasets.append(stream)
+            return df
+        except FileNotFoundError:
+            print(f'Warning: "{dataset_path}" not found. Skipping this dataset.')
+
+    def create_data(self):
+        print(f'Starting to read data ...')
+        
+        df_netflix = self.load_dataset('data_netflix.csv','Netflix')
+        df_amazon = self.load_dataset('data_amazon.csv','Amazon')
+        df_disney = self.load_dataset('data_disney.csv','Disney')
+        df_imdb = self.load_dataset('data_imdb.csv','IMDB')
+
+        dataframes = [df for df in [df_imdb, df_netflix, df_amazon, df_disney] if df is not None]
+        if not dataframes:
+            print("Error: No datasets loaded. Cannot create combined data.")
+            return
+
+        df_all = pd.concat(dataframes, ignore_index=True, sort=False)
+        df_all = df_all.infer_objects(copy=False)
+        self.data = df_all
+
+        print(f'Data from {", ".join(self.loaded_datasets)} loaded successfully.')
+
+    def save_data(self):
+        self.data.to_csv(self.data_file, index=False)
+        print(f'Data saved to {self.data_file} successfully.')
+
+    def load_data(self):
+        self.data = pd.read_csv(self.data_file)
+        num_rows = self.data.shape[0]
+        print(f'{num_rows} titles loaded successfully.')
+
+
+class UserData:
+
+    def __init__(self):
+        self.user_data = None
+
+    def input(self):
+        self.user_data = input("Which Movie or TV-Serie do you prefer: ")
+        return self.user_data.lower()
+
+
+class Search:
+
+    def __init__(self, data):
+        self.data = data
+        self.preprocess()
+
+    def preprocess(self):
+        self.description_vectorizer = TfidfVectorizer(stop_words='english')
+        self.description_matrix = self.description_vectorizer.fit_transform(self.data['description'].fillna(''))
+        
+        self.onehot_encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)
+        genres_type_matrix = self.onehot_encoder.fit_transform(self.data[['genres', 'type']].fillna(''))
+
+        self.feature_matrix = np.hstack([
+            self.description_matrix.toarray(),
+            genres_type_matrix,
+            self.data[['release_year']].fillna(0).to_numpy()
+        ])
+
+    def search(self, query, top_n=20):
+        query_vec = self.description_vectorizer.transform([query])
+        
+        if hasattr(query_vec, "toarray"):
+            query_vec = query_vec.toarray()
+        
+        similarity = cosine_similarity(query_vec, self.description_matrix).flatten()
+
+        top_indices = similarity.argsort()[-top_n:][::-1]
+        return self.data.iloc[top_indices][['title', 'genres', 'type', 'release_year', 'stream','description']]
+
+
+class Recommendations:
+
+    def __init__(self):
+        self.result = None
+
+    def get_recommendations(self, user_data, title_data):
+        if title_data is not None and not title_data.empty:     
+            search_data = Search(title_data)
+            self.results = search_data.search(user_data)
+            print(self.results)
+        else:
+            print("No data available to search.")
+
+
+def main():
+
+    data_loader = LoadData()
+    title_data = data_loader.check_data()
+
+    user_data = UserData()
+    user_input = user_data.input()
+
+    recommendations = Recommendations()
+    recommendations.get_recommendations(user_data, title_data)
+
+if __name__ == "__main__":
+    main()