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RandomForestROSRID.py
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RandomForestROSRID.py
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import os
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import tkinter as tk
from sklearn.ensemble import RandomForestRegressor
from tkinter import messagebox
import random
#spotify api
client_id = '--------------------' //I explained what should be written here at readme file.
client_secret = '---------------------'//I explained what should be written here at readme file.
#spotify oturum açma
client_credentials_manager = SpotifyClientCredentials(client_id=client_id, client_secret=client_secret)
sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)
#dataset
dataset_folder = 'MillionSongSubset'
data_path = os.path.join(os.getcwd(), dataset_folder)
#şarkı özelliklerini alma fonk
def get_features_and_target(tracks):
features = []
target = []
for track in tracks:
track_id = track['track']['id']
audio_features = sp.audio_features(track_id)[0]
if audio_features is not None:
feature_values = [
audio_features['danceability'],
audio_features['energy'],
audio_features['speechiness'],
audio_features['acousticness'],
audio_features['instrumentalness'],
audio_features['liveness'],
audio_features['valence'],
audio_features['tempo']
]
features.append(feature_values)
#hedef deger popularity
if 'popularity' in audio_features:
target_value = audio_features['popularity']
target.append(target_value)
else:
target.append(0) #olmayan değerler için 0
return features, target
#şarkı öneren fonksiyon
def recommend_song(playlist_id):
#çalma listesi şarkıları
results = sp.playlist_items(playlist_id)
tracks = results['items']
#şarkı özelliklerini ve hedef değeri alıyoruz
features, target = get_features_and_target(tracks)
#modelimiz:
model = RandomForestRegressor()
model.fit(features, target)
#çalma listesinde olmayan ve benzerlik açısından en yakın şarkıları bulmamız lazım
recommended_songs = []
while len(recommended_songs) < 3:
recommended_song_index = random.randint(0, len(features)-1)
all_results = sp.search(q='year:2023', type='track', limit=50)
all_tracks = all_results['tracks']['items']
playlist_track_ids = [track['track']['id'] for track in tracks]
all_tracks = [track for track in all_tracks if track['id'] not in playlist_track_ids]
#benzerlik açısından en yakın şarkı
closest_song = find_closest_song(all_tracks, features, recommended_song_index)
recommended_song = closest_song['name']
if recommended_song not in recommended_songs:
recommended_songs.append(recommended_song)
return recommended_songs
#veri setindeki şarkılar arasından en çok benzeyen en yakınşarkı bulma fonk
def find_closest_song(tracks, features, recommended_song_index):
closest_song = None
closest_distance = float('inf')
for track in tracks:
track_features = get_track_features(track)
distance = calculate_distance(features[recommended_song_index], track_features)
if distance < closest_distance:
closest_distance = distance
closest_song = track
return closest_song
#şarkı özelliklerini liste olarak veren fonk
def get_track_features(track):
track_id = track['id']
audio_features = sp.audio_features(track_id)[0]
feature_values = [
audio_features['danceability'],
audio_features['energy'],
audio_features['speechiness'],
audio_features['acousticness'],
audio_features['instrumentalness'],
audio_features['liveness'],
audio_features['valence'],
audio_features['tempo']
]
return feature_values
#iki şarkı arasındaki özellik benzerliği
def calculate_distance(features1, features2):
distance = sum(abs(f1 - f2) for f1, f2 in zip(features1, features2))
return distance
#tkinter
def create_app():
def on_recommend():
playlist_id = playlist_id_entry.get()
recommended_songs = recommend_song(playlist_id)
recommendation_text = '\n'.join([f'{i+1}. {song}' for i, song in enumerate(recommended_songs)])
messagebox.showinfo('Önerilen Şarkılar', recommendation_text)
#asıl pencere
window = tk.Tk()
window.title('ROSRID - Spotify Şarkı Önerici')
window.geometry('500x100')
window.iconbitmap('music.ico')
#playlist için kutu
playlist_id_label = tk.Label(window, text='Çalma Listesi Kimliği:', font=('Arial', 12))
playlist_id_label.pack()
playlist_id_entry = tk.Entry(window, font=('Arial', 12))
playlist_id_entry.pack()
#buton
recommend_button = tk.Button(window, text='Öner', command=on_recommend, font=('Arial', 12))
recommend_button.pack()
window.mainloop()
create_app()