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spotify_music_recommender.py
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spotify_music_recommender.py
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import numpy as np
import pandas as pd
import openai
import spotipy
import pickle
import os
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.metrics import euclidean_distances
from scipy.spatial.distance import cdist
from spotipy.oauth2 import SpotifyClientCredentials
from collections import defaultdict
import warnings
warnings.filterwarnings("ignore")
def feature_get_pipeline_data_column_names():
"""
Reads data from a CSV file, performs K-means clustering on numeric columns,
and assigns cluster labels to the data.
Returns:
- song_cluster_pipeline: Pipeline object containing the scaler and K-means model.
- data: DataFrame with the original data and cluster labels.
- feature_column_names: List of column names containing numeric values.
"""
data = pd.read_csv("data/data.csv")
song_cluster_pipeline = Pipeline([('scaler', StandardScaler()),
('kmeans', KMeans(n_clusters=20,
verbose=False))
], verbose=False)
X = data.select_dtypes(np.number)
feature_column_names = list(X.columns)
song_cluster_pipeline.fit(X)
song_cluster_labels = song_cluster_pipeline.predict(X)
data['cluster_label'] = song_cluster_labels
return song_cluster_pipeline, data, feature_column_names
def get_model_values(data_path, file_path, cluster_path):
with open(file_path, 'rb') as file:
loaded_pipeline = pickle.load(file)
data = pd.read_csv(data_path)
labels = pd.read_csv(cluster_path)
data["cluster_label"] = labels["cluster_label"]
feature_column_names = ['valence', 'year', 'acousticness', 'danceability', 'duration_ms', 'energy', 'explicit',
'instrumentalness', 'key', 'liveness', 'loudness', 'mode', 'popularity', 'speechiness', 'tempo']
return loaded_pipeline, data, feature_column_names
def find_song(name, year):
"""
Finds a song on Spotify based on the song name and year.
Args:
- name: Name of the song.
- year: Year of the song.
Returns:
- DataFrame containing the song's data.
"""
if os.path.isfile(".\secret_keys.py"):
import secret_keys
sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(
client_id=secret_keys.client_id, client_secret=secret_keys.client_secret))
else:
sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(
client_id=os.environ.get("client_id"), client_secret=os.environ.get("client_secret")))
song_data = defaultdict()
results = sp.search(q='track: {} year: {}'.format(name, year), limit=1)
if results['tracks']['items'] == []:
return None
results = results['tracks']['items'][0]
track_id = results['id']
audio_features = sp.audio_features(track_id)[0]
song_data['name'] = [name]
song_data['year'] = [year]
song_data['explicit'] = [int(results['explicit'])]
song_data['duration_ms'] = [results['duration_ms']]
song_data['popularity'] = [results['popularity']]
for key, value in audio_features.items():
song_data[key] = value
return pd.DataFrame(song_data)
def find_song_uri(name, year):
"""
Finds the Spotify URI of a song based on the song name and year.
Args:
- name: Name of the song.
- year: Year of the song.
Returns:
- Spotify URI of the song.
"""
# Create a Spotify client object.
if os.path.isfile(".\secret_keys.py"):
import secret_keys
client = spotipy.Spotify(auth_manager=SpotifyClientCredentials(
client_id=secret_keys.client_id, client_secret=secret_keys.client_secret))
else:
client = spotipy.Spotify(auth_manager=SpotifyClientCredentials(
client_id=os.environ.get("client_id"), client_secret=os.environ.get("client_secret")))
results = client.search(q='track: {} year: {}'.format(name, year), limit=1)
track = results['tracks']['items'][0]
song_id = track['uri']
return song_id
def get_response(text):
"""
Retrieves a response using OpenAI's GPT-3 language model.
Args:
- input_text: The input text for the model.
Returns:
- Generated response as a string.
"""
if os.path.isfile(".\secret_keys.py"):
import secret_keys
openai.api_key = secret_keys.openai_api_key
else:
openai.api_key = os.environ.get("openai_api_key")
response = openai.Completion.create(
model="text-davinci-003",
prompt=text,
temperature=0.7,
max_tokens=128,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)
return response.choices[0].get("text")
def get_finetune_text(user_critic, list_song_data):
init_text = "I want you to act as a song recommender. I will provide you songs data with following format future_columns=[ <valence>, <published_year>, <acousticness>, <danceability>, <duration_ms>, <energy>, <explicit>,<instrumentalness>, <key>, <liveness>, <loudness>, <mode>, <popularity>, <speechiness>, <tempo>] \
values and user critic about the given song. And you will change given array values based on user critic and return result array. Do not write any explanations or other words, just return an array that include changes in future_columns\
and here is the np.describe() values of future_columns \n\
valence year acousticness danceability duration_ms energy explicit instrumentalness key liveness loudness mode popularity speechiness tempo \n \
count 170653 170653 170653 170653 170653 170653 170653 170653 170653 170653 170653 170653 170653 170653 170653 \n \
mean 0.528587211 1976.787241 0.502114764 0.537395535 230948.3107 0.482388835 0.084575132 0.167009581 5.199844128 0.205838655 -11.46799004 0.706902311 31.43179434 0.098393262 116.8615896 \n \
std 0.263171464 25.91785256 0.376031725 0.176137736 126118.4147 0.267645705 0.278249228 0.313474674 3.515093906 0.174804661 5.697942912 0.455184191 21.82661514 0.162740072 30.70853304 \n \
min 0 1921 0 0 5108 0 0 0 0 0 -60 0 0 0 0 \n \
25% 0.317 1956 0.102 0.415 169827 0.255 0 0 2 0.0988 -14.615 0 11 0.0349 93.421 \n \
50% 0.54 1977 0.516 0.548 207467 0.471 0 0.000216 5 0.136 -10.58 1 33 0.045 114.729 \n \
75% 0.747 1999 0.893 0.668 262400 0.703 0 0.102 8 0.261 -7.183 1 48 0.0756 135.537 \n \
max 1 2020 0.996 0.988 5403500 1 1 1 11 1 3.855 1 100 0.97 243.507"
# init_last = "\n\n start with only typing random future_columns values in given range as a array"
# user_critic_example = "\n \"user_critic=it was too old and loud but i like the energy\" "
# example_features = "future_columns=[0.68, 1976, 0.78, 0.62, 230948.3, 0.44, 0.22, 0.43, 5.2, 0.27, -9.67, 1, 31, 0.19, 118.86]"
# test_input = init_text + user_last + user_critic + example_features + user_critic_last
user_critic_last = "your output will be future_columns=[ <valence>, <published_year>, <acousticness>, <danceability>, <duration_ms>, <energy>, <explicit>,<instrumentalness>, <key>, <liveness>, <loudness>, <mode>, <popularity>, <speechiness>, <tempo>] format"
user_last = "\n\n start with the adjust following future_columns based on user_critic. "
features = "future_columns=" + list_song_data
real_input = init_text + user_last + \
user_critic + features + user_critic_last
return real_input
def format_gpt_output(raw_recommendation_array):
formatted = raw_recommendation_array[3:-1].split(",")
list_song_data = [float(i) for i in formatted]
return list_song_data
def format_song_string(song_data, feature_column_names):
list_song_data = song_data[feature_column_names].values.tolist()[0]
list_song_data = '[' + ', '.join([str(num)
for num in list_song_data]) + ']'
return list_song_data
def format_chatgpt_recommendations(song_list, spotify_data, song_cluster_pipeline, n_songs=15):
"""
Recommends a song using OpenAI's GPT-3 language model.
Args:
- song_name: The name of the song.
- song_year: The year of the song.
Returns:
- Recommended song as a list of string.
"""
feature_column_names = ['valence', 'year', 'acousticness', 'danceability', 'duration_ms', 'energy', 'explicit',
'instrumentalness', 'key', 'liveness', 'loudness', 'mode', 'popularity', 'speechiness', 'tempo']
metadata_cols = ['name', 'year', 'artists']
song_center = np.array(song_list)
scaler = song_cluster_pipeline.steps[0][1]
scaled_data = scaler.transform(spotify_data[feature_column_names])
scaled_song_center = scaler.transform(song_center.reshape(1, -1))
distances = cdist(scaled_song_center, scaled_data, 'cosine')
index = list(np.argsort(distances)[:, :n_songs][0])
rec_songs = spotify_data.iloc[index]
# rec_songs = rec_songs[~rec_songs['name'].isin(song_dict['name'])]
return rec_songs[metadata_cols].to_dict(orient='records')
def get_recommendation_song_uri(res):
song_spotipy_info = []
for song in res:
song_spotipy_info.append(find_song_uri(song["name"], song["year"]))
return song_spotipy_info
def get_recommendation_array(song_name, song_year, feature_column_names, user_critic_text):
song_data = find_song(song_name, song_year)
list_song_data = format_song_string(song_data, feature_column_names)
user_critic = "\n \"user_critic=" + user_critic_text
recommendation = get_response(get_finetune_text(user_critic, list_song_data))
raw_recommendation_array = format_gpt_output(recommendation)
return raw_recommendation_array
def get_random_song():
data = pd.read_csv("data/data.csv")
sample = data.sample(n=1)
return sample.name, sample.year
def control():
# song_cluster_pipeline, data, feature_column_names = feature_get_pipeline_data_column_names()
data_path = "data/data.csv"
file_path = "data/pipeline.pkl"
cluster_labels = "data/cluster_labels.csv"
song_cluster_pipeline, data, feature_column_names = get_model_values(
data_path, file_path, cluster_labels)
user_critic_text = "it was dull and very loud"
song_name = "Poem of a Killer"
song_year = 2022
raw_recommendation_array = get_recommendation_array(
song_name, song_year, feature_column_names, user_critic_text)
result = format_chatgpt_recommendations(raw_recommendation_array, data, song_cluster_pipeline)
print(result, get_recommendation_song_uri(result))
if __name__ == "__main__":
control()