Spaces:
Sleeping
Sleeping
File size: 4,379 Bytes
4a584a3 edb462b 4a584a3 c3c18c4 4a584a3 3629f8b edb462b 3629f8b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
import numpy as np
from vectorization import spotify_data
import json
import gradio as gr
from gradio.components import Textbox
from ast import literal_eval
spotify_data_processed = pd.read_csv('dataset_modificado.csv')
def convert_string_to_array(str_vector):
# Si str_vector ya es un array de NumPy, devolverlo directamente
if isinstance(str_vector, np.ndarray):
return str_vector
try:
cleaned_str = str_vector.replace('[', '').replace(']', '').replace('\n', ' ').replace('\r', '').strip()
vector_elements = [float(item) for item in cleaned_str.split()]
return np.array(vector_elements)
except ValueError as e:
print("Error:", e)
return np.zeros((100,))
spotify_data_processed['song_vector'] = spotify_data_processed['song_vector'].apply(convert_string_to_array)
# Aplicar la función a las primeras filas para ver los resultados
sample_data = spotify_data_processed['song_vector'].head()
converted_vectors = sample_data.apply(convert_string_to_array)
print(converted_vectors)
def recommend_song(song_name, artist_name, spotify_data_processed, top_n=4):
# Filtrar para encontrar la canción específica
specific_song = spotify_data_processed[(spotify_data_processed['song'] == song_name)
& (spotify_data_processed['artist'] == artist_name)]
# Verificar si la canción existe en el dataset
if specific_song.empty:
return pd.DataFrame({"Error": ["Canción no encontrada en la base de datos."]})
# Obtener el vector de la canción específica
song_vec = specific_song['song_vector'].iloc[0]
# Asegurarte de que song_vec sea un array de NumPy
if isinstance(song_vec, str):
song_vec = convert_string_to_array(song_vec)
all_song_vectors = np.array(spotify_data_processed['song_vector'].tolist())
# Calcular similitudes
similarities = cosine_similarity([song_vec], all_song_vectors)[0]
# Obtener los índices de las canciones más similares
top_indices = np.argsort(similarities)[::-1][1:top_n+1]
# Devolver los nombres y artistas de las canciones más similares
recommended_songs = spotify_data_processed.iloc[top_indices][['song', 'artist']]
return recommended_songs
def recommend_song_interface(song_name, artist_name):
recommendations_df = recommend_song(song_name, artist_name, spotify_data_processed)
# Verificar si el DataFrame está vacío o si las columnas necesarias están presentes
if isinstance(recommendations_df, pd.DataFrame) and not recommendations_df.empty and {'song', 'artist'}.issubset(recommendations_df.columns):
recommendations_list = recommendations_df[['song', 'artist']].values.tolist()
formatted_recommendations = ["{} by {}".format(song, artist) for song, artist in recommendations_list]
# Rellenar con cadenas vacías si hay menos de 4 recomendaciones
while len(formatted_recommendations) < 4:
formatted_recommendations.append("")
return formatted_recommendations[:4]
else:
random_song = spotify_data_processed.sample() # Escoge una linea la azar de todo el conjunto de datos .sample()
random_song_name = random_song['song'].iloc[0] # Extrae el valor de la columna song de la fila sample (Nombre)
random_artist_name = random_song['artist'].iloc[0] # Extrae el valor de la columna artist de la fila sample (Artista)
# Obtener recomendaciones para la canción aleatoria
random_recommendations_df = recommend_song(random_song_name, random_artist_name, spotify_data_processed)
random_recommendations_list = random_recommendations_df[['song', 'artist']].values.tolist()
formatted_random_recommendations = ["{} by {}".format(song, artist) for song, artist in random_recommendations_list]
# Rellenar con cadenas vacías si hay menos de 4 recomendaciones
while len(formatted_random_recommendations) < 4:
formatted_random_recommendations.append("")
return formatted_random_recommendations[:4]
# Ejemplo de uso
# Asegúrate de que spotify_data_processed es un DataFrame de Pandas válido con las columnas 'song' y 'artist'
recommendations = recommend_song_interface("song_name", "artist_name")
|