Create recomendation

recomendation

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+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")