import streamlit as st import math import numpy as np import psycopg2 from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split import pandas as pd from scipy.spatial import distance import random # Initialize connection. # Uses st.experimental_singleton to only run once. @st.experimental_singleton def init_connection(): return psycopg2.connect(**st.secrets["postgres"]) list_selected_songs = [] list_allsongs = [] emotion = [] q1_song = [] q2_song = [] q3_song = [] q4_song = [] conn = init_connection() # number of recommendation of the song to be made num_recommendation = 10 # Perform query. # Uses st.experimental_memo to only rerun when the query changes or after 10 min. @st.experimental_memo(ttl=600) def run_query(query): with conn.cursor() as cur: cur.execute(query) return cur.fetchall() def count_emotion(list_emotion): s1 = 'q1' s2 = 'q2' s3 = 'q3' s4 = 'q4' global count_q1 global count_q2 global count_q3 global count_q4 count_q1 = list_emotion.count(s1) count_q2 = list_emotion.count(s2) count_q3 = list_emotion.count(s3) count_q4 = list_emotion.count(s4) def recommended_songs_q1_q2_q3_q4(num_recommendation_q1, num_recommendation_q2, num_recommendation_q3, num_recommendation_q4): global q1 global q2 global q3 global g4 q1 = np.array(q1_song[0:int(num_recommendation_q1)]) q2 = np.array(q2_song[0:int(num_recommendation_q2)]) q3 = np.array(q3_song[0:int(num_recommendation_q3)]) q4 = np.array(q4_song[0:int(num_recommendation_q4)]) return q1, q2, q3, q4 def get_number_quadrant_recommendation(): total = count_q1+count_q2+count_q3+count_q4 percentage_q1 = count_q1/total percentage_q2 = count_q2/total percentage_q3 = count_q3/total percentage_q4 = count_q4/total global num_recommendation_q1 global num_recommendation_q2 global num_recommendation_q3 global num_recommendation_q4 num_recommendation_q1 = (percentage_q1*num_recommendation) num_recommendation_q2 = (percentage_q2*num_recommendation) num_recommendation_q3 = (percentage_q3*num_recommendation) num_recommendation_q4 = (percentage_q4*num_recommendation) def get_distance(e): # index 6 consists of euclidian distance between two songs, seleted song and song in the database return e[7] def get_distance1(e): # index 0 consists of euclidian distance between two songs in the case of the repeated song return e[0] rows = run_query("SELECT song_title, artist from nepali_songs order by song_title") st.title('Emotion Based Music Recommendation System') st.image('music.jpg') with st.form("my_form"): options = st.multiselect('Choose the song that you want to listen', [ ':'.join(map(str, x)) for x in rows]) submitted = st.form_submit_button("Recommend song to me") # splitting artist and song title of the song that user have selected if submitted: artist_song_list = [] for index, element in enumerate(options): artist_list = element.split(':') artist_song_list.append(artist_list) # storing the information of the audio features of the songs that the user have selected by retrieving through the database for x in artist_song_list: with conn.cursor() as cur: cur.execute( "select * from nepali_songs where song_title=%s and artist=%s", [x[0], x[1]]) row = cur.fetchall() list_selected_songs.append(row) print("selected songs") happy_songs=[] tensed_songs=[] sad_songs=[] calm_songs=[] # collecting the information about the emotion quadrant of the each song that the user have selected and storing in the list emotion for i in list_selected_songs: print(i[0][12],i[0][13]) emotion.append(i[0][14]) #categorizing the songs into 4 different emotions if(i[0][14]=='q1'): happy_songs.append(i[0][12]) elif(i[0][14]=='q2'): tensed_songs.append(i[0][12]) elif(i[0][14]=='q3'): sad_songs.append(i[0][12]) else: calm_songs.append(i[0][12]) # calculating the total number of emotion quadrant of the song that the user have selected count_emotion(emotion) total = count_q1+count_q2+count_q3+count_q4 st.write("You have selected ", count_q1, " happy songs") st.write(happy_songs) st.write("You have selected ", count_q2, " tensed songs") st.write(tensed_songs) st.write("You have selected ", count_q3, " sad songs") st.write(sad_songs) st.write("You have selected ", count_q4, " calm songs") st.write(calm_songs) # retriving information of all songs in the database and storing in the list list_allsongs distance_info_song_all = [] with conn.cursor() as cur: cur.execute("select * from nepali_songs") row = cur.fetchall() # print(type(row[0])) list_allsongs.append(row) # calculating the euclidean distance of selected songs with all songs in the database for i in list_selected_songs: valence_selected_songs = float(i[0][11]) energy_selected_songs = float(i[0][2]) list1 = [valence_selected_songs, energy_selected_songs] for lists in list_allsongs: # print("printing the list of songs\n") distance_info = [] for tuples in lists: valence_allsongs = float(tuples[11]) energy_allsongs = float(tuples[2]) list2 = [valence_allsongs, energy_allsongs] dist = distance.euclidean(list1, list2) # i[0][0] is the spotifyid of the selected song and tuples[0] is the spotifyid # of the song in the database distance_info.append( [i[0][12], i[0][14], i[0][0], tuples[12], tuples[14], tuples[13], tuples[0], dist]) distance_info_song_all.append(distance_info) # st.write("distance info") # st.write(distance_info_song_all) nearest_neighbour = [] # distance_info_song_all consists of the euclidian distance of the selected song with all songs in the database # recommend_list consist of the top 10 songs having minimum distance with all the selected song for i in distance_info_song_all: i.sort(key=get_distance, reverse=False) #sorting the 10 nearest neighbour of the each selected song selected song for k in range(0,10): nearest_neighbour.append(i[k]) # st.write(nearest_neighbour) # calculating the percentange of song associated with q1, q2, q3 and q4 that the user have selected # st.write("q1 recommendation number", num_recommendation_q1) # st.write("q2 recommendation number", num_recommendation_q2) # st.write("q3 recommendation number", num_recommendation_q3) # st.write("q4 recommendation numner", num_recommendation_q4) # st.write(num_recommendation) # selecting the song of the particular quadrant from the nearest neighbour and storing in the list for t in nearest_neighbour: if (t[4] == 'q2' and t[7]!=0.0): q2_song.append(t) elif (t[4] == 'q1' and t[7]!=0.0): q1_song.append(t) elif (t[4] == 'q3' and t[7]!=0.0): q3_song.append(t) elif (t[4] == 'q4' and t[7]!=0.0): q4_song.append(t) else: pass q1_song.sort(key=get_distance,reverse=False) q2_song.sort(key=get_distance,reverse=False) q3_song.sort(key=get_distance,reverse=False) q4_song.sort(key=get_distance,reverse=False) get_number_quadrant_recommendation() q1, q2, q3, q4 = recommended_songs_q1_q2_q3_q4( num_recommendation_q1, num_recommendation_q2, num_recommendation_q3, num_recommendation_q4) # st.write("q1") # st.write(q1) # st.write("q2") # st.write(q2) # st.write("q3") # st.write(q3) # st.write("q4") # st.write(q4) w = set() x = set() y = set() z = set() for i in q1: w.add(i[6]) for j in q2: x.add(j[6]) for k in q3: y.add(k[6]) for l in q4: z.add(l[6]) # Checking whether the set w,x,z which consists of the unique spotifyid of the song in the q1, q2, q3, q4 consists of at least one element if len(w) != 0: # a set that consists of the spotify id of the repeated song spotifyid=set() #list consist of one copy of the repeated song with minimum distance q1_=[] for i in w: lst1=[] solutions=np.argwhere(q1==i) #if the spotify id ofthat songs consists in the q1 more than one time if(len(solutions)>1): for i in range(0,len(solutions)): # print(q1[solutions[i][0]][7]) # since that song is repeated more than once, collecing the info of the euclidian distance of that repeated songs in the lst1 lst1.append([q1[solutions[i][0]][7],q1[solutions[i][0]][0],q1[solutions[i][0]][1],q1[solutions[i][0]][3],q1[solutions[i][0]][2],q1[solutions[i][0]][5],q1[solutions[i][0]][4],q1[solutions[i][0]][6]]) spotifyid.add(q1[solutions[i][0]][6]) lst1.sort(key=get_distance1,reverse=False) q1_.append(lst1[0]) #excluding all the repeated songs for i in spotifyid: q1 = [item for item in q1 if item[6]!= i] #appending the only one copy of repeated song with minimum distance for i in q1_: q1.append(i) if len(x) != 0: # a set that consists of the spotify id of the repeated song spotifyid=set() q2_=[] for i in x: lst2=[] solutions=np.argwhere(q2==i) #if the spotify id ofthat songs consists in the q1 more than one time if(len(solutions)>1): for i in range(0,len(solutions)): # print(q1[solutions[i][0]][7]) # since that song is repeated more than once, collecing the info of the euclidian distance of that repeated songs in the lst1 lst2.append([q2[solutions[i][0]][7],q2[solutions[i][0]][0],q2[solutions[i][0]][1],q2[solutions[i][0]][3],q2[solutions[i][0]][2],q2[solutions[i][0]][5],q2[solutions[i][0]][4],q2[solutions[i][0]][6]]) spotifyid.add(q2[solutions[i][0]][6]) lst2.sort(key=get_distance1,reverse=False) q2_.append(lst2[0]) for i in spotifyid: q2 = [item for item in q1 if item[6]!= i] for i in q2_: q2.append(i) if len(y) != 0: # a set that consists of the spotify id of the repeated song spotifyid=set() q3_=[] for i in y: lst3=[] solutions=np.argwhere(q3==i) #if the spotify id ofthat songs consists in the q1 more than one time if(len(solutions)>1): for i in range(0,len(solutions)): # print(q1[solutions[i][0]][7]) # since that song is repeated more than once, collecing the info of the euclidian distance of that repeated songs in the lst1 lst3.append([q3[solutions[i][0]][7],q3[solutions[i][0]][0],q3[solutions[i][0]][1],q3[solutions[i][0]][3],q3[solutions[i][0]][2],q3[solutions[i][0]][5],q3[solutions[i][0]][4],q3[solutions[i][0]][6]]) spotifyid.add(q3[solutions[i][0]][6]) lst3.sort(key=get_distance1,reverse=False) q3_.append(lst3[0]) for i in spotifyid: q3 = [item for item in q3 if item[6]!= i] for i in q3_: q3.append(i) if len(z) != 0: # a set that consists of the spotify id of the repeated song spotifyid=set() q4_=[] for i in z: lst4=[] solutions=np.argwhere(q4==i) #if the spotify id ofthat songs consists in the q1 more than one time if(len(solutions)>1): for i in range(0,len(solutions)): # print(q1[solutions[i][0]][7]) # since that song is repeated more than once, collecing the info of the euclidian distance of that repeated songs in the lst1 lst4.append([q4[solutions[i][0]][7],q4[solutions[i][0]][0],q4[solutions[i][0]][1],q4[solutions[i][0]][3],q4[solutions[i][0]][2],q4[solutions[i][0]][5],q4[solutions[i][0]][4],q4[solutions[i][0]][6]]) spotifyid.add(q4[solutions[i][0]][6]) lst4.sort(key=get_distance1,reverse=False) q4_.append(lst4[0]) for i in spotifyid: q4 = [item for item in q4 if item[6]!= i] for i in q4_: q4.append(i) st.markdown(f'

{"Recommened list of songs"}

', unsafe_allow_html=True) print("Recommended list of songs") if(q1!=0): st.write("Happy Songs\n") for i in q1: print(i[3]," ",i[6]) st.write(i[3],":",i[5]) if(q2!=0): st.write("Tensed Songs\n") for j in q2: print(j[3]," ",j[6]) st.write(j[3],":",j[5]) if(q3!=0): st.write("Sad Songs\n") for k in q3: print(k[3]," ",k[6]) st.write(k[3],":",k[5]) if(q4!=0): st.write("Calm Songs\n") for l in q4: print(l[3]," ",l[6]) st.write(l[3],":",l[5])