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| 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. | |
| 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. | |
| 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'<h1 style="font-size:24px;">{"Recommened list of songs"}</h1>', 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]) | |