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K.Tharmikan
TMP-23-105
Commits
eeb3cf8b
Commit
eeb3cf8b
authored
Nov 23, 2023
by
M.A. Miqdad Ali Riza
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Update Wishlist_create.py
parent
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Wishlist_create.py
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Wishlist_create.py
View file @
eeb3cf8b
from
main_imports
import
MDScreen
from
ProjectFiles.applibs
import
utils
import
warnings
from
kivy.core.audio
import
SoundLoader
import
pandas
as
pd
import
pandas
as
pd
import
numpy
as
np
import
numpy
as
np
from
sklearn.metrics.pairwise
import
cosine_similarity
from
sklearn.metrics.pairwise
import
cosine_similarity
# Load the song data
import
os
song_ratings
=
pd
.
read_csv
(
"song_ratings.csv"
)
warnings
.
filterwarnings
(
'ignore'
)
utils
.
load_kv
(
"Wish_list.kv"
)
# Create a user-song matrix
user_song_ratings
=
song_ratings
.
pivot_table
(
index
=
'user_id'
,
columns
=
'song_id'
,
values
=
'rating'
)
user_song_ratings
.
fillna
(
0
,
inplace
=
True
)
# Calculate the cosine similarity between songs
song_similarity
=
cosine_similarity
(
user_song_ratings
.
T
)
# Define a function to generate song recommendations for a user
class
Wish_list_Screen
(
MDScreen
):
def
generate_song_recommendations
(
user_id
,
num_recommendations
=
10
):
def
__init__
(
self
,
**
kwargs
):
if
user_id
not
in
user_song_ratings
.
index
:
super
(
Wish_list_Screen
,
self
)
.
__init__
(
**
kwargs
)
print
(
f
"User ID {user_id} not found."
)
return
[]
user_ratings
=
user_song_ratings
.
loc
[
user_id
]
.
values
def
btnA
(
self
):
song_scores
=
np
.
dot
(
user_ratings
,
song_similarity
)
/
np
.
sum
(
np
.
abs
(
song_similarity
),
axis
=
1
)
recommended_song_indices
=
np
.
argsort
(
-
song_scores
)[:
num_recommendations
]
recommended_song_ids
=
user_song_ratings
.
columns
[
recommended_song_indices
]
.
tolist
()
return
recommended_song_ids
# Example: Generate song recommendations for user 98549
user_id
=
98549
recommended_songs
=
generate_song_recommendations
(
user_id
,
num_recommendations
=
5
)
if
recommended_songs
:
class
RecommendationSystem
:
def
__init__
(
self
,
song_ratings_file
):
data
=
pd
.
read_csv
(
song_ratings_file
)
with
open
(
"mood_label.txt"
,
"r"
)
as
file
:
readdata
=
file
.
read
()
self
.
song_ratings
=
data
[
data
[
'types'
]
==
readdata
]
# self.song_ratings = pd.read_csv(song_ratings_file)
self
.
user_song_ratings
=
self
.
preprocess_data
()
def
preprocess_data
(
self
):
# Pivot the data to create a user-song matrix
user_song_ratings
=
self
.
song_ratings
.
pivot_table
(
index
=
'user_id'
,
columns
=
'song_id'
,
values
=
'rating'
)
user_song_ratings
.
fillna
(
0
,
inplace
=
True
)
return
user_song_ratings
def
content_based_recommendations
(
self
,
user_id
,
num_recommendations
=
10
):
user_songs
=
self
.
user_song_ratings
.
loc
[
user_id
]
recommended_songs
=
user_songs
.
sort_values
(
ascending
=
False
)
.
head
(
num_recommendations
)
return
recommended_songs
.
index
.
tolist
()
# Initialize the recommendation system
recommendation_system
=
RecommendationSystem
(
"song_ratings_new.csv"
)
user_ids
=
[
1
]
for
user_id
in
user_ids
:
# Get content-based recommendations for each user
recommended_songs
=
recommendation_system
.
content_based_recommendations
(
user_id
,
num_recommendations
=
5
)
if
recommended_songs
:
print
(
f
"Recommended songs for user {user_id}:"
)
print
(
f
"Recommended songs for user {user_id}:"
)
print
(
recommended_songs
)
print
(
recommended_songs
)
else
:
self
.
l1
.
text
=
str
(
recommended_songs
)
print
(
"No recommendations available for this user."
)
else
:
print
(
f
"No recommendations available for user {user_id}"
)
self
.
ids
.
play
.
opacity
=
1
self
.
ids
.
pause
.
opacity
=
1
self
.
ids
.
next
.
opacity
=
1
self
.
ids
.
previous
.
opacity
=
1
self
.
ids
.
current_audio_label
.
opacity
=
1
audio_files
=
recommended_songs
static_part
=
'wishlistData/'
self
.
audio_files
=
[
static_part
+
file
+
'.mp3'
for
file
in
audio_files
]
file_path
=
'audio_files.txt'
# Open the file in write mode and write the audio files
with
open
(
file_path
,
'w'
)
as
file
:
for
item
in
self
.
audio_files
:
file
.
write
(
"
%
s
\n
"
%
item
)
self
.
current_audio_index
=
0
self
.
sound
=
None
if
self
.
current_audio_index
<
len
(
self
.
audio_files
):
self
.
sound
=
SoundLoader
.
load
(
self
.
audio_files
[
self
.
current_audio_index
])
def
play_audio
(
self
):
if
self
.
sound
and
self
.
sound
.
state
==
'stop'
:
self
.
sound
.
play
()
self
.
ids
.
current_audio_label
.
text
=
f
"Current Audio: {self.audio_files[self.current_audio_index]}"
def
play_next_audio
(
self
):
if
self
.
sound
:
self
.
sound
.
stop
()
self
.
current_audio_index
+=
1
if
self
.
current_audio_index
>=
len
(
self
.
audio_files
):
self
.
current_audio_index
=
0
self
.
sound
.
unload
()
self
.
sound
=
SoundLoader
.
load
(
self
.
audio_files
[
self
.
current_audio_index
])
self
.
sound
.
play
()
self
.
ids
.
current_audio_label
.
text
=
f
"Current Audio: {self.audio_files[self.current_audio_index]}"
def
pause_audio
(
self
):
if
self
.
sound
and
self
.
sound
.
state
==
'play'
:
self
.
sound
.
stop
()
def
play_previous_audio
(
self
):
if
self
.
sound
:
self
.
sound
.
stop
()
self
.
current_audio_index
-=
1
if
self
.
current_audio_index
<
0
:
self
.
current_audio_index
=
len
(
self
.
audio_files
)
-
1
self
.
sound
.
unload
()
self
.
sound
=
SoundLoader
.
load
(
self
.
audio_files
[
self
.
current_audio_index
])
self
.
sound
.
play
()
self
.
ids
.
current_audio_label
.
text
=
f
"Current Audio: {self.audio_files[self.current_audio_index]}"
\ No newline at end of file
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