Spaces:
Runtime error
Runtime error
File size: 6,344 Bytes
9dd7d9c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
!pip install python-dotenv
# imports
import pandas as pd
import h5py
import os
from sqlalchemy import create_engine
import requests
import time
from dotenv import load_dotenv
import pandas as pd
df = pd.read_csv('/content/drive/MyDrive/CMPE-258: Team Neurobytes/Neurobytes/db/data/music_data.csv')
df.dropna(inplace=True)
import pandas as pd
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from sklearn.model_selection import train_test_split
import torch.optim as optim
# Encode categorical data
label_encoders = {}
unknown_label = 'unknown' # Define an unknown label
for column in ['artist_name', 'tags', 'title']:
le = LabelEncoder()
# Get unique categories plus an 'unknown' category
unique_categories = df[column].unique().tolist()
# Add 'unknown' to the list of categories
unique_categories.append(unknown_label)
# Fit the LabelEncoder to these categories
le.fit(unique_categories)
df[column] = le.transform(df[column].astype(str))
# Store the encoder
label_encoders[column] = le
# Normalize numerical features
scaler = MinMaxScaler()
df[['duration', 'listeners', 'playcount']] = scaler.fit_transform(
df[['duration', 'listeners', 'playcount']])
# Split data into features and target
X = df[['artist_name', 'tags', 'duration', 'listeners', 'playcount']]
y = df['title']
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42)
class SongRecommender(nn.Module):
def __init__(self):
super(SongRecommender, self).__init__()
self.fc1 = nn.Linear(5, 128) # Adjust input features if needed
self.fc2 = nn.Linear(128, 256)
self.fc3 = nn.Linear(256, 128)
# Output size = number of unique titles including 'unknown'
# Add 1 for the 'unknown' label
self.output = nn.Linear(128, len(y.unique()) + 1)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = self.output(x)
return x
model = SongRecommender()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
def train_model(model, X_train, y_train, X_test, y_test):
train_loader = DataLoader(
list(zip(X_train.values.astype(float), y_train)), batch_size=50, shuffle=True)
test_loader = DataLoader(
list(zip(X_test.values.astype(float), y_test)), batch_size=50, shuffle=False)
model.train()
for epoch in range(50): # Number of epochs
train_loss = 0
for features, labels in train_loader:
optimizer.zero_grad()
outputs = model(torch.tensor(features).float())
# Ensure labels are long type
loss = criterion(outputs, torch.tensor(labels).long())
loss.backward()
optimizer.step()
train_loss += loss.item()
# Validation phase
model.eval()
validation_loss = 0
for features, labels in test_loader:
outputs = model(torch.tensor(features).float())
loss = criterion(outputs, torch.tensor(labels).long())
validation_loss += loss.item()
print(f'Epoch {epoch+1}, Training Loss: {train_loss / len(train_loader)}, Validation Loss: {validation_loss / len(test_loader)}')
train_model(model, X_train, y_train, X_test, y_test)
# save the model
torch.save(model.state_dict(), 'model.pth')
# load the model
model = SongRecommender()
def recommend_songs(model, input_features):
model.eval()
print(input_features)
with torch.no_grad():
try:
artist_index = label_encoders['artist_name'].transform(
[input_features['artist_name']])
except ValueError:
artist_index = label_encoders['artist_name'].transform(['unknown'])
try:
tags_index = label_encoders['tags'].transform(
[input_features['tags']])
except ValueError:
tags_index = label_encoders['tags'].transform(['unknown'])
# Create a DataFrame with feature names
scaled_features = pd.DataFrame(
[[input_features['duration'], input_features['listeners'],
input_features['playcount']]],
columns=['duration', 'listeners', 'playcount']
)
scaled_features = scaler.transform(scaled_features)[0]
features = torch.tensor(
[artist_index[0], tags_index[0], *scaled_features]).float().unsqueeze(0)
predictions = model(features)
top_5_values, top_5_indices = predictions.topk(5)
recommended_song_ids = top_5_indices.squeeze().tolist()
return label_encoders['title'].inverse_transform(recommended_song_ids)
import requests
def fetch_song_data(api_key, artist_name, track_name):
url = "http://ws.audioscrobbler.com/2.0/"
params = {
'method': 'track.getInfo',
'api_key': api_key,
'artist': artist_name,
'track': track_name,
'format': 'json'
}
response = requests.get(url, params=params)
print(response.content)
return response.json() if response.status_code == 200 else {}
def parse_song_data(song_data):
if song_data and 'track' in song_data:
track = song_data['track']
return {
'artist_name': track['artist']['name'],
'tags': ', '.join([tag['name'] for tag in track.get('toptags', {}).get('tag', [])]),
'duration': float(track.get('duration', 0)),
'listeners': int(track.get('listeners', 0)),
'playcount': int(track.get('playcount', 0)),
'album': track.get('album', {}).get('title', 'Unknown')
}
return {}
from dotenv import load_dotenv
import os
load_dotenv()
api_key = os.getenv('LASTFM_API_KEY')
artist_name = 'Lagy Gaga'
track_name = 'Poker Face'
# Fetch and parse song data
song_data = fetch_song_data(api_key, artist_name, track_name)
parsed_data = parse_song_data(song_data)
print(song_data)
# if the song is not found, or the tags column is empty, print a message
if not parsed_data or not parsed_data['tags']:
print("Song not found or tags not available.")
else:
recommend_songs(model, parsed_data) |