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from flask import Flask, render_template, request, redirect, url_for
import os
import librosa
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
import pickle
import subprocess # Untuk menjalankan perintah FFmpeg
import threading # Untuk menjalankan penghapusan otomatis file setelah delay
app = Flask(__name__)
# Path folder untuk menyimpan file yang diunggah
UPLOAD_FOLDER = 'static/uploads'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
# Load model dan scaler
model = tf.keras.models.load_model('model_code1.keras')
with open('scaler.pkl', 'rb') as f:
scaler = pickle.load(f)
# Label genre musik
genres = ['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock']
# Fungsi untuk menghapus file setelah delay
def delete_file_after_delay(file_path, delay=3600):
def delete_file():
try:
if os.path.exists(file_path):
os.remove(file_path)
print(f"File {file_path} berhasil dihapus setelah {delay} detik.")
except Exception as e:
print(f"Gagal menghapus file {file_path}: {e}")
# Jalankan penghapusan file dalam thread baru
threading.Timer(delay, delete_file).start()
# Fungsi untuk mengonversi MP3 ke WAV menggunakan FFmpeg
def convert_mp3_to_wav(mp3_path):
wav_path = mp3_path.replace('.mp3', '.wav') # Ubah ekstensi ke .wav
try:
# Jalankan perintah FFmpeg untuk konversi
subprocess.run(['ffmpeg', '-i', mp3_path, wav_path], check=True)
# Hapus file MP3 setelah berhasil dikonversi
os.remove(mp3_path)
return wav_path
except subprocess.CalledProcessError as e:
print(f"Error converting MP3 to WAV: {e}")
return None
except OSError as e:
print(f"Error deleting MP3 file: {e}")
return None
# Fungsi untuk ekstraksi fitur dari file musik
def extract_features(file_path):
try:
y, sr = librosa.load(file_path, duration=30, sr=22050)
# Ekstraksi fitur
mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13).T, axis=0)
chroma = np.mean(librosa.feature.chroma_stft(y=y, sr=sr).T, axis=0)
spectral_contrast = np.mean(librosa.feature.spectral_contrast(y=y, sr=sr).T, axis=0)
zero_crossings = np.mean(librosa.feature.zero_crossing_rate(y).T, axis=0)
tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
# Menggabungkan semua fitur
features = np.hstack([mfccs, chroma, spectral_contrast, zero_crossings, tempo])
return features
except Exception as e:
print(f"Error extracting features: {e}")
return None
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
# Periksa apakah file diunggah
if 'file' not in request.files:
return redirect(request.url)
file = request.files['file']
if file.filename == '':
return redirect(request.url)
# Simpan file ke folder yang ditentukan
file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
file.save(file_path)
# Periksa format file
if file.filename.lower().endswith('.mp3'):
# Konversi MP3 ke WAV
file_path_wav = convert_mp3_to_wav(file_path)
if file_path_wav is None:
return "Konversi MP3 ke WAV gagal. Pastikan file yang diunggah valid."
file_path = file_path_wav # Gunakan file WAV untuk proses berikutnya
# Ekstraksi fitur dari file yang diunggah
features = extract_features(file_path)
if features is None:
return "Ekstraksi fitur gagal. Coba unggah file lain."
# Normalisasi fitur menggunakan scaler
features_scaled = scaler.transform([features])
# Prediksi genre menggunakan model
prediction = model.predict(features_scaled)
predicted_genre = genres[np.argmax(prediction)]
# Hapus file WAV setelah 30 detik
delete_file_after_delay(file_path, delay=30)
# Kembalikan hasil prediksi
return render_template('index.html', file_path=file_path, prediction=predicted_genre)
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True) |