Update app.py

app.py

@@ -1,118 +1,124 @@
-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)
+from fastapi import FastAPI
+from asgiref.wsgi import WsgiToAsgi
+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
+from fastapi.middleware.wsgi import WSGIMiddleware
+
+flask_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
+
+
+@flask_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')
+
+
+# FastAPI app to mount Flask app
+app = FastAPI()
+
+# Mount Flask app inside FastAPI using WSGIMiddleware
+app.mount("/", WSGIMiddleware(flask_app))