Update app.py

app.py

@@ -1,34 +1,107 @@
 import gradio as gr
+from flask import Flask, request, jsonify
 from pydub import AudioSegment
-import base64
-import tempfile
 import numpy as np
-import io
-
-def process_audio_gr(audio):
-    if audio is None:
-        return None
-    # Load audio with pydub
-    audio_segment = AudioSegment.from_file(io.BytesIO(audio))
-    # Simple effect: normalize
-    normalized = audio_segment.normalize()
-    # Export to bytes
-    buf = io.BytesIO()
-    normalized.export(buf, format="wav")
-    buf.seek(0)
-    audio_bytes = buf.read()
-    # Convert to base64
-    audio_b64 = base64.b64encode(audio_bytes).decode()
-    data_uri = f"data:audio/wav;base64,{audio_b64}"
-    return data_uri
-
-iface = gr.Interface(
-    fn=process_audio_gr,
-    inputs=gr.Audio(source="upload", type="file"),
-    outputs=gr.Audio(type="auto"),
-    title="Audio Normalize Demo",
-    description="Upload audio, get normalized audio back."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import tempfile
+import os
+import noisereduce as nr
+import torch
+from demucs import pretrained
+from demucs.apply import apply_model
+import torchaudio
+import librosa
+import soundfile as sf
+import json
+import warnings
+
+warnings.filterwarnings("ignore")
+
+app = Flask(__name__)
+
+# Helper Functions
+def audiosegment_to_array(audio):
+    return np.array(audio.get_array_of_samples()), audio.frame_rate
+
+def array_to_audiosegment(samples, frame_rate, channels=1):
+    return AudioSegment(
+        samples.tobytes(),
+        frame_rate=int(frame_rate),
+        sample_width=samples.dtype.itemsize,
+        channels=channels
+    )
+
+def save_audiosegment_to_temp(audio, suffix=".wav"):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:
+        audio.export(f.name, format=suffix.lstrip('.'))
+        return f.name
+
+def load_audiofile_to_numpy(path):
+    samples, sr = sf.read(path, dtype="int16")
+    if samples.ndim > 1 and samples.shape[1] > 2:
+        samples = samples[:, :2]
+    return samples, sr
+
+# Effect Functions
+def apply_normalize(audio):
+    return audio.normalize()
+
+def apply_noise_reduction(audio):
+    samples, sr = audiosegment_to_array(audio)
+    reduced = nr.reduce_noise(y=samples, sr=sr)
+    return array_to_audiosegment(reduced, sr, channels=audio.channels)
+
+# Add other effect functions here...
+
+# API Endpoints
+@app.route('/process_audio', methods=['POST'])
+def process_audio_endpoint():
+    if 'audio' not in request.files:
+        return jsonify({'error': 'No audio file provided'}), 400
+
+    audio_file = request.files['audio']
+    audio_path = save_audiosegment_to_temp(AudioSegment.from_file(audio_file))
+
+    # Process the audio file here
+    audio = AudioSegment.from_file(audio_path)
+    audio = apply_normalize(audio)
+    audio = apply_noise_reduction(audio)
+
+    # Save the processed audio
+    processed_audio_path = save_audiosegment_to_temp(audio)
+    samples, sr = load_audiofile_to_numpy(processed_audio_path)
+
+    return jsonify({
+        'samples': samples.tolist(),
+        'sample_rate': sr,
+        'message': 'Audio processed successfully'
+    })
+
+@app.route('/apply_effect', methods=['POST'])
+def apply_effect_endpoint():
+    data = request.get_json()
+    effect = data.get('effect')
+    audio_file = data.get('audio_file')
+
+    if not audio_file:
+        return jsonify({'error': 'No audio file provided'}), 400
+
+    audio = AudioSegment.from_file(audio_file)
+
+    # Apply the selected effect
+    if effect == 'normalize':
+        audio = apply_normalize(audio)
+    elif effect == 'noise_reduction':
+        audio = apply_noise_reduction(audio)
+    # Add other effects here...
+
+    processed_audio_path = save_audiosegment_to_temp(audio)
+    samples, sr = load_audiofile_to_numpy(processed_audio_path)
+
+    return jsonify({
+        'samples': samples.tolist(),
+        'sample_rate': sr,
+        'message': f'{effect} applied successfully'
+    })
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)