Update app.py

app.py

@@ -21,6 +21,7 @@ from faster_whisper import WhisperModel
 from TTS.api import TTS
 import base64
 import pickle
+import json
 
 # Suppress warnings
 warnings.filterwarnings("ignore")
@@ -157,6 +158,29 @@ def auto_eq(audio, genre="Pop"):
 
     return array_to_audiosegment(samples.astype(np.int16), sr, channels=audio.channels)
 
+# === AI Mastering Chain – Genre EQ + Loudness Match + Limiting ===
+def ai_mastering_chain(audio_path, genre="Pop", target_lufs=-14.0):
+    audio = AudioSegment.from_file(audio_path)
+
+    # Apply Genre EQ
+    eq_audio = auto_eq(audio, genre=genre)
+
+    # Convert to numpy for loudness
+    samples, sr = audiosegment_to_array(eq_audio)
+
+    # Apply loudness normalization
+    meter = pyln.Meter(sr)
+    loudness = meter.integrated_loudness(samples.astype(np.float64) / 32768.0)
+    gain_db = target_lufs - loudness
+    final_audio = eq_audio + gain_db
+
+    # Final limiting
+    final_audio = apply_limiter(final_audio)
+
+    out_path = os.path.join(tempfile.gettempdir(), "mastered_output.wav")
+    final_audio.export(out_path, format="wav")
+    return out_path
+
 # === Vocal Isolation Helpers ===
 def load_track_local(path, sample_rate, channels=2):
     sig, rate = torchaudio.load(path)
@@ -255,7 +279,7 @@ def process_audio(audio_file, selected_effects, isolate_vocals, preset_name, exp
         status = f"❌ Error: {str(e)}"
         return None, None, status, "", status
 
-# === Visualize Waveform ===
+# === Waveform + Spectrogram Generator ===
 def show_waveform(audio_file):
     try:
         audio = AudioSegment.from_file(audio_file)
@@ -323,7 +347,7 @@ def batch_process_audio(files, selected_effects, isolate_vocals, preset_name, ex
             results.append(processed_path)
             session_logs.append(log)
 
-        zip_path = os.path.join(output_dir, "batch_output.zip")
+        zip_path = os.path.join(tempfile.gettempdir(), "batch_output.zip")
         with zipfile.ZipFile(zip_path, 'w') as zipf:
             for i, res in enumerate(results):
                 filename = f"processed_{i}.{export_format.lower()}"
@@ -347,6 +371,7 @@ def auto_tune_vocal(audio_path, target_key="C"):
 def visualize_spectrum(audio_path):
     y, sr = torchaudio.load(audio_path)
     y_np = y.numpy().flatten()
+    
     stft = librosa.stft(y_np)
     db = librosa.amplitude_to_db(abs(stft))
 
@@ -388,6 +413,21 @@ with gr.Blocks(title="AI Audio Studio", css="style.css") as demo:
             output_audio, waveform_img, session_log_out, genre_out, status_box
         ])
 
+    # --- AI Mastering Chain Tab – Now Fully Defined ===
+    with gr.Tab("🎧 AI Mastering Chain"):
+        gr.Interface(
+            fn=ai_mastering_chain,
+            inputs=[
+                gr.Audio(label="Upload Track", type="filepath"),
+                gr.Dropdown(choices=["Pop", "EDM", "Rock", "Hip-Hop", "Acoustic", "Metal", "Trap", "LoFi"], label="Genre", value="Pop"),
+                gr.Slider(minimum=-24, maximum=-6, value=-14, label="Target LUFS")
+            ],
+            outputs=gr.Audio(label="Mastered Output", type="filepath"),
+            title="Genre-Based Mastering",
+            description="Apply genre-specific EQ + loudness matching + limiter",
+            allow_flagging="never"
+        )
+
     # --- Remix Mode ---
     with gr.Tab("🎛 Remix Mode"):
         gr.Interface(
@@ -405,21 +445,6 @@ with gr.Blocks(title="AI Audio Studio", css="style.css") as demo:
             clear_btn=None
         )
 
-    # --- AI Mastering Chain Tab ===
-    with gr.Tab("🎧 AI Mastering Chain"):
-        gr.Interface(
-            fn=ai_mastering_chain,
-            inputs=[
-                gr.Audio(label="Upload Track", type="filepath"),
-                gr.Dropdown(choices=["Pop", "EDM", "Rock", "Hip-Hop", "Acoustic", "Metal", "Trap", "LoFi"], label="Genre", value="Pop"),
-                gr.Slider(minimum=-24, maximum=-6, value=-14, label="Target LUFS")
-            ],
-            outputs=gr.Audio(label="Mastered Output", type="filepath"),
-            title="Genre-Based Mastering",
-            description="Apply genre-specific EQ + loudness matching + limiter",
-            allow_flagging="never"
-        )
-
     # --- Harmonic Saturation / Exciter ===
     with gr.Tab("🧬 Harmonic Saturation"):
         gr.Interface(