Update app.py

app.py

@@ -1,34 +1,39 @@
 import os
-import joblib
-import pandas as pd
-import numpy as np
-import torch
-import torchaudio
-import warnings
-import gradio as gr
-
-# --- STEP 1: THE ROBUST MONKEY PATCH ---
-# This fixes both the 'use_auth_token' error and the 'custom.py' 404 crash
 import huggingface_hub
+from speechbrain.inference.classifiers import EncoderClassifier
+
+# 1. Corrected Monkey Patch for SpeechBrain 1.0.0 + huggingface-hub 0.23+
 orig_download = huggingface_hub.hf_hub_download
 
 def patched_download(*args, **kwargs):
-    # 1. Fix the 'use_auth_token' renaming issue
+    # Fix the 'use_auth_token' vs 'token' renaming issue
     if 'use_auth_token' in kwargs:
         kwargs['token'] = kwargs.pop('use_auth_token')
     
+    # Get the requested filename
+    fname = kwargs.get('filename') or (args[1] if len(args) > 1 else None)
+    
     try:
         return orig_download(*args, **kwargs)
     except Exception as e:
-        # 2. Fix the 'custom.py' 404 crash
-        # SpeechBrain looks for this optional file; if it's missing, we return None
-        fname = kwargs.get('filename') or (args[1] if len(args) > 1 else None)
+        # If 'custom.py' is missing (404), return a dummy file path instead of None
+        # to prevent the 'NoneType' crash in pathlib.
         if fname == "custom.py" and ("404" in str(e) or "Not Found" in str(e)):
-            return None
+            dummy_path = os.path.abspath("dummy_custom.py")
+            if not os.path.exists(dummy_path):
+                with open(dummy_path, "w") as f:
+                    f.write("# Dummy file for SpeechBrain compatibility\n")
+            return dummy_path
         raise e
 
 huggingface_hub.hf_hub_download = patched_download
 
+# 2. Load the model
+print("Loading SpeechBrain ECAPA feature extractor...")
+feature_extractor = EncoderClassifier.from_hparams(
+    source="speechbrain/spkrec-ecapa-voxceleb",
+    savedir="pretrained_models/spkrec-ecapa-voxceleb"
+)
 # Import SpeechBrain AFTER the patch
 from speechbrain.inference.speaker import EncoderClassifier