update load model function

app.py

@@ -34,23 +34,25 @@ def load_models(hf_token):
     """Load all required models"""
     global whisper_model, diarization_pipeline, med_ner
     
-    # Load Whisper (Speech-to-Text)
-    if whisper_model is None:
-        whisper_model = whisper.load_model(WHISPER_MODEL_SIZE, device="cuda" if torch.cuda.is_available() else "cpu")
-    
-    # Load Diarization
-    if diarization_pipeline is None:
-        diarization_pipeline = Pipeline.from_pretrained(
-            "pyannote/speaker-diarization", 
-            use_auth_token=hf_token
-        )
-    
-    # Load Medical NER
-    if med_ner is None:
-        med_ner = hf_pipeline("ner", model=MEDICAL_NER_MODEL, aggregation_strategy="simple")
-    
-    return "Models loaded successfully"
-
+    try:
+        # Load Whisper
+        if whisper_model is None:
+            whisper_model = whisper.load_model(WHISPER_MODEL_SIZE, device="cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Load Diarization
+        if diarization_pipeline is None:
+            diarization_pipeline = Pipeline.from_pretrained(
+                "pyannote/speaker-diarization-3.1", 
+                use_auth_token=hf_token
+            )
+        
+        # Load Medical NER
+        if med_ner is None:
+            med_ner = hf_pipeline("ner", model=MEDICAL_NER_MODEL, aggregation_strategy="simple")
+        
+        return "Models loaded successfully", None  # Return tuple matching expected outputs
+    except Exception as e:
+        return f"Error loading models: {str(e)}", None
 def convert_audio_to_wav(input_file):
     """Convert any audio file to 16kHz WAV format"""
     audio = AudioSegment.from_file(input_file)