Use sync vllm.LLM instead of AsyncLLMEngine to avoid event loop conflicts

main.py

@@ -339,20 +339,43 @@ async def startup():
     from vllm import AsyncLLMEngine
     from vllm.engine.arg_utils import AsyncEngineArgs
     
-    # Monkey-patch OrpheusModel to support max_model_len (PyPI version doesn't)
+    # Monkey-patch OrpheusModel to use sync LLM for proper sync context
     original_setup_engine = OrpheusModel._setup_engine
     def patched_setup_engine(self):
         # Get the mapped model name (handles "medium-3b" -> full path)
         model_name = self._map_model_params(self.model_name)
-        engine_args = AsyncEngineArgs(
+        # Use LLM (sync) instead of AsyncLLMEngine to avoid event loop conflicts
+        from vllm import LLM
+        return LLM(
             model=model_name,
             max_model_len=MAX_MODEL_LEN,  # Our custom limit!
             gpu_memory_utilization=0.85,  # Leave some headroom
             enforce_eager=False,
         )
-        return AsyncLLMEngine.from_engine_args(engine_args)
     OrpheusModel._setup_engine = patched_setup_engine
     
+    # Also patch generate_tokens_sync to work with sync LLM
+    def patched_generate_tokens_sync(self, prompt, voice=None, request_id="req-001", temperature=0.6, top_p=0.8, max_tokens=1200, stop_token_ids=[49158], repetition_penalty=1.3):
+        from vllm import SamplingParams
+        prompt_string = self._format_prompt(prompt, voice)
+        print(prompt)
+        sampling_params = SamplingParams(
+            temperature=temperature,
+            top_p=top_p,
+            max_tokens=max_tokens,
+            stop_token_ids=stop_token_ids,
+            repetition_penalty=repetition_penalty,
+        )
+        # Use sync generate - yields full output
+        outputs = self.engine.generate([prompt_string], sampling_params)
+        # Yield individual tokens from the output text
+        for output in outputs:
+            text = output.outputs[0].text
+            # Tokens are space-separated custom_token_XXX
+            for token in text.split():
+                yield token
+    OrpheusModel.generate_tokens_sync = patched_generate_tokens_sync
+    
     model = OrpheusModel(model_name=ORPHEUS_MODEL)
     
     print("✓ Orpheus model loaded successfully")