Upload app.py

app.py

@@ -13,9 +13,6 @@ model_id = "nvidia/canary-qwen-2.5b"
 print("Loading NVIDIA Canary-Qwen-2.5B model using NeMo...")
 model = SALM.from_pretrained(model_id)
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device)
-
 def generate_text(prompt, max_tokens=200, temperature=0.7, top_p=0.9):
     """Generate text using the NVIDIA NeMo model (LLM mode)"""
     
@@ -31,8 +28,7 @@ def generate_text(prompt, max_tokens=200, temperature=0.7, top_p=0.9):
             )
         
         # Convert IDs to text using model's tokenizer
-        # response = model.tokenizer.ids_to_text(answer_ids[0].cpu())
-        response = model.tokenizer.ids_to_text(answer_ids[0].to(device))
+        response = model.tokenizer.ids_to_text(answer_ids[0].cpu())
         return response
         
     except Exception as e:
@@ -54,8 +50,7 @@ def transcribe_audio(audio_file, user_prompt="Transcribe the following:"):
         )
         
         # Convert IDs to text
-        # transcript = model.tokenizer.ids_to_text(answer_ids[0].cpu())
-        transcript = model.tokenizer.ids_to_text(answer_ids[0].to(device))
+        transcript = model.tokenizer.ids_to_text(answer_ids[0].cpu())
         return transcript
         
     except Exception as e:
@@ -238,4 +233,5 @@ with gr.Blocks(title="NVIDIA Canary-Qwen-2.5B Chat") as demo:
     )
 
 if __name__ == "__main__":
+    demo.queue(api_open=True)
     demo.launch(share=True)