test

app.py

@@ -27,13 +27,21 @@ device = None  # Declare device at the global level
 @spaces.GPU
 def download_model():
     global model, processor, device
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-    model_id = "openai/whisper-large-v3"
-    model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        model_id, torch_dtype=torch_dtype
-    ).to(device)
-    processor = AutoProcessor.from_pretrained(model_id)
+    try:
+        logging.info("Starting model download...")
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+        model_id = "openai/whisper-large-v3"
+        
+        model = AutoModelForSpeechSeq2Seq.from_pretrained(
+            model_id, torch_dtype=torch_dtype
+        ).to(device)
+        processor = AutoProcessor.from_pretrained(model_id)
+        
+        logging.info("Model and processor downloaded successfully.")
+    except Exception as e:
+        logging.error(f"An error occurred while downloading the model and processor: {e}")
+
 
 # Function to set up the pipeline
 def create_pipeline():
@@ -133,4 +141,6 @@ with gr.Blocks() as demo:
             youtube_url_input = gr.Textbox(label="Enter YouTube URL")
             youtube_download_output = gr.File(label="Download MP3", file_count="single")
             youtube_url_input.change(youtube_video_to_mp3, inputs=youtube_url_input, outputs=youtube_download_output)
-demo.launch()
+
+demo.launch()
+