Update app.py

app.py

@@ -19,7 +19,6 @@ import soundfile as sf
 import numpy as np
 import torch
 import uvicorn
-from tqdm import tqdm
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
@@ -63,9 +62,8 @@ class S3ModelLoader:
         except EnvironmentError:
             logging.info(f"Model {model_name} not found in S3. Downloading...")
             try:
-                with tqdm(unit="B", unit_scale=True, desc=f"Downloading {model_name}", disable=False) as t:
-                    model = AutoModelForCausalLM.from_pretrained(model_name, token=HUGGINGFACE_HUB_TOKEN, _tqdm=t)
-                    tokenizer = AutoTokenizer.from_pretrained(model_name, token=HUGGINGFACE_HUB_TOKEN)
+                model = AutoModelForCausalLM.from_pretrained(model_name, token=HUGGINGFACE_HUB_TOKEN)
+                tokenizer = AutoTokenizer.from_pretrained(model_name, token=HUGGINGFACE_HUB_TOKEN)
                 logging.info(f"Downloaded {model_name} successfully.")
                 logging.info(f"Saving {model_name} to S3...")
                 model.save_pretrained(s3_uri)
@@ -150,7 +148,7 @@ async def generate(request: Request, body: GenerateRequest):
             sf.write(audio_bytesio, audio["sampling_rate"], np.int16(audio["audio"]))
             audio_bytes = audio_bytesio.getvalue()
             return Response(content=audio_bytes, media_type="audio/wav")
-        
+
         elif body.task_type == "text-to-video":
             try:
                 generator = pipeline("text-to-video", model=model, tokenizer=tokenizer, device=device)