ajsdasdjks

app.py

@@ -2,10 +2,20 @@ import streamlit as st
 from transformers import HubertForSequenceClassification, HubertConfig, Wav2Vec2FeatureExtractor
 import torch
 import soundfile as sf
+import gdown
+
+file_id = "1xm9Uf7_wn3VR2ivuftCW0jkz5bDC0YxF"
 
 # Load model and tokenizer
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model_name = "model_hubert_finetuned_nopeft.pth"  # Replace with your model path or Hugging Face model hub path
+model_name = "model_hubert_finetuned_nopeft.pth"
+if not os.path.exists(model_name):
+    print(f"Downloading {model_name} from Google Drive...")
+    gdown.download(f'https://drive.google.com/uc?id={file_id}', model_name, quiet=False)
+else:
+    print(f"{output} already exists, skipping download.")
+
+  # Replace with your model path or Hugging Face model hub path
 config = HubertConfig.from_pretrained("superb/hubert-large-superb-er")
 config.id2label = {0: 'neu', 1: 'hap', 2: 'ang', 3: 'sad', 4: 'dis', 5: 'sur', 6: 'fea', 7: 'cal'}
 config.label2id = {"neu": 0, "hap": 1, "ang": 2, "sad": 3, "dis": 4, "sur": 5, "fea": 6, "cal": 7}

requirements.txt

@@ -1,4 +1,7 @@
 streamlit
 transformers
 torch
-soundfile
+soundfile
+gdown
+flask_cors
+flask