Update app.py

app.py

@@ -115,8 +115,8 @@ class RNN(nn.Module):
             h = h[-1]
         return self.fc(self.dropout(h))
 
-model_path = hf_hub_download(repo_id="Di12/sentiment_analysis", filename="model.pt", repo_type="dataset")
-embedding_path = hf_hub_download(repo_id="Di12/sentiment_analysis", filename="vi_word2vec_reduced.txt", repo_type="dataset")
+model_path = hf_hub_download(repo_id="Di12/sentiment_analysis", filename="model.pt", repo_type="space")
+embedding_path = hf_hub_download(repo_id="Di12/sentiment_analysis", filename="vi_word2vec_reduced.txt", repo_type="space")
 
 # Load pretrained embeddings and build vocab
 word_embedding = vocab.Vectors(