Update app.py

app.py

@@ -17,8 +17,8 @@ bert_path = "bert-base-uncased"
 tokenizer = AutoTokenizer.from_pretrained(bert_path)
 
 # Read and format data.
-tweets_raw = pd.read_csv("test.csv", nrows=20)
-labels_raw = pd.read_csv("test_labels.csv", nrows=20)
+tweets_raw = pd.read_csv("test.csv", nrows=50)
+labels_raw = pd.read_csv("test_labels.csv", nrows=50)
 
 label_set = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
 label_vector = labels_raw[label_set].values.tolist()
@@ -63,7 +63,7 @@ class ToxicityDataset(Dataset):
 
 # Based on user model selection, prepare Dataset and DataLoader
 infer_dataset = ToxicityDataset(tweet_df, tokenizer, MAX_LENGTH)
-infer_params = {"batch_size": INFER_BATCH_SIZE, "shuffle": True}
+infer_params = {"batch_size": INFER_BATCH_SIZE, "shuffle": False}
 infer_loader = DataLoader(infer_dataset, **infer_params)
 
 class BertClass(torch.nn.Module):