Update app.py

app.py

@@ -10,17 +10,18 @@ from transformers import RobertaTokenizer, RobertaModel
 from transformers import AutoModelForSequenceClassification
 from transformers import TFAutoModelForSequenceClassification
 from transformers import AutoTokenizer, AutoModelForTokenClassification
-from transformers import XLNetTokenizer, XLNetForSequenceClassification
+
 import matplotlib.pyplot as plt
 import sys
 import csv
-import sentencepiece
+
 csv.field_size_limit(sys.maxsize)
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-tokenizer = AutoTokenizer.from_pretrained("vikvenk/ADR_Detection")
-model = AutoModelForSequenceClassification.from_pretrained("vikvenk/ADR_Detection").to(device)
+tokenizer = AutoTokenizer.from_pretrained("MarkAdamsMSBA24/ADRv2024")  
+model = AutoModelForSequenceClassification.from_pretrained("MarkAdamsMSBA24/ADRv2024").to(device)
+
 # build a pipeline object to do predictions
 pred = transformers.pipeline("text-classification", model=model, 
                              tokenizer=tokenizer, return_all_scores=True)
@@ -137,4 +138,4 @@ with gr.Blocks(title=title) as demo:
          # , med, sym
                              ], main, cache_examples=True)
     
-    demo.launch()
+demo.launch()