Update app.py

app.py

@@ -20,8 +20,8 @@ csv.field_size_limit(sys.maxsize)
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-tokenizer = AutoTokenizer.from_pretrained("paragon_analytics/ADRv2024")  
-model = AutoModelForSequenceClassification.from_pretrained("paragon_analytics/ADRv2024").to(device)
+tokenizer = AutoTokenizer.from_pretrained("MiyunKim/Mod4Team5")  
+model = AutoModelForSequenceClassification.from_pretrained("MiyunKim/Mod4Team5").to(device)
 
 # build a pipeline object to do predictions
 pred = transformers.pipeline("text-classification", model=model, 
@@ -134,7 +134,7 @@ with gr.Blocks(title=title) as demo:
     with gr.Row():
         gr.Markdown("### Click on any of the examples below to see how it works:")
         gr.Examples([["A 65 year-old male had severe headache after taking Aspirin. The lab results were normal."],
-                     ["A 65 year-old female had minor pain in upper abdomen after taking Acetaminophen."]], 
+                     ["A 65 year-old female had minor pain in upper abdomen after taking Warfarin."]], 
                     [prob1], [label,local_plot, htext
          # , med, sym
                              ], main, cache_examples=True)