Update app.py

app.py

@@ -11,7 +11,7 @@ tokenizer = AutoTokenizer.from_pretrained("ikoghoemmanuell/finetuned_fake_news_b
 @st.cache_resource
 def detect_fake_news(text):
     # Load the pipeline.
-    pipeline = transformers.pipeline("text-classification")
+    model = transformers.pipeline("text-classification", model=model, tokenizer=tokenizer)
 
     # Predict the sentiment.
     prediction = pipeline(text)
@@ -59,7 +59,7 @@ unsafe_allow_html=True
 if text:
     label, score = detect_fake_news(text)
     print(label, score)
-    if label == "NEGATIVE":
+    if label == "LABEL_1":
         st.error(f"The text is likely to be fake news with a confidence score of {score*100:.2f}%!")
     else:
         st.success(f"The text is likely to be genuine with a confidence score of {score*100:.2f}%!")