Update app.py

app.py

@@ -4,7 +4,7 @@ import torch
 
 # Define the summarization pipeline
 summarizer_ntg = pipeline("text2text-generation", model="mrm8488/t5-base-finetuned-summarize-news")
-model_bb = pipeline("Lauraayu/News_Classi_Model")
+
 
 # Streamlit application title
 st.title("News Article Summarizer and Classifier")
@@ -17,16 +17,6 @@ text = st.text_area("Enter the news article text here:")
 if st.button("Classify"):
     # Perform text summarization
     summary = summarizer_ntg(text)[0]['summary_text']
-            
-    # Perform text classification
-    with torch.no_grad():
-        outputs = model_bb(**summary)
-    
-    # Get the predicted label
-    predicted_label_id = torch.argmax(outputs.logits, dim=-1).item()
-    label_mapping = model_bb.config.id2label
-    predicted_label = label_mapping[predicted_label_id]
-    
+        
     # Display the summary and classification result
     st.write("Summary:", summary)
-    st.write("Category:", predicted_label)