Update app.py

app.py

@@ -1,12 +1,9 @@
 import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
 import torch
 
-# Define the summarization pipeline
+# Define pipelines
 summarizer_ntg = pipeline("summarization", model="mrm8488/t5-base-finetuned-summarize-news")
-
-# Load the tokenizer and model for classification
-tokenizer_bb = AutoTokenizer.from_pretrained("Lauraayu/News_Classi_Model")
 model_bb = AutoModelForSequenceClassification.from_pretrained("Lauraayu/News_Classi_Model")
 
 # Streamlit application title
@@ -20,18 +17,15 @@ text = st.text_area("Enter the news article text here:")
 if st.button("Classify"):
     # Perform text summarization
     summary = summarizer_ntg(text)[0]['summary_text']
-    
-    # Tokenize the summarized text
-    inputs = tokenizer_bb(summary, return_tensors="pt", truncation=True, padding=True, max_length=512)
-    
+        
     # Move inputs and model to the same device (GPU or CPU)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    inputs = {k: v.to(device) for k, v in inputs.items()}
+    summary = {k: v.to(device) for k, v in inputs.items()}
     model_bb.to(device)
     
     # Perform text classification
     with torch.no_grad():
-        outputs = model_bb(**inputs)
+        outputs = model_bb(**summary)
     
     # Get the predicted label
     predicted_label_id = torch.argmax(outputs.logits, dim=-1).item()