Update app.py

app.py

@@ -3,17 +3,8 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassifica
 import torch
 
 # Define the summarization pipeline
-try:
-    summarizer_ntg = pipeline("text2text-generation", model="mrm8488/t5-base-finetuned-summarize-news")
-except Exception as e:
-    st.error(f"Error loading summarization model: {e}")
-
-# Load the tokenizer and model for classification
-try:
-    tokenizer_bb = AutoTokenizer.from_pretrained("Lauraayu/News_Classi_Model")
-    model_bb = AutoModelForSequenceClassification.from_pretrained("Lauraayu/News_Classi_Model")
-except Exception as e:
-    st.error(f"Error loading classification model or tokenizer: {e}")
+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")
@@ -24,32 +15,18 @@ text = st.text_area("Enter the news article text here:")
 
 # Perform summarization and classification when the user clicks the "Classify" button
 if st.button("Classify"):
-    if not text:
-        st.error("Please enter some text to classify.")
-    else:
-        try:
-            # 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()}
-            model_bb.to(device)
-            
-            # Perform text classification
-            with torch.no_grad():
-                outputs = model_bb(**inputs)
-            
-            # 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]
+    # Perform text summarization
+    summary = summarizer_ntg(text)[0]['summary_text']
             
-            # Display the summary and classification result
-            st.write("Summary:", summary)
-            st.write("Category:", predicted_label)
-        except Exception as e:
-            st.error(f"Error during summarization or classification: {e}")
+    # 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)