Update app.py

app.py

@@ -1,32 +1,26 @@
 import streamlit as st
 from transformers import pipeline
-import torch
 
-# Define pipelines
-summarizer_ntg = pipeline("summarization", model="mrm8488/t5-base-finetuned-summarize-news")
-model_bb = AutoModelForSequenceClassification.from_pretrained("Lauraayu/News_Classi_Model")
-
-# Streamlit application title
-st.title("News Article Summarizer and Classifier")
-st.write("Enter a news article text to get its summary and category.")
-
-# Text input for user to enter the news article text
-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"):
-    # Perform text summarization
-    summary = summarizer_ntg(text)[0]['summary_text']
+def main():
+    # Define pipelines
+    summarizer_ntg = pipeline(model="mrm8488/t5-base-finetuned-summarize-news")
+    model = pipeline(model="Lauraayu/News_Classi_Model")
     
-    # Perform text classification
-    with torch.no_grad():
-        outputs = model_bb(**summary)
+    # Streamlit application title
+    st.title("News Article Classifier")
+    st.write("Enter a news article text to get its category:")
     
-    # 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]
+    # Text input for user to enter the news article text
+    text = st.text_area("Enter the news article text here:")
     
-    # Display the summary and classification result
-    st.write("Summary:", summary)
-    st.write("Category:", predicted_label)
+    # Perform summarization and classification when the user clicks the "Classify" button
+    if st.button("Classify"):
+        # Perform text summarization
+        summary = summarizer_ntg(text)[0]
+        
+        # Perform classification
+        output = model(summary)
+        category = output[0]["label"]
+
+        # Display the summary and classification result
+        st.write("Category:", category)