Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the summariztion model pipeline
+summarizer_ntg = pipeline("text2text-generation", model="mrm8488/t5-base-finetuned-summarize-news")
+classifier = pipeline("text-classification", model='Lauraayu/News_Classi_Model', return_all_scores=True)
+
+# Streamlit application title
+st.title("News Classification")
+st.write("Classification for different News types")
+
+# Text input for user to enter the text to classify
+text = st.text_area("Enter the News to classify","")
+
+# Perform text classification when the user clicks the "Classify" button
+if st.button("Classify"):
+    
+    # Perform text classification on the input text
+    result0 = summarizer_ntg(text)
+    result = classifier(result0)
+    # Display the classification result
+    max_score = float('-inf')
+    max_label = ''
+    for result in results:
+        if result['score'] > max_score:
+        max_score = result['score']
+        max_label = result['label']
+    st.write("Text:", text)
+    st.write("Label:", max_label)
+    st.write("Score:", max_score)