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import streamlit as st
from transformers import pipeline
# Load the text summarization pipeline
try:
model3_p1 = pipeline("summarization", model="syndi-models/titlewave-t5-base")
except ValueError as e:
st.error(f"Error loading summarization model: {e}")
# Load the news classification pipeline
model_name2_p2 = "elozano/bert-base-cased-news-category"
try:
classifier = pipeline("text-classification", model=model_name2_p2, return_all_scores=True)
except ValueError as e:
st.error(f"Error loading classification model: {e}")
# Streamlit app title
st.title("Summarization and News Classification")
# Tab layout
tab1, tab2 = st.tabs(["Text Summarization", "News Classification"])
with tab1:
st.header("Text Summarization")
# Input text for summarization
text_to_summarize = st.text_area("Enter text to summarize:", "")
if st.button("Summarize") and 'model3_p1' in globals():
# Perform text summarization
summary = model3_p1(text_to_summarize, max_length=130, min_length=30, do_sample=False)
# Display the summary result
st.write("Summary:", summary[0]['summary_text'])
with tab2:
st.header("News Classification")
# Input text for news classification
text_to_classify = st.text_area("Enter text to classify:", "")
if st.button("Classify") and 'classifier' in globals():
# Perform news classification
results = classifier(text_to_classify)[0]
# 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_to_classify)
st.write("Category:", max_label)
st.write("Score:", max_score) |