File size: 1,622 Bytes
7f0e3da |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
import streamlit as st
from transformers import pipeline
# Load the text summarization pipeline
model3_p1 = pipeline("summarization", model="syndi-models/titlewave-t5-base")
# Load the classification pipeline
model_name2_p2 = "elozano/bert-base-cased-news-category"
classifier = pipeline("text-classification", model=model_name2_p2, return_all_scores=True)
# Streamlit app title
st.title("Question Summarization and Classification")
# Tab layout
tab1, tab2 = st.tabs(["Question Summarization", "Question Classification"])
with tab1:
st.header("Question Summarization")
# Input text for summarization
text_to_summarize = st.text_area("Enter question to summarize:", "")
if st.button("Summarize"):
# 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("Question Classification")
# Input text for news classification
text_to_classify = st.text_area("Enter question title to classify:", "")
if st.button("Classify"):
# Perform question 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) |