Emily666666
commited on
Commit
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00e92d0
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Parent(s):
0b6cb62
Update app.py
Browse files
app.py
CHANGED
@@ -2,34 +2,40 @@ import streamlit as st
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from transformers import pipeline
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# Load the text summarization pipeline
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# Load the classification pipeline
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model_name2_p2 = "elozano/bert-base-cased-news-category"
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# Streamlit app title
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st.title("
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# Tab layout
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tab1, tab2 = st.tabs(["
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with tab1:
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st.header("
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# Input text for summarization
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text_to_summarize = st.text_area("Enter
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if st.button("Summarize"):
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# Perform text summarization
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summary = model3_p1(text_to_summarize, max_length=130, min_length=30, do_sample=False)
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# Display the summary result
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st.write("Summary:", summary[0]['summary_text'])
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with tab2:
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st.header("
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# Input text for news classification
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text_to_classify = st.text_area("Enter
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if st.button("Classify"):
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# Perform
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results = classifier(text_to_classify)[0]
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# Display the classification result
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max_score = float('-inf')
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from transformers import pipeline
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# Load the text summarization pipeline
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try:
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model3_p1 = pipeline("summarization", model="syndi-models/titlewave-t5-base")
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except ValueError as e:
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st.error(f"Error loading summarization model: {e}")
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# Load the news classification pipeline
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model_name2_p2 = "elozano/bert-base-cased-news-category"
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try:
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classifier = pipeline("text-classification", model=model_name2_p2, return_all_scores=True)
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except ValueError as e:
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st.error(f"Error loading classification model: {e}")
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# Streamlit app title
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st.title("Summarization and News Classification")
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# Tab layout
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tab1, tab2 = st.tabs(["Text Summarization", "News Classification"])
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with tab1:
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st.header("Text Summarization")
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# Input text for summarization
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text_to_summarize = st.text_area("Enter text to summarize:", "")
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if st.button("Summarize") and 'model3_p1' in globals():
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# Perform text summarization
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summary = model3_p1(text_to_summarize, max_length=130, min_length=30, do_sample=False)
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# Display the summary result
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st.write("Summary:", summary[0]['summary_text'])
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with tab2:
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st.header("News Classification")
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# Input text for news classification
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text_to_classify = st.text_area("Enter text to classify:", "")
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if st.button("Classify") and 'classifier' in globals():
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# Perform news classification
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results = classifier(text_to_classify)[0]
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# Display the classification result
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max_score = float('-inf')
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