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Create app.py

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  1. app.py +30 -0
app.py ADDED
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+ import streamlit as st
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+ from transformers import pipeline
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+
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+ # Load the text classification model pipeline
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+ classifier = pipeline("text-classification",model='isom5240sp24/bert-base-uncased-emotion', return_all_scores=True)
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+
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+ # Streamlit application title
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+ st.title("Text Classification for you")
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+ st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
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+
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+ # Text input for user to enter the text to classify
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+ text = st.text_area("Enter the text to classify", "")
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+
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+ # Perform text classification when the user clicks the "Classify" button
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+ if st.button("Classify"):
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+ # Perform text classification on the input text
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+ results = classifier(text)[0]
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+
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+ # Display the classification result
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+ max_score = float('-inf')
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+ max_label = ''
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+
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+ for result in results:
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+ if result['score'] > max_score:
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+ max_score = result['score']
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+ max_label = result['label']
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+
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+ st.write("Text:", text)
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+ st.write("Label:", max_label)
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+ st.write("Score:", max_score)