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Browse files- MLOps NLP.ipynb +0 -0
- app.py +23 -0
- sentiment_analysis.pkl +3 -0
MLOps NLP.ipynb
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app.py
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import streamlit as st
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import pickle
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# load model
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model = pickle.load(open('sentiment_analysis.pkl', 'rb'))
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# create title
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st.title('Sentiment Analysis Model')
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review = st.text_input('Enter your review:')
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submit = st.button('Predict')
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if submit:
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prediction = model.predict([review])
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# print(prediction)
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# st.write(prediction)
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if prediction[0] == 'positive':
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st.success('Positive Review')
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else:
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st.warning('Negative Review')
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sentiment_analysis.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:fa7fffb45ffd63280a9c17218a159472940674396da2b9124a91d1a8c541d648
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size 3613726
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