32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import streamlit as st
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from backend import load_model_and_vectorizer, predict_sentiment
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def app():
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# Dropdown untuk memilih model
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model_choice = st.selectbox('Pilih Model', ['SVM', 'Naive Bayes', 'KNN'])
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# Input teks dari user
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user_input = st.text_area('Masukkan teks untuk analisis sentimen')
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# Tombol untuk melakukan prediksi
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if st.button('Prediksi Sentimen'):
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if model_choice == 'SVM':
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model_path = 'models/svm_model.pkl'
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vectorizer_path = 'models/datasets-tfidf.pkl'
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elif model_choice == 'Naive Bayes':
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model_path = 'models/nb_model.pkl'
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vectorizer_path = 'models/datasets-tfidf.pkl'
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elif model_choice == 'KNN':
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model_path = 'models/knn_model.pkl'
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vectorizer_path = 'models/datasets-tfidf.pkl'
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# Load model dan vectorizer
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model, vectorizer = load_model_and_vectorizer(model_path, vectorizer_path)
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# Prediksi sentimen
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prediction = predict_sentiment(model, vectorizer, user_input)
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st.write(f'#### Prediksi Sentimen: {prediction}')
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if __name__ == '__main__':
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app() |