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import streamlit as st |
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from transformers import pipeline |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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st.title("Sentiment Analysis") |
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def analyze(input, model): |
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return "This is a sample output" |
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fine_tuned = "jbraha/twitter-bert" |
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input = st.text_area("insert text to be analyzed", value="Nice to see you today.", |
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height=None, max_chars=None, key=None, help=None, on_change=None, |
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args=None, kwargs=None, placeholder=None, disabled=False, |
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label_visibility="visible") |
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option = st.selectbox( |
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'Choose a transformer model:', |
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('Default', 'Fine-Tuned' , 'Roberta')) |
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if option == 'Fine-Tuned': |
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model = AutoModelForSequenceClassification.from_pretrained(fine_tuned) |
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tokenizer = AutoTokenizer.from_pretrained(fine_tuned) |
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classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) |
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elif option == 'Roberta': |
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model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") |
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tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") |
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classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) |
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else: |
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classifier = pipeline('sentiment-analysis') |
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if st.button('Analyze'): |
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st.write(classifier(input)) |
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else: |
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st.write('Excited to analyze!') |
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