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charanj001
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92753d3
Create app.py
Browse files
app.py
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import streamlit as st
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from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
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from transformers import pipeline
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def multilingualmodel():
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st.markdown("# multilingual model 🎈")
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st.sidebar.markdown("# nlptown/bert-base-multilingual-uncased-sentiment🎈")
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st.write("This classifier can now deal with texts in English, French, but also Dutch, German, Italian and Spanish!")
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classifier = pipeline('sentiment-analysis')
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model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
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model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
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user_input = st.text_area('Enter Text to Analyze')
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button = st.button("Analyze")
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if user_input and button :
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tt = classifier(user_input)
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st.write(tt)
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for result in tt:
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st.success(f"label: {result['label']}, with score: {round(result['score'], 4)}")
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def engdistilbertmod():
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st.markdown("distilbert base finetuned english ❄️")
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st.sidebar.markdown("# distilbert-base-uncased-finetuned-sst-2-english ❄️")
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model_name = "distilbert-base-uncased-finetuned-sst-2-english"
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tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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classifier = pipeline('sentiment-analysis', model=tf_model, tokenizer=tokenizer)
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user_input = st.text_area('Enter Text to Analyze With distilbert ', key= "distilbert_input")
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button = st.button("Analyze", key= "distilbert_button")
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if user_input and button :
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tt = classifier(user_input)
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for result in tt:
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st.success(f"label: {result['label']}, with score: {round(result['score'], 4)}")
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page_names_to_funcs = {
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"Bert-base-Multilingual": multilingualmodel,
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"Distilbert base": engdistilbertmod,
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}
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selected_page = st.sidebar.selectbox("Select a page", page_names_to_funcs.keys())
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page_names_to_funcs[selected_page]()
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