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