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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]()
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