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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
import streamlit as st | |
def sentiment_analysis(inp): | |
model_name = 'distilbert-base-uncased-finetuned-sst-2-english' | |
model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) | |
res = classifier(inp) | |
return res | |
def main(): | |
st.header("Check the sentiment of your text") | |
my_string = 'Hello, it was fun making this streamlit application.' | |
user_input = st.text_input("Enter text here:", value=my_string) | |
st.write("You entered:", user_input) | |
res = sentiment_analysis(user_input) | |
print(res) | |
with st.form("my_form"): | |
submit_button = st.form_submit_button(label='Submit') | |
sentiment = res[0]['label'] | |
conf = res[0]['score'] | |
if submit_button: | |
st.write("Sentiment of the input: ", sentiment) | |
st.write("Confidence of the predicted sentiment: ", conf) | |
if __name__ == '__main__': | |
main() |