File size: 1,104 Bytes
640262e bdfe5a9 3503540 640262e 3503540 640262e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 |
import streamlit as st
from transformers import pipeline
st.title('Sentiment Analysis using Transformers pipeline function')
st.write('This app uses the Hugging Face Transformers [sentiment analyzer](https://huggingface.co/course/chapter1/3?fw=tf) library to classify the sentiment of your input as positive or negative. The web app is built using [Streamlit](https://docs.streamlit.io/en/stable/getting_started.html).')
st.write('References: \n https://medium.com/@rtkilian/deploy-and-share-your-sentiment-analysis-app-using-streamlit-sharing-2ba3ca6a3ead')
st.write(' \t\t\t https://huggingface.co/learn/nlp-course/chapter1/3?fw=pt')
form = st.form(key='sentiment-form')
user_input = form.text_area('Enter your text')
submit = form.form_submit_button('Submit')
if submit:
classifier = pipeline("sentiment-analysis") #using the pipeline() function
result = classifier(user_input)[0]
label = result['label']
score = result['score']
if label == 'POSITIVE':
st.success(f'{label} sentiment (score: {score})')
else:
st.error(f'{label} sentiment (score: {score})')
|