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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(
'To find out how this app was developed, please check out my [Medium post](https://medium.com/@rtkilian/deploy-and-share-your-sentiment-analysis-app-using-streamlit-sharing-2ba3ca6a3ead). To see my source code, have a look at my [GitHub repo](https://github.com/rtkilian/streamlit-huggingface).')
st.write('*Note: it will take up to 30 seconds to run the app.*')
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")
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})')
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