| 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})') | |