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import streamlit as st | |
from transformers import pipeline | |
# title | |
st.title("Sentiment Analysis App") | |
# subtitle | |
st.markdown("Enter a text and select a pretrained model to get the sentiment analysis:") | |
st.markdown("Link to the app - [sentiment-analysis-streamlit on π€ Spaces](https://huggingface.co/spaces/rd448/sentiment-analysis-streamlit)") | |
# text input | |
text = st.text_input("Enter text here", "I love you") | |
# Model selection dropdown | |
model_names = ["distilbert-base-uncased-finetuned-sst-2-english"] | |
model = st.selectbox("Select a pretrained model", model_names) | |
# Sentiment analysis function | |
def analyze_sentiment(text, model): | |
if model == "distilbert-base-uncased-finetuned-sst-2-english": | |
classifier = pipeline("sentiment-analysis", model=model) | |
result = classifier(text)[0] | |
sentiment = result['label'] | |
score = result['score'] | |
return sentiment, score | |
if st.button("Analyze"): | |
sentiment, score = analyze_sentiment(text, model) | |
st.write(f"Sentiment: {sentiment}") | |
if score is not None: | |
st.write(f"Score: {score}") |