|
|
| import streamlit as st |
| from transformers import pipeline |
|
|
| st.set_page_config(page_title="Semantic Analyzer") |
|
|
| st.title("Semantic Analyzer") |
| st.write("Analyze the sentiment of your text using NLP.") |
|
|
| @st.cache_resource |
| def load_model(): |
| return pipeline( |
| "sentiment-analysis", |
| model="distilbert/distilbert-base-uncased-finetuned-sst-2-english", |
| device=-1 |
| ) |
|
|
| text = st.text_area("Enter a sentence or paragraph") |
|
|
| if st.button("Analyze"): |
| if text.strip(): |
| with st.spinner("Analyzing text..."): |
| model = load_model() |
| result = model(text[:5000])[0] |
|
|
| st.subheader("Result") |
| st.write("Sentiment:", result["label"]) |
| st.write("Confidence:", f"{result['score']:.2%}") |
| else: |
| st.warning("Please enter some text.") |
|
|
