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import streamlit as st | |
from transformers import pipeline | |
pipe = pipeline('sentiment-analysis') | |
# Set the title and add introductory text | |
st.title("Sentiment Analysis App") | |
st.write("This simple app analyzes the sentiment of your text.") | |
# Use 'form' to group input elements together | |
with st.form("sentiment_form"): | |
# Input for text to analyze sentiment | |
text = st.text_area("Enter text for sentiment analysis:") | |
# Add a button with a label | |
submit_button = st.form_submit_button("Analyze Sentiment") | |
# Check if the form was submitted | |
if text and submit_button: | |
# Analyze sentiment | |
out = pipe(text) | |
result = out[0] # Assuming you want the first result if multiple are returned | |
sentiment = result["label"] | |
score = round(result["score"], 2) # Round the score to two decimal places | |
# Display sentiment analysis results | |
st.header("Sentiment Analysis Result") | |
st.write(f"**Sentiment**: {sentiment}") | |
st.write(f"**Sentiment Score**: {score}") | |
# Add a section for instructions on how to use the app | |
st.header("How to Use") | |
st.write("1. Enter text in the text area above.") | |
st.write("2. Click the 'Analyze Sentiment' button to analyze the sentiment.") | |
st.write("3. The sentiment label and score will be displayed below.") | |
# Add a section with information about the sentiment analysis model | |
st.header("About the Model") | |
st.write("The sentiment analysis is performed using the Hugging Face Transformers library.") | |
st.write("The model used is 'nlptown/bert-base-multilingual-uncased-sentiment'.") | |
# Footer with a link to LinkedIn profile | |
st.header("Connect with Me on LinkedIn") | |
st.write("Feel free to connect with me on LinkedIn for any inquiries or collaborations.") | |
st.markdown("[LinkedIn Profile](https://www.linkedin.com/in/iam-manoj/)") | |
# Footer with additional information or links | |
st.footer("For more information, visit the [Hugging Face Transformers website](https://huggingface.co/transformers/).") | |