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import streamlit as st
from transformers import pipeline
# Load NER model
@st.cache_resource
def load_ner_model():
return pipeline("ner", grouped_entities=True)
ner_model = load_ner_model()
# Set the title with custom style
st.markdown(
"<h1 style='text-align: center; color: #4CAF50;'>Named Entity Recognition </h1>",
unsafe_allow_html=True
)
# Set up text input with a description
st.write(
"<p style='text-align: center;'>Enter your text below for entity recognition.</p>",
unsafe_allow_html=True
)
# Center the input text area
text_input = st.text_area(
"Text Input",
placeholder="Type your text here...",
height=200
)
# Customize the button and center it
button_style = """
<style>
div.stButton > button {
width: 100%;
background-color: #4CAF50;
color: white;
font-size: large;
padding: 10px;
border-radius: 8px;
}
</style>
"""
st.markdown(button_style, unsafe_allow_html=True)
# Button to trigger NER model
if st.button("Recognize Entities"):
if text_input:
with st.spinner("Processing..."):
entities = ner_model(text_input)
if entities:
st.subheader("Named Entities")
# Loop through entities and display with improved visibility
for entity in entities:
entity_html = f"""
<div style="background-color: #333333; border-radius: 8px; padding: 10px; margin: 5px 0;">
<strong style="color: #ff9800;">Entity:</strong> {entity['word']}
<br>
<strong style="color: #03a9f4;">Type:</strong> {entity['entity_group']}
<br>
<strong style="color: #8bc34a;">Confidence:</strong> {entity['score']:.2f}
</div>
"""
st.markdown(entity_html, unsafe_allow_html=True)
else:
st.write("No named entities found in the text.")
else:
st.error("Please enter some text for entity recognition.")
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