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import streamlit as st
from textblob import TextBlob
import spacy
from collections import Counter
# Load Spacy model
nlp = spacy.load("en_core_web_sm")
# App title
st.title("NLP Blog with Sidebar and Buttons")
# Sidebar options
st.sidebar.title("Select NLP Task")
task = st.sidebar.selectbox("Choose a task:", ["Sentiment Analysis", "Keyword Extraction", "Named Entity Recognition (NER)"])
# Input text area
st.write("Enter text for analysis below:")
user_text = st.text_area("Input your text here:", height=200)
# Buttons
if st.button("Analyze"):
if user_text.strip():
if task == "Sentiment Analysis":
# Perform sentiment analysis
blob = TextBlob(user_text)
sentiment = blob.sentiment
st.subheader("Sentiment Analysis Result")
st.write(f"Polarity: {sentiment.polarity:.2f}")
st.write(f"Subjectivity: {sentiment.subjectivity:.2f}")
elif task == "Keyword Extraction":
# Extract keywords
doc = nlp(user_text)
keywords = [token.text for token in doc if token.is_alpha and not token.is_stop]
most_common_keywords = Counter(keywords).most_common(10)
st.subheader("Keyword Extraction Result")
st.write("Most Common Keywords:")
st.write(most_common_keywords)
elif task == "Named Entity Recognition (NER)":
# Perform Named Entity Recognition
doc = nlp(user_text)
st.subheader("Named Entity Recognition Result")
for ent in doc.ents:
st.write(f"Entity: {ent.text}, Label: {ent.label_}")
else:
st.error("Please enter some text for analysis.")
# Footer
st.sidebar.write("---")
st.sidebar.write("Developed with ❤️ using Streamlit.")
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