Update Intro.py

Intro.py

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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.")