Update app.py

app.py

@@ -1,52 +1,55 @@
 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.")
+st.title("NLP Theory Blog")
+
+# Sidebar for navigation
+st.sidebar.title("Navigation")
+pages = ["Introduction to NLP", "NLP Techniques"]
+page = st.sidebar.radio("Go to:", pages)
+
+# Content for each page
+if page == "Introduction to NLP":
+    st.header("What is Natural Language Processing (NLP)?")
+    st.write("""
+        Natural Language Processing (NLP) is a field of Artificial Intelligence that focuses on the interaction between computers and humans through natural language.
+        It enables machines to understand, interpret, and respond to human language in a meaningful way.
+    
+        **Applications of NLP include:**
+        - Sentiment Analysis
+        - Machine Translation
+        - Chatbots
+        - Speech Recognition
+    
+        NLP combines computational linguistics with machine learning and deep learning techniques to process language.
+    """)
+
+elif page == "NLP Techniques":
+    st.header("Common NLP Techniques")
+    st.write("""
+        NLP involves several techniques for processing and analyzing text and speech data. Here are some key techniques:
+
+        1. **Tokenization:** Breaking text into smaller units like words or sentences.
+        2. **Stopword Removal:** Eliminating common words (e.g., 'the', 'is') that may not contribute to meaning.
+        3. **Stemming and Lemmatization:** Reducing words to their base or root form.
+        4. **Part-of-Speech (POS) Tagging:** Identifying grammatical parts of speech in a text.
+        5. **Named Entity Recognition (NER):** Extracting named entities like people, organizations, and locations.
+        6. **Sentiment Analysis:** Determining the sentiment (positive, negative, neutral) of a text.
+        7. **Text Summarization:** Producing a summary of a longer text.
+        8. **Machine Translation:** Translating text from one language to another.
+
+        These techniques are often used in combination to build sophisticated NLP applications.
+    """)
+
+# Pagination buttons
+if st.button("Previous Page"):
+    if page == "NLP Techniques":
+        st.experimental_set_query_params(page=pages[0])
+
+if st.button("Next Page"):
+    if page == "Introduction to NLP":
+        st.experimental_set_query_params(page=pages[1])
 
 # Footer
 st.sidebar.write("---")
 st.sidebar.write("Developed with ❤️ using Streamlit.")
-