Update pages/Basics Of NLP.py

pages/Basics Of NLP.py

@@ -12,8 +12,6 @@ nltk.download("stopwords")
 nltk.download("wordnet")
 nltk.download("vader_lexicon")
 
-# Load spaCy model
-nlp = spacy.load("en_core_web_sm")
 
 # Streamlit app configuration
 st.set_page_config(page_title="NLP Basics", page_icon="🤖", layout="wide")
@@ -40,7 +38,7 @@ if not text_input.strip():
     st.warning("Please enter some text to explore NLP concepts.")
 
 # NLP Processing Sections
-tabs = st.tabs(["Tokenization", "Stopwords", "Lemmatization & Stemming", "Bag of Words (BoW)", "TF-IDF", "Named Entity Recognition", "Sentiment Analysis", "Text Summarization", "Word Cloud Visualization"])
+tabs = st.tabs(["Tokenization", "Stopwords", "Lemmatization & Stemming", "Bag of Words (BoW)", "TF-IDF"])
 
 # Tokenization
 with tabs[0]:
@@ -103,40 +101,6 @@ with tabs[4]:
         st.write("**TF-IDF Matrix:**", tfidf_matrix.toarray())
         st.write("**Feature Names:**", tfidf_vectorizer.get_feature_names_out())
 
-# Named Entity Recognition (NER)
-with tabs[5]:
-    st.header("🏷 Named Entity Recognition (NER)")
-    if text_input.strip():
-        doc = nlp(text_input)
-        entities = [(ent.text, ent.label_) for ent in doc.ents]
-        st.write("**Named Entities:**", entities)
-
-# Sentiment Analysis
-with tabs[6]:
-    st.header("😊 Sentiment Analysis")
-    if text_input.strip():
-        sia = SentimentIntensityAnalyzer()
-        sentiment_scores = sia.polarity_scores(text_input)
-        st.write("**Sentiment Scores:**", sentiment_scores)
-
-# Text Summarization
-with tabs[7]:
-    st.header("📝 Text Summarization")
-    if text_input.strip():
-        parser = PlaintextParser.from_string(text_input, Tokenizer("english"))
-        summarizer = LsaSummarizer()
-        summary = summarizer(parser.document, 3)  # Summarize into 3 sentences
-        st.write("**Summary:**", " ".join([str(s) for s in summary]))
-
-# Word Cloud Visualization
-with tabs[8]:
-    st.header("☁️ Word Cloud Visualization")
-    if text_input.strip():
-        wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text_input)
-        fig, ax = plt.subplots()
-        ax.imshow(wordcloud, interpolation='bilinear')
-        ax.axis("off")
-        st.pyplot(fig)
 
 # Footer
 st.markdown("---")