Update app.py

app.py

@@ -41,25 +41,6 @@ The NLP lifecycle consists of several stages, each contributing to transforming
 
 These stages are crucial for building effective NLP applications that provide value to users.
 """)
-
-# NLP Techniques
-st.header('NLP Techniques')
-st.write("""
-Some key techniques used in NLP include:
-
-- **Tokenization**: The process of breaking down text into smaller units, such as words or sentences.
-- **Stop Word Removal**: The process of removing common words (e.g., "the", "a", "and") that do not contribute significant meaning to the text.
-- **Stemming**: Reducing words to their root form (e.g., "running" → "run").
-- **Lemmatization**: Similar to stemming but more accurate, reducing words to their dictionary form (e.g., "better" → "good").
-- **Named Entity Recognition (NER)**: Identifying entities such as people, organizations, and locations within text.
-- **Part-of-Speech Tagging**: Identifying the grammatical structure of words in a sentence, such as nouns, verbs, adjectives, etc.
-- **Word Embeddings**: A technique that maps words into continuous vector space, capturing semantic relationships between words (e.g., Word2Vec, GloVe).
-- **Text Classification**: Categorizing text into predefined labels or categories (e.g., spam detection, sentiment analysis).
-- **Sentiment Analysis**: Determining the sentiment expressed in a text, such as whether it is positive, negative, or neutral.
-
-These techniques are the building blocks for solving various NLP tasks and are essential for developing applications that can understand human language.
-""")
-
 # Define the available NLP lifecycle stages
 lifecycle_stages = ['Data Collection', 'Text Preprocessing', 'Text Representation', 
                     'Model Training', 'Evaluation', 'Deployment']
@@ -164,6 +145,25 @@ elif selected_page == 'Deployment':
     - Retraining may be required periodically to account for changes in language usage or new trends in the data.
     """)
 
+# NLP Techniques
+st.header('NLP Techniques')
+st.write("""
+Some key techniques used in NLP include:
+
+- **Tokenization**: The process of breaking down text into smaller units, such as words or sentences.
+- **Stop Word Removal**: The process of removing common words (e.g., "the", "a", "and") that do not contribute significant meaning to the text.
+- **Stemming**: Reducing words to their root form (e.g., "running" → "run").
+- **Lemmatization**: Similar to stemming but more accurate, reducing words to their dictionary form (e.g., "better" → "good").
+- **Named Entity Recognition (NER)**: Identifying entities such as people, organizations, and locations within text.
+- **Part-of-Speech Tagging**: Identifying the grammatical structure of words in a sentence, such as nouns, verbs, adjectives, etc.
+- **Word Embeddings**: A technique that maps words into continuous vector space, capturing semantic relationships between words (e.g., Word2Vec, GloVe).
+- **Text Classification**: Categorizing text into predefined labels or categories (e.g., spam detection, sentiment analysis).
+- **Sentiment Analysis**: Determining the sentiment expressed in a text, such as whether it is positive, negative, or neutral.
+
+These techniques are the building blocks for solving various NLP tasks and are essential for developing applications that can understand human language.
+""")
+
+
 # Define the available NLP tasks
 tasks = ['Text Classification', 'Sentiment Analysis', 'Named Entity Recognition (NER)', 
          'Language Translation', 'Text Summarization', 'Part-of-Speech Tagging',