Update app.py

app.py

@@ -1,215 +1,221 @@
 import streamlit as st
 
-# App title
-st.title("NLP Theory Blog")
-
-# Sidebar for navigation
-st.sidebar.title("Navigation")
-pages = ["Introduction to NLP", "NLP Life Cycle", "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.
+# Title of the app
+st.title('Natural Language Processing (NLP) Overview')
+
+# Introduction to NLP
+st.header('Introduction to Natural Language Processing (NLP)')
+st.write("""
+Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables machines to understand, 
+interpret, and generate human language. NLP is used in a wide variety of applications, such as chatbots, search engines, 
+translation systems, and voice assistants.
+
+Some common NLP tasks include:
+- Text Classification
+- Sentiment Analysis
+- Named Entity Recognition (NER)
+- Language Translation
+- Text Summarization
+- Part-of-Speech Tagging
+
+### Importance of NLP:
+- **Automation of manual tasks**: NLP is widely used to automate tasks such as document categorization, content summarization, and sentiment analysis.
+- **Understanding and generating human language**: NLP allows machines to understand the meaning behind words, sentences, and paragraphs, making human-machine interactions more natural.
+""")
+
+# Define the available NLP lifecycle stages
+lifecycle_stages = ['Data Collection', 'Text Preprocessing', 'Text Representation', 
+                    'Model Training', 'Evaluation', 'Deployment']
+
+# Add a selectbox for the user to choose a lifecycle stage
+selected_lifecycle_stage = st.selectbox('Choose an NLP Lifecycle Stage:', lifecycle_stages)
+
+# Define the pages for each NLP lifecycle stage
+if selected_lifecycle_stage == 'Data Collection':
+    st.write("""
+    ### Data Collection:
+    The first stage of the NLP lifecycle involves gathering text data from various sources such as:
+    - Social media posts
+    - Websites and blogs
+    - News articles
+    - Customer reviews
+    - Books and papers
     
-        **Applications of NLP include:**
-        - Sentiment Analysis
-        - Machine Translation
-        - Chatbots
-        - Speech Recognition
+    **Example**: Collecting customer feedback from surveys or scraping news articles to analyze sentiment.
     
-        NLP combines computational linguistics with machine learning and deep learning techniques to process language.
-    """)
-
-elif page == "NLP Life Cycle":
-    st.header("NLP Life Cycle")
-    
-    st.subheader("1. Problem Definition")
-    st.write("""
-        In this phase, the problem you're trying to solve with NLP is defined. Examples include:
-        - Sentiment analysis
-        - Named entity recognition (NER)
-        - Text classification
-        - Machine translation
-        - Language generation
-    """)
-
-    st.subheader("2. Data Collection")
-    st.write("""
-        Gather relevant textual data. Sources include:
-        - Web scraping (e.g., using BeautifulSoup or Scrapy)
-        - APIs (e.g., Twitter API)
-        - Pre-existing datasets (e.g., Kaggle, UCI repositories)
-        - User-generated content (e.g., reviews, social media)
-    """)
-
-    st.subheader("3. Data Preprocessing")
-    st.write("""
-        Prepare the data for modeling by performing tasks such as:
-        - Text cleaning (removing unnecessary characters, punctuation)
-        - Tokenization (splitting text into words/sentences)
-        - Stopword removal
-        - Stemming or lemmatization
-        - Part-of-speech tagging
+    **Key Points**:
+    - Data must be relevant to the task you are solving (e.g., sentiment analysis, text classification).
+    - The data can be structured (e.g., databases) or unstructured (e.g., plain text from websites).
     """)
 
-    st.subheader("4. Feature Engineering")
+elif selected_lifecycle_stage == 'Text Preprocessing':
     st.write("""
-        Convert text data into numerical form for model consumption:
-        - Bag of Words (BoW)
-        - TF-IDF (Term Frequency-Inverse Document Frequency)
-        - Word embeddings (Word2Vec, GloVe)
-        - Contextual embeddings (BERT, GPT)
-    """)
-
-    st.subheader("5. Modeling")
-    st.write("""
-        Train machine learning or deep learning models using the preprocessed text data:
-        - Supervised learning (e.g., Logistic Regression, SVM)
-        - Unsupervised learning (e.g., K-means clustering)
-        - Deep learning (e.g., RNNs, LSTMs, BERT)
+    ### Text Preprocessing:
+    Text preprocessing is essential for preparing raw text data for analysis. The steps involved include:
+    - **Tokenization**: Breaking text into smaller units like words or sentences.
+    - **Removing Stop Words**: Stop words (e.g., "the", "a", "is") are common words that don't carry much information and are often removed.
+    - **Stemming**: Reducing words to their base or root form (e.g., "running" → "run").
+    - **Lemmatization**: Similar to stemming but more accurate, it reduces words to their dictionary form (e.g., "better" → "good").
+    - **Lowercasing**: Converting all text to lowercase to avoid treating the same word in different cases (e.g., "Hello" vs "hello").
+    - **Removing Special Characters**: Eliminating punctuation marks, numbers, and other non-alphabetic characters that may not contribute to the analysis.
+    
+    **Key Points**:
+    - Preprocessing is crucial for reducing noise in the text, ensuring that the machine learning models focus on the important features.
     """)
 
-    st.subheader("6. Model Evaluation")
+elif selected_lifecycle_stage == 'Text Representation':
     st.write("""
-        Evaluate the model's performance using metrics like:
-        - Accuracy
-        - Precision, Recall, F1-Score
-        - Confusion Matrix
-        - Cross-validation
+    ### Text Representation:
+    After preprocessing, text needs to be converted into a numerical form for machine learning algorithms. 
+    The common techniques for text representation include:
+    - **Bag of Words (BoW)**: Converts text into a matrix of word frequencies.
+    - **TF-IDF (Term Frequency - Inverse Document Frequency)**: A statistical method to evaluate the importance of a word within a document relative to a collection of documents.
+    - **Word Embeddings**: Maps words to dense vectors, preserving semantic meaning (e.g., Word2Vec, GloVe, FastText).
+    
+    **Key Points**:
+    - BoW and TF-IDF are more traditional methods, while word embeddings capture semantic relationships and are widely used in modern NLP tasks.
     """)
 
-    st.subheader("7. Model Optimization")
+elif selected_lifecycle_stage == 'Model Training':
     st.write("""
-        Improve model performance by:
-        - Hyperparameter tuning (e.g., grid search)
-        - Regularization (e.g., L2 regularization, dropout)
-        - Ensemble methods (e.g., Random Forest, XGBoost)
+    ### Model Training:
+    In the model training stage, machine learning algorithms are used to train a model on the preprocessed and represented data. 
+    The choice of model depends on the task at hand. For example:
+    - For **text classification**, algorithms like Naive Bayes, SVM, or neural networks are commonly used.
+    - For **named entity recognition (NER)**, sequence models such as CRF (Conditional Random Fields) or LSTM (Long Short-Term Memory) can be used.
+    - For **sentiment analysis**, simple models like logistic regression or complex models like BERT can be employed.
+    
+    **Key Points**:
+    - The choice of model depends on the task (e.g., classification, sequence generation, summarization).
+    - The model learns patterns and relationships in the text data, which it will use to make predictions.
     """)
 
-    st.subheader("8. Model Deployment")
+elif selected_lifecycle_stage == 'Evaluation':
     st.write("""
-        Deploy the trained model into production:
-        - Expose the model via APIs (using Flask or FastAPI)
-        - Integrate with applications (e.g., chatbots, recommendation systems)
-        - Monitor the model's performance
+    ### Evaluation:
+    Once a model is trained, it is evaluated to understand its performance. Common evaluation metrics include:
+    - **Accuracy**: The proportion of correct predictions.
+    - **Precision**: The ratio of correctly predicted positive observations to the total predicted positives.
+    - **Recall**: The ratio of correctly predicted positive observations to the total actual positives.
+    - **F1-Score**: The weighted average of precision and recall.
+    - **ROC and AUC**: Performance measurement for classification problems.
+    
+    **Key Points**:
+    - Evaluation helps determine if the model is overfitting (memorizing the training data) or underfitting (not learning the data properly).
+    - It ensures that the model will perform well on unseen data (real-world applications).
     """)
 
-    st.subheader("9. Post-Deployment Maintenance")
+elif selected_lifecycle_stage == 'Deployment':
     st.write("""
-        Keep the model updated with new data:
-        - Retraining the model with fresh data
-        - Error analysis and model refinement
-        - Collecting user feedback for continuous improvement
+    ### Deployment:
+    The final stage is deploying the trained model for real-time use. The model can be integrated into applications like:
+    - Chatbots for customer service
+    - Sentiment analysis for social media monitoring
+    - Language translation systems
+    - Search engines for better query results
+    
+    **Key Points**:
+    - Continuous monitoring and maintenance are necessary to ensure that the model stays effective over time, especially as new data comes in.
+    - Retraining may be required periodically to account for changes in language usage or new trends in the data.
     """)
 
-    st.subheader("10. End-User Interaction")
-    st.write("""
-        Present the model's results in an understandable way:
-        - Data visualization (e.g., charts, word clouds)
-        - Interactive dashboards (e.g., using Streamlit or Dash)
-        - Interface design (e.g., web or mobile apps)
-    """)
+# Define the available NLP tasks
+tasks = ['Text Classification', 'Sentiment Analysis', 'Named Entity Recognition (NER)', 
+         'Language Translation', 'Text Summarization', 'Part-of-Speech Tagging', 
+         'Text Generation', 'Text Similarity']
 
-elif page == "NLP Techniques":
-    st.header("Common NLP Techniques")
+# Add a selectbox for the user to choose an NLP task
+selected_task = st.selectbox('Choose an NLP Task:', tasks)
 
-    st.subheader("1. Tokenization")
+# Define the pages for each NLP task
+if selected_task == 'Text Classification':
     st.write("""
-        Tokenization is the process of breaking text into smaller units like words, phrases, or sentences. This is a crucial first step in many NLP tasks.
-
-        **Example:**
-        Text: "Natural Language Processing is amazing!"
-        Tokenized text: ["Natural", "Language", "Processing", "is", "amazing"]
-
-        Tokenization helps in making the text more manageable and ready for further processing.
+    ### Text Classification:
+    Text Classification is the task of categorizing text into predefined labels. 
+    This can be used for spam detection, topic categorization, etc.
+    **Example**: Categorizing news articles into topics like 'Sports', 'Politics', etc.
+    
+    **Techniques**:
+    - Bag of Words (BoW)
+    - TF-IDF
+    - Word Embeddings
     """)
 
-    st.subheader("2. Stopword Removal")
+elif selected_task == 'Sentiment Analysis':
     st.write("""
-        Stopword removal involves eliminating common words (e.g., 'the', 'is', 'in') that may not contribute significantly to the meaning of the text.
-
-        **Example:**
-        Text: "The quick brown fox jumps over the lazy dog."
-        After stopword removal: ["quick", "brown", "fox", "jumps", "lazy", "dog"]
-
-        Removing stopwords helps reduce the size of the dataset and focuses on meaningful terms.
+    ### Sentiment Analysis:
+    Sentiment Analysis determines the sentiment of a given text, such as whether it is positive, negative, or neutral.
+    **Example**: Analyzing product reviews to determine customer satisfaction.
+    
+    **Techniques**:
+    - Lexicon-based (e.g., VADER)
+    - Machine Learning (e.g., Naive Bayes, SVM)
     """)
 
-    st.subheader("3. Stemming and Lemmatization")
+elif selected_task == 'Named Entity Recognition (NER)':
     st.write("""
-        Both stemming and lemmatization are techniques for reducing words to their base or root form.
-
-        - **Stemming**: Cuts off prefixes or suffixes. For example, "running" becomes "run".
-        - **Lemmatization**: Uses a dictionary to find the base form of a word. For example, "better" becomes "good".
-
-        **Example:**
-        Word: "running"
-        - Stemming: "run"
-        - Lemmatization: "run"
+    ### Named Entity Recognition (NER):
+    NER is the process of identifying named entities in text, such as people, organizations, dates, locations, etc.
+    **Example**: Extracting names of people and organizations from news articles.
+    
+    **Techniques**:
+    - Rule-based NER
+    - Machine Learning-based NER (e.g., CRF, LSTM)
     """)
 
-    st.subheader("4. Part-of-Speech (POS) Tagging")
+elif selected_task == 'Language Translation':
     st.write("""
-        Part-of-speech tagging assigns a part-of-speech label (e.g., noun, verb, adjective) to each word in a sentence.
-
-        **Example:**
-        Text: "The cat sat on the mat."
-        POS tags: [("The", "DT"), ("cat", "NN"), ("sat", "VBD"), ("on", "IN"), ("the", "DT"), ("mat", "NN")]
-
-        POS tagging is useful for tasks like Named Entity Recognition (NER) or syntactic parsing.
+    ### Language Translation:
+    Language Translation involves translating text from one language to another.
+    **Example**: Translating a sentence from English to Spanish.
+    
+    **Techniques**:
+    - Statistical Machine Translation (SMT)
+    - Neural Machine Translation (NMT)
     """)
 
-    st.subheader("5. Named Entity Recognition (NER)")
+elif selected_task == 'Text Summarization':
     st.write("""
-        Named Entity Recognition (NER) is the task of identifying named entities such as people, organizations, locations, etc., in text.
-
-        **Example:**
-        Text: "Apple is looking to buy a startup in London."
-        NER output: [("Apple", "ORG"), ("London", "LOC")]
-
-        NER is crucial for information extraction tasks like identifying company names or locations in a text.
+    ### Text Summarization:
+    Text Summarization involves condensing long pieces of text into a shorter, meaningful version.
+    **Example**: Generating a summary of a long article.
+    
+    **Techniques**:
+    - Extractive Summarization
+    - Abstractive Summarization
     """)
 
-    st.subheader("6. Sentiment Analysis")
+elif selected_task == 'Part-of-Speech Tagging':
     st.write("""
-        Sentiment Analysis is the process of determining the sentiment expressed in a text, typically classified as positive, negative, or neutral.
-
-        **Example:**
-        Text: "I love this phone, it's amazing!"
-        Sentiment: Positive
-
-        Sentiment analysis is widely used in social media monitoring, customer feedback analysis, and product reviews.
+    ### Part-of-Speech (POS) Tagging:
+    POS Tagging involves identifying the grammatical components of a sentence, such as nouns, verbs, adjectives, etc.
+    **Example**: Tagging words in a sentence: 'I am learning NLP' -> [('I', 'PRP'), ('am', 'VBP'), ('learning', 'VBG'), ('NLP', 'NN')]
+    
+    **Techniques**:
+    - Rule-based POS Tagging
+    - Machine Learning-based POS Tagging (e.g., HMM, CRF)
     """)
 
-    st.subheader("7. Text Summarization")
+elif selected_task == 'Text Generation':
     st.write("""
-        Text summarization generates a shorter version of a given text, maintaining the most important information.
-
-        - **Extractive summarization**: Extracts important sentences from the original text.
-        - **Abstractive summarization**: Generates new sentences to summarize the original content.
-
-        **Example (Extractive):**
-        Original text: "Natural Language Processing is a subfield of AI. It deals with how computers understand human language."
-        Summarized: "NLP is a subfield of AI that deals with human language."
-
-        Text summarization helps in condensing large documents into key points.
+    ### Text Generation:
+    Text Generation is the task of generating new, coherent text based on some input.
+    **Example**: Generating a paragraph based on a given topic or generating captions for images.
+    
+    **Techniques**:
+    - RNN (Recurrent Neural Networks)
+    - LSTM (Long Short-Term Memory)
+    - Transformer-based models (e.g., GPT-3)
     """)
 
-    st.subheader("8. Machine Translation")
+elif selected_task == 'Text Similarity':
     st.write("""
-        Machine Translation is the task of translating text from one language to another.
-
-        **Example:**
-        Text in English: "Hello, how are you?"
-        Translated text in Spanish: "Hola, ¿cómo estás?"
-
-        Machine translation systems like Google Translate use deep learning models to produce translations.
+    ### Text Similarity:
+    Text Similarity involves measuring the similarity between two pieces of text.
+    **Example**: Comparing two sentences to see if they convey the same meaning.
+    
+    **Techniques**:
+    - Cosine Similarity
+    - Jaccard Similarity
+    - Semantic-based methods (e.g., using embeddings like Word2Vec, BERT)
     """)
-
-# Footer
-st.sidebar.write("---")
-st.sidebar.write("Developed with ❤️ using Streamlit.")