Upload 2 files

pages/introduction.py

@@ -0,0 +1,39 @@
+import streamlit as st
+
+st.write("<h1><center>Intro to NLP</center></h1>",unsafe_allow_html=True)
+
+st.write("<h3>NLP stands for Natural Language Processing</h3>",unsafe_allow_html=True)
+
+st.write("<p>Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and respond to human language. It involves the interaction between computers and humans through natural language, aiming to make it easier for machines to process and analyze large amounts of natural language data.</p>",unsafe_allow_html=True)
+
+st.write("<p>NLP combines computational linguistics and machine learning to help computers perform tasks such as translating text, recognizing speech, analyzing sentiments, summarizing content, and answering questions. It allows machines to handle language in a way that is both meaningful and useful for various applications, such as chatbots, virtual assistants, and search engines.</p>",unsafe_allow_html=True)
+
+st.write("<p>By using techniques like tokenization, part-of-speech tagging, and named entity recognition, NLP makes it possible for computers to break down and understand text, allowing them to handle a wide range of tasks that involve human language. Despite its progress, NLP still faces challenges like ambiguity in language and understanding context, but it continues to evolve with advancements in AI.</p>",unsafe_allow_html=True)
+
+
+st.write("<h1><center>What is NLP?</center></h1>",unsafe_allow_html=True)
+
+# Introduction
+st.write("""
+    **Natural Language Processing (NLP)** is a field of technology that helps computers understand, interpret, 
+    and respond to human language in a way that feels natural. It allows machines to work with text or speech 
+    in a way similar to how humans do, such as reading, listening, and talking.
+""")
+
+# Core Concepts
+st.markdown("<h2 style='font-size: 20px;'>At its core, NLP involves:</h2>", unsafe_allow_html=True)
+
+st.write("""
+- **Understanding Language**: Teaching computers to understand what words mean and how sentences are formed.
+- **Text and Speech Processing**: Converting spoken or written language into something that computers can work with.
+- **Making Predictions**: Helping computers make decisions or predictions based on language, such as determining if a review is positive or negative.
+""")
+
+# Everyday Applications
+st.markdown("<h2 style='font-size: 20px;'>NLP in Everyday Applications</h2>", unsafe_allow_html=True)
+
+st.write("""
+NLP is used in everyday applications like voice assistants (e.g., Siri or Alexa), chatbots, translation tools 
+(e.g., Google Translate), and even in recommendation systems (e.g., suggesting what to watch next). 
+It’s all about making computers more "language-smart."
+""")

pages/life cycle of nlp.py

@@ -0,0 +1,568 @@
+import streamlit as st
+
+
+st.title("NLP Life Cycle")
+st.write("Click on a stage below to learn more about each step in the NLP life cycle.")
+
+# Create buttons for each stage of the NLP life cycle
+stage = st.radio(
+    "Select an NLP Life Cycle Stage:",
+    ["Data Collection", "Text Preprocessing", "Text Representation", 
+     "Model Training", "Evaluation", "Post-Processing", 
+     "Deployment", "Monitoring and Maintenance"]
+)
+
+# Display information based on the selected stage
+if stage == "Data Collection":
+    # Title of the Streamlit app
+    st.subheader("Data Collection in the NLP Life Cycle")
+
+# Introduction to Data Collection
+    st.write("""
+    Data Collection is the first step in the Natural Language Processing (NLP) life cycle. It involves gathering text or language data
+    that we will later use to teach the computer to understand and process human language.""")
+
+
+# Where Does the Data Come From?
+    st.markdown("<h2 style='font-size: 20px;'>Where Does the Data Come From?</h2>", unsafe_allow_html=True)
+
+    st.write("""
+    - **Websites**: Text can be collected from articles, blogs, and news stories on the internet.
+    - **Social Media**: Posts, tweets, and comments on platforms like Twitter or Facebook.
+    - **Books and Articles**: Text from books, journals, and research papers.
+    - **Public Datasets**: Some organizations share their data freely for research (e.g., Kaggle).
+    - **Surveys**: Data can come from questionnaires or feedback from people.
+    """)
+
+# Types of Data
+    st.markdown("<h2 style='font-size: 20px;'>Types of Data</h2>", unsafe_allow_html=True)
+
+    st.write("""
+    - **Text**: The data we collect is mostly in text form. For example, a tweet, a news article, or a customer review.
+    - **Structured or Unstructured**: Sometimes the data comes in a neat format like a spreadsheet (structured), 
+    and other times it's messy, like comments or blog posts (unstructured).
+    """)
+
+# Why Do We Need Lots of Data?
+    st.markdown("<h2 style='font-size: 20px;'>Why Do We Need Lots of Data?</h2>", unsafe_allow_html=True)
+    st.write("""
+    The more data we have, the better the computer can understand language. Just like learning a new language, the more examples 
+    you see, the better you get at understanding it.
+    For example, if you want to teach a computer to recognize happy or sad posts, you need lots of examples of both happy and sad posts.
+    """)
+
+# Labeling Data
+    st.markdown("<h2 style='font-size: 20px;'>Labeling Data (Sometimes Needed)</h2>", unsafe_allow_html=True)
+
+    st.write("""
+    If you're doing something like sentiment analysis (deciding whether a text is positive or negative), you might need to "label" the data. 
+    This means saying, "This review is positive," or "This tweet is negative."
+    """)
+
+# Being Careful with Data
+    st.markdown("<h2 style='font-size: 20px;'>Being Careful with Data</h2>", unsafe_allow_html=True)
+
+    st.write("""
+    It's important to make sure you're using data in a responsible way. For example, you shouldn’t use private or personal data without permission, 
+    and you should follow rules about privacy.
+    """)
+
+elif stage == "Text Preprocessing":
+ 
+
+# Title of the app
+    st.subheader("Text Preprocessing in the NLP Life Cycle")
+
+# 1. Text Collection
+    st.markdown("<h2 style='font-size: 20px;'>1. Text Collection</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Data Sources**: Raw text is collected from various sources like websites, books, social media, news articles, and more. This data could be noisy, unstructured, and might include irrelevant or unnecessary content.
+    **Example**: Collecting a dataset of tweets or product reviews.
+    """)
+
+# 2. Text Cleaning
+    st.markdown("<h2 style='font-size: 20px;'>2. Text Cleaning</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Remove irrelevant data and prepare the text for further processing.
+    **Steps**:
+    - **Removing Punctuation**: Punctuation marks (e.g., commas, periods, exclamation points) may not always be relevant, especially in tasks like sentiment analysis.
+    - **Removing Special Characters**: Any unnecessary special characters, symbols, or emojis that might not add value for the task are removed.
+    - **Lowercasing**: Convert all text to lowercase to ensure uniformity (e.g., "Apple" and "apple" are treated the same).
+    - **Removing Numbers**: If the numbers don't contribute to the analysis (like in a sentiment analysis task), they are removed.
+    - **Removing Whitespace**: Extra spaces and line breaks are trimmed.
+    **Example**: "I love NLP!!! 123 " → "i love nlp"
+    """)
+
+# 3. Tokenization
+    st.markdown("<h2 style='font-size: 20px;'>3. Tokenization</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Break down text into smaller units such as words, sentences, or subwords, making it easier for the model to process.
+    **Types**:
+    - **Word Tokenization**: Splits text into individual words.
+    - **Sentence Tokenization**: Splits text into sentences.
+    - **Subword Tokenization**: Splits text into smaller components (useful for languages with complex morphology).
+    **Example**:
+    - Sentence: "I love NLP."
+    - Word tokens: ["I", "love", "NLP"]
+    - Sentence tokens: ["I love NLP."]
+    """)
+
+# 4. Stop Words Removal
+    st.markdown("<h2 style='font-size: 20px;'>4. Stop Words Removal</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Remove common words that do not carry significant meaning and may add noise (e.g., "the", "is", "in", "and").
+    **Note**: This step depends on the task. In some cases, stop words may carry meaning and should be retained (e.g., in sentiment analysis).
+    **Example**:
+    - Original text: "This is a good day."
+    - After stop words removal: "good day"
+    """)
+
+# 5. Stemming
+    st.markdown("<h2 style='font-size: 20px;'>5. Stemming</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Reduce words to their base or root form, removing suffixes or prefixes. Stemming helps in reducing the complexity of the data by treating variations of the same word as a single term.
+    **Example**:
+    - "running" → "run"
+    - "better" → "better" (this is because stemming may not handle irregular forms well)
+    **Algorithms**:
+    - **Porter Stemmer**: Most common stemming algorithm.
+    - **Lancaster Stemmer**: A more aggressive stemming approach.
+    """)
+    
+# 6. Lemmatization
+    st.markdown("<h2 style='font-size: 20px;'>6. Lemmatization</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Similar to stemming, but lemmatization uses vocabulary and grammatical knowledge to convert words to their base form (lemma). Unlike stemming, lemmatization considers context and part of speech.
+    **Example**:
+    - "better" → "good"
+    - "running" → "run"
+    - "went" → "go"
+    **Tools**: Popular libraries for lemmatization include the WordNetLemmatizer in NLTK or spaCy's lemmatization.
+    """)
+
+# 7. Part of Speech Tagging (POS Tagging)
+    st.markdown("<h2 style='font-size: 20px;'>7. Part of Speech Tagging (POS Tagging)</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Identify the grammatical parts of speech for each word in the text (e.g., noun, verb, adjective). This can be helpful in tasks such as named entity recognition (NER) or syntactic parsing.
+    **Example**:
+    - "I love programming." → [('I', 'PRP'), ('love', 'VBP'), ('programming', 'NN')]
+    """)
+
+# 8. Named Entity Recognition (NER)
+    st.markdown("<h2 style='font-size: 20px;'>8. Named Entity Recognition (NER)</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Identify named entities in text such as names of people, organizations, locations, dates, etc. This is useful in tasks like information extraction or document summarization.
+    **Example**:
+    - "Apple Inc. is headquartered in Cupertino." → [('Apple Inc.', 'ORG'), ('Cupertino', 'GPE')]
+    """)
+
+# 9. Text Normalization
+    st.markdown("<h2 style='font-size: 20px;'>9. Text Normalization</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Standardize the text for further processing and reduce variations. This step may involve:
+    - **Spelling Correction**: Correcting common spelling mistakes.
+    - **Expanding Contractions**: Expanding contractions such as "I'm" to "I am", "don't" to "do not".
+    - **Text Transformation**: Transforming text for consistency (e.g., converting all words to lowercase).
+    **Example**:
+    - Original: "I'm going to the store."
+    - After normalization: "I am going to the store."
+    """)
+
+    # 10. Vectorization
+    st.markdown("<h2 style='font-size: 20px;'>10. Vectorization</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Purpose**: Convert the processed text into numerical format so that machine learning models can process it. There are several ways to convert text into vectors:
+    - **Bag of Words (BoW)**: Represents text by counting the frequency of words.
+    - **TF-IDF (Term Frequency-Inverse Document Frequency)**: Weighs words based on their importance in the context of the entire dataset.
+    - **Word Embeddings**: Represents words as dense vectors in a continuous vector space (e.g., Word2Vec, GloVe, or fastText).
+    - **Transformers (e.g., BERT embeddings)**: Pre-trained transformer models generate contextualized word representations.
+    """)
+
+    # Title of the app
+    st.subheader("Why Text Preprocessing is Important")
+
+# Displaying the reasons for text preprocessing
+
+# Noise Reduction
+    st.markdown("<h2 style='font-size: 20px;'>1. Noise Reduction</h2>", unsafe_allow_html=True)
+    st.write("""
+    Raw text data is often noisy and unstructured. Preprocessing helps remove irrelevant information and reduce noise, making the data more structured and clean for further processing.
+    """)
+
+# Model Efficiency
+    st.markdown("<h2 style='font-size: 20px;'>2. Model Efficiency</h2>", unsafe_allow_html=True)
+    st.write("""
+    Preprocessed data is easier for machine learning models to work with. Cleaning the text and normalizing it makes the features more meaningful, improving the performance and efficiency of models.
+    """)
+
+# Handling Variations
+    st.markdown("<h2 style='font-size: 20px;'>3. Handling Variations</h2>", unsafe_allow_html=True)
+    st.write("""
+    Words in natural language can have many forms (e.g., "running", "ran"). Text preprocessing techniques like lemmatization and stemming help normalize these variations, making it easier for models to handle and understand different forms of the same word.
+    """)
+
+# Improved Results
+    st.markdown("<h2 style='font-size: 20px;'>4. Improved Results</h2>", unsafe_allow_html=True)
+    st.write("""
+    Properly preprocessed data ensures that the model focuses on the most important features (words) in the data. This helps improve the overall quality of predictions, as the model is trained on cleaner, more relevant data.
+    """)
+
+
+elif stage == "Text Representation":
+
+# Title of the app
+    st.subheader("Text Representation in NLP Life Cycle")
+
+# Displaying the text representation techniques
+
+    st.markdown("<h2 style='font-size: 20px;'>1. Bag of Words (BoW)</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Definition**: This method represents text as a collection of words, disregarding grammar and word order but keeping track of the frequency of each word.
+    - **Pros**: Simple and easy to implement. Effective for tasks where word frequency matters.
+    - **Cons**: Does not capture the context or meaning of words. Very high-dimensional if the corpus is large.
+    **Example**:
+    - Sentence 1: "I love machine learning."
+    - Sentence 2: "I love deep learning."
+    After BoW transformation: ["I", "love", "machine", "learning", "deep"]
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>2. TF-IDF (Term Frequency - Inverse Document Frequency)</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Definition**: A more sophisticated version of the Bag of Words model. It weights words based on how frequently they appear in a document and how unique they are across the entire corpus.
+    - **Pros**: Helps reduce the importance of common words and gives higher weight to words that are more informative.
+    - **Cons**: Still doesn’t account for word order or context.
+    **Example**:
+    After TF-IDF transformation, frequent words like "I" and "love" will have lower scores, while "machine" and "deep" will have higher importance.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>3. Word Embeddings</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Definition**: Word embeddings are dense vector representations of words, where similar words have similar vector representations. These representations capture semantic meaning and word relationships.
+    - **Pros**: Captures semantic relationships (e.g., "king" - "man" + "woman" = "queen").
+    - **Cons**: Requires large amounts of data and computation power.
+    **Example**:
+    - "king" → [0.4, 0.2, 0.1, ...]
+    - "queen" → [0.3, 0.5, 0.2, ...]
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>4. Contextualized Word Embeddings (Transformers)</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Definition**: These embeddings are generated by models that take the context of words into account. Unlike traditional word embeddings, these embeddings are dynamic, meaning the representation of a word changes depending on its context in a sentence.
+    - **Popular Models**: BERT, GPT
+    - **Pros**: Captures word meaning based on context, making them powerful for many NLP tasks.
+    - **Cons**: Computationally expensive and requires fine-tuning for specific tasks.
+    **Example**:
+    - Sentence 1: "He went to the bank to fish."
+    - Sentence 2: "She went to the bank to withdraw money."
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>5. Sentence and Document Embeddings</h2>", unsafe_allow_html=True)
+    st.write("""
+    **Definition**: These methods represent entire sentences or documents as fixed-size vectors.
+    - **Popular Models**: Doc2Vec, Sentence-BERT
+    - **Pros**: Useful for document similarity, clustering, or classification tasks.
+    - **Cons**: Less effective at capturing fine-grained information at the word level.
+    """)
+
+
+elif stage == "Model Training":
+    
+
+# Title of the app
+    st.subheader("Model Training in the NLP Life Cycle")
+
+# Displaying the steps in model training
+
+    st.markdown("<h2 style='font-size: 20px;'>1. Choosing a Model Architecture</h2>", unsafe_allow_html=True)
+    st.write("""
+    Choose the appropriate machine learning or deep learning model depending on the task. 
+    Common models include Logistic Regression, Naive Bayes, SVM for traditional ML tasks, and RNN, LSTM, Transformers for deep learning tasks.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>2. Preparing Training Data</h2>", unsafe_allow_html=True)
+    st.write("""
+    Split the data into training, validation, and test sets. Cross-validation can also be used to evaluate performance during training.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>3. Model Initialization</h2>", unsafe_allow_html=True)
+    st.write("""
+    Initialize model parameters (weights) before training to allow learning during the process.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>4. Model Training (Fitting the Model)</h2>", unsafe_allow_html=True)
+    st.write("""
+    Train the model using optimization algorithms like gradient descent. During this phase, the model adjusts its weights based on the loss function.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>5. Hyperparameter Tuning</h2>", unsafe_allow_html=True)
+    st.write("""
+    Tune the model’s hyperparameters (e.g., learning rate, number of layers) to achieve better performance using techniques like grid search or Bayesian optimization.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>6. Regularization</h2>", unsafe_allow_html=True)
+    st.write("""
+    Apply regularization techniques (e.g., L1, L2, dropout) to prevent overfitting.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>7. Evaluation During Training</h2>", unsafe_allow_html=True)
+    st.write("""
+    Monitor performance on the validation set using metrics such as accuracy, precision, recall, and loss to prevent overfitting.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>8. Model Evaluation (Post-Training)</h2>", unsafe_allow_html=True)
+    st.write("""
+    After training, evaluate the model on the test set to measure its generalization ability.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>9. Model Saving and Deployment</h2>", unsafe_allow_html=True)
+    st.write("""
+    Save the trained model and deploy it into production for real-time or batch predictions.
+    """)
+
+    st.subheader("Importance of Model Training in NLP")
+
+# Displaying the importance of model training
+
+    st.markdown("<h2 style='font-size: 20px;'>1. Learning from Data</h2>", unsafe_allow_html=True)
+    st.write("""
+    Model training allows the system to learn patterns and relationships from the data,making predictions, classifications, and other language tasks possible.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>2. Performance Improvement</h2>", unsafe_allow_html=True)
+    st.write("""
+    Well-trained models that are tuned correctly can yield high performance, providing valuable insights and predictions.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>3. Generalization</h2>", unsafe_allow_html=True)
+    st.write("""
+    The goal is to train a model that generalizes well to new, unseen data and avoids overfitting to the training data.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>4. Real-world Impact</h2>", unsafe_allow_html=True)
+    st.write("""
+    Once deployed, trained models are used to automate tasks, like sentiment analysis, text summarization, machine translation, etc.
+    """)
+
+
+elif stage == "Evaluation":
+    
+
+# Title of the app
+    st.subheader("Evaluation in the NLP Life Cycle")
+
+# Displaying the steps in evaluation
+
+    st.markdown("<h2 style='font-size: 20px;'>1. Purpose of Evaluation</h2>", unsafe_allow_html=True)
+    st.write("""
+    Evaluate the model's performance, ensure generalization, and fine-tune the model to achieve optimal results. 
+    This helps in selecting the best-performing model for the task.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>2. Splitting Data for Evaluation</h2>", unsafe_allow_html=True)
+    st.write("""
+    Split the dataset into training, validation, and test sets. Cross-validation can also be used for better evaluation.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>3. Evaluation Metrics</h2>", unsafe_allow_html=True)
+    st.write("""
+    Use appropriate evaluation metrics like Accuracy, Precision, Recall, F1-Score for classification tasks,BLEU, ROUGE for generation tasks, and MAE/MSE for regression tasks.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>4. Model Evaluation Techniques</h2>", unsafe_allow_html=True)
+    st.write("""
+    Use methods like Hold-out, Cross-validation, and confusion matrix analysis to evaluate the model's performance.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>5. Handling Evaluation Failures</h2>", unsafe_allow_html=True)
+    st.write("""
+    Perform error analysis, address bias in data, and consider metrics like Precision, Recall, and F1-Score for imbalanced datasets.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>6. Post-Evaluation Steps</h2>", unsafe_allow_html=True)
+    st.write("""
+    After evaluation, improve the model, use ensemble methods, and prepare for deployment in production.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>7. Continuous Evaluation and Monitoring</h2>", unsafe_allow_html=True)
+    st.write("""
+    Continuously monitor the model’s performance in production, retraining it as needed to handle new data patterns.
+    """)
+
+
+# Title of the app
+    st.subheader("Importance of Evaluation in NLP")
+
+# Displaying the importance of evaluation
+
+    st.markdown("<h2 style='font-size: 20px;'>1. Ensures Quality</h2>", unsafe_allow_html=True)
+    st.write("""
+    Evaluation ensures that the model performs well on unseen data, which is essential for real-world applications. 
+    This is crucial because it guarantees that the model can generalize to new data and not just memorize the training set.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>2. Guides Model Selection</h2>", unsafe_allow_html=True)
+    st.write("""
+    Evaluation helps choose the best model from several candidates and ensures it is the most appropriate for the task. 
+    It allows us to compare models and select the one that offers the best performance.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>3. Avoids Overfitting</h2>", unsafe_allow_html=True)
+    st.write("""
+    Evaluating the model on separate data (validation/test sets) helps ensure that the model does not memorize the training data, but generalizes well to new, unseen data. This is important for avoiding overfitting, where the model is too specific to the training set.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>4. Informs Model Improvement</h2>", unsafe_allow_html=True)
+    st.write("""
+    Evaluation metrics guide the process of improving the model by revealing areas where it is underperforming or biased. 
+    By analyzing the results, we can understand the model’s strengths and weaknesses, leading to better performance in the future.
+    """)
+
+
+elif stage == "Post-Processing":
+
+
+# Title of the app
+    st.subheader("Post-Processing in the NLP Life Cycle")
+
+# Displaying the importance of post-processing
+
+    st.markdown("<h2 style='font-size: 20px;'>1. Purpose of Post-Processing</h2>", unsafe_allow_html=True)
+    st.write("""
+    Post-processing ensures that the output is refined, readable, and suitable for its intended use. 
+    It improves the quality of the model's predictions and ensures coherence in the final output.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>2. Common Post-Processing Steps</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Text Formatting**: Adjusting sentence structure and adding proper punctuation.
+    - **Capitalization and Punctuation**: Correcting capitalization and adding punctuation to the output text.
+    - **Spelling and Grammar Correction**: Ensuring the text has no spelling or grammatical mistakes.
+    - **Named Entity Correction**: Fixing incorrect named entity recognition.
+    - **Filtering/Removing Irrelevant Information**: Removing unnecessary data or context from the output.
+    - **Handling Repetition**: Removing redundant text in generative models.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>3. Post-Processing for Specific NLP Tasks</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Text Summarization**: Refining summaries for readability and coherence.
+    - **Machine Translation**: Correcting sentence structure and grammar in translations.
+    - **Named Entity Recognition (NER)**: Correcting or formatting extracted named entities.
+    - **Text Generation**: Eliminating irrelevant or repetitive content and improving logical flow.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>4. Post-Processing Tools and Techniques</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Text Normalization**: Correcting spelling mistakes, formatting issues, and standardizing the output.
+    - **Rule-based Systems**: Predefined rules to fix common issues.
+    - **Automated Tools**: Tools like `spaCy`, `language_tool`, and `Ginger Software` for spelling and grammar correction.
+    - **Regex (Regular Expressions)**: Custom processing such as date formatting and text extraction.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>5. Importance of Post-Processing</h2>", unsafe_allow_html=True)
+    st.write("""
+    Post-processing improves user experience, accuracy, consistency, and customization, ensuring that the output text is clear, coherent, and tailored to the application.
+    """)
+    
+
+elif stage == "Deployment":
+
+# Title of the app
+    st.subheader("Deployment in the NLP Life Cycle")
+
+# Displaying the importance of deployment
+
+    st.markdown("<h2 style='font-size: 20px;'>1. Purpose of Deployment</h2>", unsafe_allow_html=True)
+    st.write("""
+    Deployment allows the model to be used in real-world applications, ensuring continuous monitoring, updates, and scalability to handle production traffic.
+    """)
+
+    st.markdown("<h2 style='font-size: 20px;'>2. Deployment Steps</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Model Export**: Exporting the trained model in a suitable format for deployment.
+    - **Setting Up Infrastructure**: Setting up servers or cloud platforms to host the model.
+    - **API Integration**: Wrapping the model into an API for easy access.
+    - **Batch vs. Real-Time Processing**: Deciding between batch processing or real-time prediction.
+    - **Model Serving**: Using tools like TensorFlow Serving to serve the model.
+    - **Scaling and Load Balancing**: Ensuring the model can scale to handle high traffic.
+    """)
+    
+    st.markdown("<h2 style='font-size: 20px;'>3. Monitoring and Maintenance</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Model Monitoring**: Tracking model performance, latency, and accuracy.
+    - **A/B Testing**: Comparing different model versions to improve performance.
+    - **Model Updates**: Retraining and redeploying models with new data.
+    """)
+    
+    st.markdown("<h2 style='font-size: 20px;'>4. Challenges in Deployment</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Latency**: Ensuring low latency in real-time applications.
+    - **Scalability**: Handling large traffic and scaling the deployment.
+    - **Data Privacy and Security**: Protecting sensitive data and complying with regulations.
+    - **Model Drift**: Monitoring and updating models to avoid performance degradation.
+    - **Integration with Existing Systems**: Ensuring smooth integration with other systems.
+    """)
+
+
+elif stage == "Monitoring and Maintenance":
+
+
+# Title of the app
+    st.subheader("Monitoring and Maintenance in the NLP Life Cycle")
+
+# Purpose of Monitoring and Maintenance
+    st.markdown("<h2 style='font-size: 20px;'>1. Purpose of Monitoring and Maintenance</h2>", unsafe_allow_html=True)
+    st.write("""
+    Monitoring ensures the model performs well over time, adapts to new data, and detects potential performance issues like model drift. 
+    Maintenance involves retraining the model and handling changes in input data to keep the model relevant.
+    """)
+
+# Key Aspects of Monitoring and Maintenance
+    st.markdown("<h2 style='font-size: 20px;'>2. Key Aspects of Monitoring and Maintenance</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Model Performance Metrics**: Accuracy, precision, recall, and latency are tracked to ensure the model's performance is on target.
+    - **Model Drift Detection**: Detect changes in data or relationships between data and predictions (concept and data drift).
+    - **Performance Thresholds**: Define thresholds for performance metrics to trigger model updates or retraining.
+    """)
+
+# Model Retraining and Updates
+    st.markdown("<h2 style='font-size: 20px;'>3. Model Retraining and Updates</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Continuous Learning**: Retraining the model periodically or incrementally as new data is available.
+    - **Batch vs. Online Learning**: Choose between retraining on fixed data batches or updating the model incrementally.
+    - **Model Fine-Tuning**: Adjusting model parameters without full retraining to handle evolving data.
+    """)
+
+# Automated Monitoring Systems
+    st.markdown("<h2 style='font-size: 20px;'>4. Automated Monitoring Systems</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Monitoring Tools**: Use tools like Prometheus, Grafana, and cloud services for tracking model performance.
+    - **Log Management**: Collect and analyze logs to detect issues and ensure system reliability.
+    """)
+    
+# Handling Changes in Input Data
+    st.markdown("<h2 style='font-size: 20px;'>5. Handling Changes in Input Data</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Data Validation**: Continuously validate incoming data for quality.
+    - **Feature Engineering**: Update features or preprocessing steps based on new patterns in the data.
+    """)
+    
+# Scalability and Load Balancing
+    st.markdown("<h2 style='font-size: 20px;'>6. Scalability and Load Balancing</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Performance Under Load**: Ensure the model can handle increased traffic.
+    - **Server Health**: Monitor infrastructure health to avoid bottlenecks.
+    """)
+
+# User Feedback and Retraining
+    st.markdown("<h2 style='font-size: 20px;'>7. User Feedback and Retraining</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Human-in-the-Loop**: Incorporate user feedback for model improvement.
+    - **Continuous Evaluation**: Use feedback to understand real-world usage and improve the model.
+    """)
+
+# Cost Management
+    st.markdown("<h2 style='font-size: 20px;'>8. Cost Management</h2>", unsafe_allow_html=True)
+    st.write("""
+    - **Resource Utilization**: Monitor resource usage to optimize costs.
+    - **Efficient Infrastructure**: Use scalable and cost-effective cloud resources or hardware accelerators.
+    """)