Update app.py

app.py

@@ -1,5 +1,9 @@
 import streamlit as st
 
+# Function to redirect to different pages
+def redirect_to_page(page):
+    st.experimental_set_query_params(page=page)
+
 # Title of the app
 st.title('Natural Language Processing (NLP) Overview')
 
@@ -23,6 +27,39 @@ Some common NLP tasks include:
 - **Understanding and generating human language**: NLP allows machines to understand the meaning behind words, sentences, and paragraphs, making human-machine interactions more natural.
 """)
 
+# NLP Lifecycle
+st.header('NLP Lifecycle')
+st.write("""
+The NLP lifecycle consists of several stages, each contributing to transforming raw text into useful insights or predictions. Here are the stages of the NLP lifecycle:
+
+1. **Data Collection**: Collect text data from various sources such as websites, social media, surveys, etc.
+2. **Text Preprocessing**: Clean and preprocess the text data, removing unnecessary information like stopwords, punctuation, etc.
+3. **Text Representation**: Convert the preprocessed text into numerical form using methods like Bag of Words (BoW), TF-IDF, or Word Embeddings.
+4. **Model Training**: Train machine learning models on the text data to solve the NLP problem, such as classification or entity recognition.
+5. **Evaluation**: Assess the model's performance using evaluation metrics like accuracy, precision, recall, and F1-score.
+6. **Deployment**: Deploy the trained model to a real-world application, such as a chatbot or sentiment analysis tool, and continuously monitor and retrain the model as needed.
+
+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']
@@ -30,8 +67,16 @@ lifecycle_stages = ['Data Collection', 'Text Preprocessing', 'Text Representatio
 # 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':
+# If lifecycle stage is selected, update query params and display new content
+if selected_lifecycle_stage:
+    redirect_to_page(selected_lifecycle_stage)
+
+# Get the page from the query params
+params = st.experimental_get_query_params()
+selected_page = params.get("page", [None])[0]
+
+# Define content for different lifecycle stages
+if selected_page == 'Data Collection':
     st.write("""
     ### Data Collection:
     The first stage of the NLP lifecycle involves gathering text data from various sources such as:
@@ -48,7 +93,7 @@ if selected_lifecycle_stage == 'Data Collection':
     - The data can be structured (e.g., databases) or unstructured (e.g., plain text from websites).
     """)
 
-elif selected_lifecycle_stage == 'Text Preprocessing':
+elif selected_page == 'Text Preprocessing':
     st.write("""
     ### Text Preprocessing:
     Text preprocessing is essential for preparing raw text data for analysis. The steps involved include:
@@ -63,7 +108,7 @@ elif selected_lifecycle_stage == 'Text Preprocessing':
     - Preprocessing is crucial for reducing noise in the text, ensuring that the machine learning models focus on the important features.
     """)
 
-elif selected_lifecycle_stage == 'Text Representation':
+elif selected_page == 'Text Representation':
     st.write("""
     ### Text Representation:
     After preprocessing, text needs to be converted into a numerical form for machine learning algorithms. 
@@ -76,7 +121,7 @@ elif selected_lifecycle_stage == 'Text Representation':
     - BoW and TF-IDF are more traditional methods, while word embeddings capture semantic relationships and are widely used in modern NLP tasks.
     """)
 
-elif selected_lifecycle_stage == 'Model Training':
+elif selected_page == 'Model Training':
     st.write("""
     ### Model Training:
     In the model training stage, machine learning algorithms are used to train a model on the preprocessed and represented data. 
@@ -90,7 +135,7 @@ elif selected_lifecycle_stage == 'Model Training':
     - The model learns patterns and relationships in the text data, which it will use to make predictions.
     """)
 
-elif selected_lifecycle_stage == 'Evaluation':
+elif selected_page == 'Evaluation':
     st.write("""
     ### Evaluation:
     Once a model is trained, it is evaluated to understand its performance. Common evaluation metrics include:
@@ -105,7 +150,7 @@ elif selected_lifecycle_stage == 'Evaluation':
     - It ensures that the model will perform well on unseen data (real-world applications).
     """)
 
-elif selected_lifecycle_stage == 'Deployment':
+elif selected_page == 'Deployment':
     st.write("""
     ### Deployment:
     The final stage is deploying the trained model for real-time use. The model can be integrated into applications like:
@@ -127,8 +172,15 @@ tasks = ['Text Classification', 'Sentiment Analysis', 'Named Entity Recognition
 # Add a selectbox for the user to choose an NLP task
 selected_task = st.selectbox('Choose an NLP Task:', tasks)
 
-# Define the pages for each NLP task
-if selected_task == 'Text Classification':
+# If a task is selected, update query params and display new content
+if selected_task:
+    redirect_to_page(selected_task)
+
+# Get the task from the query params
+selected_task_page = params.get("page", [None])[0]
+
+# Define content for different NLP tasks
+if selected_task_page == 'Text Classification':
     st.write("""
     ### Text Classification:
     Text Classification is the task of categorizing text into predefined labels. 
@@ -141,7 +193,7 @@ if selected_task == 'Text Classification':
     - Word Embeddings
     """)
 
-elif selected_task == 'Sentiment Analysis':
+elif selected_task_page == 'Sentiment Analysis':
     st.write("""
     ### Sentiment Analysis:
     Sentiment Analysis determines the sentiment of a given text, such as whether it is positive, negative, or neutral.
@@ -152,7 +204,7 @@ elif selected_task == 'Sentiment Analysis':
     - Machine Learning (e.g., Naive Bayes, SVM)
     """)
 
-elif selected_task == 'Named Entity Recognition (NER)':
+elif selected_task_page == 'Named Entity Recognition (NER)':
     st.write("""
     ### Named Entity Recognition (NER):
     NER is the process of identifying named entities in text, such as people, organizations, dates, locations, etc.
@@ -163,7 +215,7 @@ elif selected_task == 'Named Entity Recognition (NER)':
     - Machine Learning-based NER (e.g., CRF, LSTM)
     """)
 
-elif selected_task == 'Language Translation':
+elif selected_task_page == 'Language Translation':
     st.write("""
     ### Language Translation:
     Language Translation involves translating text from one language to another.
@@ -174,7 +226,7 @@ elif selected_task == 'Language Translation':
     - Neural Machine Translation (NMT)
     """)
 
-elif selected_task == 'Text Summarization':
+elif selected_task_page == 'Text Summarization':
     st.write("""
     ### Text Summarization:
     Text Summarization involves condensing long pieces of text into a shorter, meaningful version.
@@ -185,7 +237,7 @@ elif selected_task == 'Text Summarization':
     - Abstractive Summarization
     """)
 
-elif selected_task == 'Part-of-Speech Tagging':
+elif selected_task_page == 'Part-of-Speech Tagging':
     st.write("""
     ### Part-of-Speech (POS) Tagging:
     POS Tagging involves identifying the grammatical components of a sentence, such as nouns, verbs, adjectives, etc.
@@ -196,7 +248,7 @@ elif selected_task == 'Part-of-Speech Tagging':
     - Machine Learning-based POS Tagging (e.g., HMM, CRF)
     """)
 
-elif selected_task == 'Text Generation':
+elif selected_task_page == 'Text Generation':
     st.write("""
     ### Text Generation:
     Text Generation is the task of generating new, coherent text based on some input.
@@ -208,7 +260,7 @@ elif selected_task == 'Text Generation':
     - Transformer-based models (e.g., GPT-3)
     """)
 
-elif selected_task == 'Text Similarity':
+elif selected_task_page == 'Text Similarity':
     st.write("""
     ### Text Similarity:
     Text Similarity involves measuring the similarity between two pieces of text.