| import streamlit as st |
|
|
| st.markdown( |
| """ |
| <style> |
| /* App Background */ |
| .stApp { |
| background: linear-gradient(to right , #009999, #006666 ,#0099FF); /* Gradient dark professional background */ |
| color: #9966FF; |
| padding: 20px; |
| } |
| /* Align content to the left */ |
| .block-container { |
| text-align: left; /* Left align for content */ |
| padding: 2rem; /* Padding for aesthetics */ |
| } |
| |
| /* Header and Subheader Text */ |
| h1 { |
| color: #0000FF !important; /* Custom styling for the main header */ |
| font-family: 'Arial', sans-serif !important; |
| font-weight: bold !important; |
| text-align: center; |
| } |
| h2, h3, h4 { |
| color: #66CCFF !important; /* Custom styling for subheaders */ |
| font-family: 'Arial', sans-serif !important; |
| font-weight: bold !important; |
| } |
| /* Paragraph Text */ |
| p { |
| color: #00CCCC !important; /* Custom styling for paragraphs */ |
| font-family: 'Arial', sans-serif !important; |
| line-height: 1.6; |
| } |
| </style> |
| """, |
| unsafe_allow_html=True |
| ) |
|
|
| |
| st.markdown( |
| """ |
| <h1>Natural Language Processing (NLP)</h1> |
| <h3>Introduction to NLP</h3> |
| <p> |
| Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on enabling machines to understand, interpret, and interact with human language in a meaningful way. |
| It bridges the gap between human communication and computer understanding, combining elements of linguistics, computer science, and machine learning. |
| </p> |
| <p> |
| Textual data refers to information or data represented in text format, which can include words, sentences, paragraphs, or documents. It is a form of unstructured data, meaning it does not adhere to a predefined schema or format like tabular data. |
| Textual data is prevalent in natural language processing (NLP) tasks, where the goal is to extract insights, meaning, or actionable information from text. |
| </p> |
| |
| <h3>Why is NLP Important?</h3> |
| <p> |
| Natural Language Processing (NLP) is crucial in today’s digital age because it enables machines to understand, interpret, and interact with human language. |
| This capability has transformed how humans interact with technology and has a profound impact across industries. . It powers a wide range of applications, including: |
| </p> |
| <ul> |
| <li><b>Language Translation:</b> Tools like Google Translate that break language barriers.</li> |
| <li><b>Sentiment Analysis:</b> Understanding emotions and opinions from customer feedback or social media.</li> |
| <li><b>Chatbots and Virtual Assistants:</b> AI systems like Alexa and Siri that interact naturally with users.</li> |
| <li><b>Text Summarization:</b> Condensing lengthy documents into concise summaries.</li> |
| </ul> |
| <p> |
| <b>Programming Insight:</b> Python is one of the most popular programming languages for NLP, thanks to libraries like <code>NLTK</code>, <code>spaCy</code>, and <code>transformers</code>, which provide powerful tools for text processing and machine learning. |
| </p> |
| |
| <h3>Understanding the NLP Workflow</h3> |
| <p> |
| The workflow of Natural Language Processing (NLP) involves a series of systematic steps to process and analyze textual data. Here’s a simplified workflow: |
| </p> |
| <ol> |
| <li><b>Input:</b> Raw text or speech data (e.g., customer reviews, transcripts).</li> |
| <li><b>Preprocessing:</b> Cleaning and transforming data (e.g., removing stopwords, tokenization).</li> |
| <li><b>Feature Extraction:</b> Converting text into machine-readable formats (e.g., word embeddings).</li> |
| <li><b>Modeling:</b> Applying algorithms to extract meaning or make predictions.</li> |
| <li><b>Output:</b> Structured data, insights, or actions (e.g., sentiment scores, keyword extraction).</li> |
| </ol> |
| """ |
| , unsafe_allow_html=True |
| ) |
