import streamlit as st

# Set page configuration
st.set_page_config(page_title="Natural Language Processing", layout="centered")

# Headline
st.title("🧠 Natural Language Processing")

# Introduction section
st.markdown("""
## Introduction
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. By combining linguistics, computer science, and machine learning, NLP seeks to bridge the gap between human communication and computer understanding.
""")

# History of NLP
st.markdown("""
## History of NLP
The evolution of NLP can be traced back to the 1950s when early work focused on machine translation and simple language processing tasks. Over the decades, the field has progressed through several stages:
- **Rule-Based Systems:** Early approaches relied on handcrafted rules and linguistic knowledge.
- **Statistical Methods:** The introduction of probabilistic models in the 1980s and 1990s enabled more data-driven approaches.
- **Deep Learning:** In recent years, neural networks and deep learning techniques have revolutionized NLP, leading to significant improvements in tasks such as translation, sentiment analysis, and text generation.
""")

# Techniques used in NLP
st.markdown("""
## Techniques and Methods in NLP
NLP encompasses a wide range of techniques that include, but are not limited to:
- **Tokenization:** Breaking text into words or sentences.
- **Part-of-Speech Tagging:** Identifying the grammatical role of each word.
- **Named Entity Recognition (NER):** Detecting entities such as names, dates, and locations.
- **Parsing:** Analyzing the grammatical structure of sentences.
- **Sentiment Analysis:** Determining the emotional tone behind text.
- **Language Modeling:** Predicting the probability of a sequence of words, which is key to text generation and autocomplete features.
- **Machine Translation:** Automatically translating text from one language to another.
""")

# Applications of NLP
st.markdown("""
## Applications of NLP
The advancements in NLP have led to its integration into a variety of real-world applications:
- **Virtual Assistants:** Siri, Alexa, and Google Assistant use NLP to understand and respond to voice commands.
- **Chatbots:** Automated customer support and interactive agents that simulate human conversation.
- **Content Recommendation:** Enhancing user experience through personalized suggestions.
- **Healthcare:** Analyzing clinical notes and patient records to assist in diagnostics and treatment planning.
- **Finance:** Sentiment analysis on news feeds and social media to inform trading strategies.
- **Education:** Automated essay scoring, language tutoring, and content summarization.
""")

# Challenges and Future Directions
st.markdown("""
## Challenges and Future Directions
While NLP has made remarkable progress, it still faces several challenges:
- **Ambiguity and Context:** Human language is inherently ambiguous, making context understanding crucial.
- **Data Privacy:** Ensuring sensitive information is handled securely during language processing.
- **Bias in Data:** Mitigating biases present in training data to avoid unfair outcomes.
- **Multilingual Processing:** Developing models that can effectively handle the diversity of human languages.
  
Looking forward, research continues to focus on more advanced models, improved context understanding, and the development of ethical guidelines for responsible NLP usage.
""")

# Footer
st.markdown("""
---
*This app is a brief overview of Natural Language Processing and its various facets. For further reading, consider exploring academic literature, online courses, and tutorials on NLP.*
""")