"""
Readability Analysis Module
Analyzes text readability using various metrics

Developer: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
Year: 2026
"""

import re
from textstat import flesch_reading_ease, flesch_kincaid_grade, smog_index, coleman_liau_index, automated_readability_index

class ReadabilityAnalyzer:
    """
    Readability analysis class
    Developer: RSK World - https://rskworld.in
    """
    
    def __init__(self):
        """Initialize readability analyzer"""
        pass
    
    def analyze(self, text):
        """
        Analyze text readability using multiple metrics
        
        Args:
            text (str): Input text
            
        Returns:
            dict: Readability analysis results
        """
        if not text or len(text.strip()) < 10:
            return {
                'error': 'Text too short for readability analysis'
            }
        
        try:
            # Calculate various readability scores
            flesch_ease = flesch_reading_ease(text)
            flesch_grade = flesch_kincaid_grade(text)
            smog = smog_index(text)
            coleman = coleman_liau_index(text)
            ari = automated_readability_index(text)
            
            # Interpret Flesch Reading Ease
            if flesch_ease >= 90:
                reading_level = "Very Easy"
            elif flesch_ease >= 80:
                reading_level = "Easy"
            elif flesch_ease >= 70:
                reading_level = "Fairly Easy"
            elif flesch_ease >= 60:
                reading_level = "Standard"
            elif flesch_ease >= 50:
                reading_level = "Fairly Difficult"
            elif flesch_ease >= 30:
                reading_level = "Difficult"
            else:
                reading_level = "Very Difficult"
            
            # Calculate average grade level
            avg_grade = (flesch_grade + smog + coleman + ari) / 4
            
            return {
                'flesch_reading_ease': round(flesch_ease, 2),
                'flesch_kincaid_grade': round(flesch_grade, 2),
                'smog_index': round(smog, 2),
                'coleman_liau_index': round(coleman, 2),
                'automated_readability_index': round(ari, 2),
                'average_grade_level': round(avg_grade, 2),
                'reading_level': reading_level,
                'interpretation': self._get_interpretation(flesch_ease, avg_grade)
            }
        except Exception as e:
            print(f"Error in readability analysis: {e}")
            return {
                'error': str(e)
            }
    
    def _get_interpretation(self, flesch_ease, avg_grade):
        """
        Get interpretation of readability scores
        
        Args:
            flesch_ease (float): Flesch Reading Ease score
            avg_grade (float): Average grade level
            
        Returns:
            str: Interpretation text
        """
        if flesch_ease >= 70 and avg_grade <= 8:
            return "The text is easy to read and suitable for general audiences."
        elif flesch_ease >= 50 and avg_grade <= 12:
            return "The text is moderately readable and suitable for high school level readers."
        elif flesch_ease >= 30 and avg_grade <= 16:
            return "The text is fairly difficult and suitable for college level readers."
        else:
            return "The text is difficult to read and may require advanced reading skills."

"""
Example usage of NLP Text Analysis Bot
Developer: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
Year: 2026
"""

from nlp_pipeline import NLPPipeline

def main():
    """
    Example usage of the NLP pipeline
    Developer: RSK World - https://rskworld.in
    """
    # Initialize the NLP pipeline
    print("Initializing NLP Pipeline...")
    pipeline = NLPPipeline()
    
    # Example 1: Product review analysis
    print("\n" + "="*60)
    print("Example 1: Product Review Analysis")
    print("="*60)
    
    review = """
    I absolutely love this new smartphone! The camera quality is amazing 
    and the battery life lasts all day. The design is sleek and modern. 
    However, the price is quite high compared to competitors. Overall, 
    it's a great product from Apple Inc.
    """
    
    results = pipeline.analyze(review)
    print(f"Sentiment: {results['sentiment']['label']}")
    print(f"Score: {results['sentiment']['score']:.3f}")
    print(f"Entities: {len(results['entities']['entities'])} found")
    print(f"Key Topics: {', '.join(results['semantic']['topics'][:3])}")
    
    # Example 2: News article analysis
    print("\n" + "="*60)
    print("Example 2: News Article Analysis")
    print("="*60)
    
    news = """
    Microsoft Corporation announced today that it will be opening a new 
    research facility in Seattle, Washington. The company's CEO, Satya 
    Nadella, stated that this investment will create over 500 jobs and 
    focus on artificial intelligence research. This is great news for 
    the local economy and technology sector.
    """
    
    results = pipeline.analyze(news)
    print(f"Sentiment: {results['sentiment']['label']}")
    print(f"Word Count: {results['summary']['word_count']}")
    print(f"Entities Found: {results['summary']['entity_count']}")
    
    # Print all entities
    print("\nDetected Entities:")
    for entity in results['entities']['entities']:
        print(f"  - {entity['text']} ({entity['label']})")
    
    # Example 3: Social media post
    print("\n" + "="*60)
    print("Example 3: Social Media Post Analysis")
    print("="*60)
    
    post = "Just had the worst experience at the restaurant. Terrible service and cold food. Never going back!"
    
    results = pipeline.analyze(post)
    print(f"Sentiment: {results['sentiment']['label']}")
    print(f"Sentiment Score: {results['sentiment']['score']:.3f}")
    if results['sentiment'].get('vader'):
        vader = results['sentiment']['vader']
        print(f"VADER Breakdown - Positive: {vader['positive']:.2%}, "
              f"Neutral: {vader['neutral']:.2%}, Negative: {vader['negative']:.2%}")

if __name__ == '__main__':
    main()

"""
Setup script for NLP Text Analysis Bot
Developer: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
Year: 2026
"""

from setuptools import setup, find_packages

with open("README.md", "r", encoding="utf-8") as fh:
    long_description = fh.read()

with open("requirements.txt", "r", encoding="utf-8") as fh:
    requirements = [line.strip() for line in fh if line.strip() and not line.startswith("#")]

setup(
    name="nlp-text-analysis-bot",
    version="1.0.0",
    author="RSK World",
    author_email="help@rskworld.in",
    description="Chatbot with natural language processing capabilities for text understanding and analysis",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://rskworld.in",
    packages=find_packages(),
    classifiers=[
        "Development Status :: 4 - Beta",
        "Intended Audience :: Developers",
        "Topic :: Software Development :: Libraries :: Python Modules",
        "Topic :: Scientific/Engineering :: Artificial Intelligence",
        "Programming Language :: Python :: 3",
        "Programming Language :: Python :: 3.8",
        "Programming Language :: Python :: 3.9",
        "Programming Language :: Python :: 3.10",
        "Programming Language :: Python :: 3.11",
    ],
    python_requires=">=3.8",
    install_requires=requirements,
)