import streamlit as st
import pandas as pd

st.markdown("""
    <style>
    /* Main background and font settings */
    body {
        background-color: black;
        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
    }
    
    /* Main title styling */
    h1 {
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        font-family: 'Arial', sans-serif;
        font-weight: 700;
        text-align: center;
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    /* Header styling */
    h2 {
        color: #2c3e50;
        font-family: 'Arial', sans-serif;
        font-weight: 600;
        margin-top: 30px;
        border-left: 4px solid #3498db;
        padding-left: 10px;
    }
    
    /* Subheader styling */
    h3 {
        color: #2c3e50;
        font-family: 'Arial', sans-serif;
        font-weight: 500;
        margin-top: 20px;
    }
    
    /* Custom text styling */
    .custom-text {
        font-family: 'Georgia', serif;
        line-height: 1.8;
        color: #34495e;
        margin-bottom: 20px;
    }
    
    /* List styling */
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        list-style-type: none;
        padding-left: 20px;
    }
    
    .custom-list li {
        font-family: 'Georgia', serif;
        font-size: 1.1em;
        margin-bottom: 10px;
        color: #34495e;
        position: relative;
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    .custom-list li::before {
        content: "•";
        color: #3498db;
        font-weight: bold;
        position: absolute;
        left: 0;
    }
    
    /* Sidebar styling */
    .sidebar .sidebar-content {
        background-color: #ffffff;
        border-radius: 8px;
        padding: 15px;
        box-shadow: 0 2px 5px rgba(0,0,0,0.1);
    }
    
    /* Info box styling */
    .stInfo {
        background-color: #e8f4fc;
        border-left: 4px solid #3498db;
        padding: 15px;
        border-radius: 0 4px 4px 0;
    }
    
    /* Success box styling */
    .stSuccess {
        background-color: #e8f8f0;
        border-left: 4px solid #2ecc71;
        padding: 15px;
        border-radius: 0 4px 4px 0;
    }
    
    /* Code block styling */
    .stCodeBlock {
        background-color: white;
        border-radius: 4px;
        padding: 15px;
        border-left: 4px solid #95a5a6;
    }
    </style>
    """, unsafe_allow_html=True)

st.title("Text Data Quality Analysis")

# Introduction section
st.markdown("""
    <div class='custom-text'>
    <h2>Understanding Text Data Quality Analysis</h2>
    <p>Evaluating raw text data quality before processing is a critical first step in any text analysis project.</p>
    </div>
    """, unsafe_allow_html=True)

st.markdown("""
    <div class='stInfo'>
    <strong>Text Data Quality Analysis is crucial because:</strong><br><br>
    • Ensures raw data quality before processing<br>
    • Helps identify potential issues early in the pipeline<br>
    • Provides insights for better data exploration<br>
    • Is independent of the specific problem statement
    </div>
    """, unsafe_allow_html=True)

# Main analysis steps
st.markdown("""
    <div class='custom-text'>
    <h2>Key Text Data Quality Checks</h2>
    </div>
    """, unsafe_allow_html=True)

st.markdown("""
    <ul class='custom-list'>
    <li><strong>Check Text Case</strong> – Identify if text is in lowercase, uppercase, or mixed case</li>
    <li><strong>Detect HTML Tags</strong> – Analyze if text contains unwanted HTML elements</li>
    <li><strong>Identify URLs</strong> – Check for web addresses that may need processing</li>
    <li><strong>Detect Mentions & Hashtags</strong> – Find occurrences of @mentions or #hashtags</li>
    <li><strong>Identify Numeric Data</strong> – Detect if text includes digits or numerical data</li>
    <li><strong>Analyze Punctuation Usage</strong> – Check whether punctuation marks affect text clarity</li>
    <li><strong>Analyze Date/Time Formats</strong> – Identify the presence of date/time-related text</li>
    </ul>
    """, unsafe_allow_html=True)

st.markdown("""
    <div class='stSuccess'>
    Performing thorough text data quality analysis ensures structured and high-quality text data, leading to better analysis and model performance.
    </div>
    """, unsafe_allow_html=True)

# Code example
st.markdown("""
    <div class='custom-text'>
    <h2>Implementation Example</h2>
    <p>Here's a Python function to perform basic text data quality checks:</p>
    </div>
    """, unsafe_allow_html=True)

st.code('''
import pandas as pd
import re

def text_quality_analysis(data, column):
    # Initialize results dictionary
    results = {}
    
    # Check for case variations
    results['has_lowercase'] = data[column].str.contains('[a-z]').sum()
    results['has_uppercase'] = data[column].str.contains('[A-Z]').sum()
    
    # Check for HTML tags
    results['has_html_tags'] = data[column].str.contains('<.*?>', regex=True).sum()
    
    # Check for URLs
    results['has_urls'] = data[column].str.contains('https?://\\S+', regex=True).sum()
    
    # Check for email addresses
    results['has_emails'] = data[column].str.contains('\\S+@\\S+', regex=True).sum()
    
    # Check for mentions and hashtags
    results['has_mentions'] = data[column].str.contains('@\\w+', regex=True).sum()
    results['has_hashtags'] = data[column].str.contains('#\\w+', regex=True).sum()
    
    # Check for digits
    results['has_digits'] = data[column].str.contains('\\d', regex=True).sum()
    
    # Check for punctuation
    results['has_punctuation'] = data[column].str.contains('[!"#$%&\\\'()*+,-./:;<=>?@[\\\\]^_`{|}~]', regex=True).sum()
    
    # Check for date formats (simple check)
    results['has_dates'] = data[column].str.contains('\\d{1,2}/\\d{1,2}/\\d{2,4}', regex=True).sum()
    
    return pd.DataFrame.from_dict(results, orient='index', columns=['Count'])
''', language='python')

st.markdown("""
    <div class='custom-text'>
    <p>This function provides a comprehensive analysis of text data quality by checking for various common elements that might need special handling during preprocessing.</p>
    <p>The results can help guide your data cleaning strategy based on the specific characteristics of your text data.</p>
    </div>
    """, unsafe_allow_html=True)