app.py

import gradio as gr
import torch
from gliner import GLiNER
import pandas as pd
import warnings
import random
import re
warnings.filterwarnings('ignore')

# Standard NER entity types
STANDARD_ENTITIES = [
    'DATE', 'EVENT', 'FAC', 'GPE', 'LANG', 'LOC',
    'MISC', 'NORP', 'ORG', 'PER', 'PRODUCT', 'WORK_OF_ART'
]

# Color schemes
STANDARD_COLORS = {
    'DATE': '#FF6B6B',      # Red
    'EVENT': '#4ECDC4',     # Teal
    'FAC': '#45B7D1',       # Blue
    'GPE': '#F9CA24',       # Yellow
    'LANG': '#6C5CE7',      # Purple
    'LOC': '#A0E7E5',       # Light Cyan
    'MISC': '#FD79A8',      # Pink
    'NORP': '#8E8E93',      # Grey
    'ORG': '#55A3FF',       # Light Blue
    'PER': '#00B894',       # Green
    'PRODUCT': '#E17055',   # Orange-Red
    'WORK_OF_ART': '#DDA0DD'  # Plum
}

# Additional colors for custom entities
CUSTOM_COLOR_PALETTE = [
    '#FF9F43', '#10AC84', '#EE5A24', '#0FBC89', '#5F27CD',
    '#FF3838', '#2F3640', '#3742FA', '#2ED573', '#FFA502',
    '#FF6348', '#1E90FF', '#FF1493', '#32CD32', '#FFD700',
    '#FF4500', '#DA70D6', '#00CED1', '#FF69B4', '#7B68EE'
]

class HybridNERManager:
    def __init__(self):
        self.gliner_model = None
        self.spacy_model = None
        self.flair_models = {}
        self.all_entity_colors = {}
        self.model_names = [
            'spacy_en_core_web_sm',
            'flair_ner-ontonotes-large', 
            'flair_ner-large',
            'gliner_medium-v2.1'
        ]

    def load_gliner_model(self):
        """Load GLiNER model for custom entities"""
        if self.gliner_model is None:
            try:
                # Use a more stable model for HF Spaces
                self.gliner_model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")
                print("✓ GLiNER model loaded successfully")
            except Exception as e:
                print(f"Error loading GLiNER model: {str(e)}")
                return None
        return self.gliner_model

    def load_model(self, model_name):
        """Load the specified model"""
        try:
            if model_name == 'spacy_en_core_web_sm':
                return self.load_spacy_model()
            elif 'flair' in model_name:
                return self.load_flair_model(model_name)
            elif 'gliner' in model_name:
                return self.load_gliner_model()
        except Exception as e:
            print(f"Error loading {model_name}: {str(e)}")
            return None

    def load_spacy_model(self):
        """Load spaCy model for standard NER"""
        if self.spacy_model is None:
            try:
                import spacy
                try:
                    self.spacy_model = spacy.load("en_core_web_sm")
                    print("✓ spaCy model loaded successfully")
                except OSError:
                    print("spaCy model not found. Using GLiNER for all entity types.")
                    return None
            except Exception as e:
                print(f"Error loading spaCy model: {str(e)}")
                return None
        return self.spacy_model

    def load_flair_model(self, model_name):
        """Load Flair models"""
        if model_name not in self.flair_models:
            try:
                from flair.models import SequenceTagger
                if 'ontonotes' in model_name:
                    model = SequenceTagger.load("flair/ner-english-ontonotes-large")
                else:
                    model = SequenceTagger.load("flair/ner-english-large")
                self.flair_models[model_name] = model
                print(f"✓ {model_name} loaded successfully")
            except Exception as e:
                print(f"Error loading {model_name}: {str(e)}")
                return None
        return self.flair_models[model_name]

    def extract_spacy_entities(self, text, entity_types):
        """Extract entities using spaCy"""
        model = self.load_spacy_model()
        if model is None:
            return []

        try:
            doc = model(text)
            entities = []
            for ent in doc.ents:
                if ent.label_ in entity_types:
                    entities.append({
                        'text': ent.text,
                        'label': ent.label_,
                        'start': ent.start_char,
                        'end': ent.end_char,
                        'confidence': 1.0,  # spaCy doesn't provide confidence scores
                        'source': 'spaCy'
                    })
            return entities
        except Exception as e:
            print(f"Error with spaCy extraction: {str(e)}")
            return []

    def assign_colors(self, standard_entities, custom_entities):
        """Assign colors to all entity types"""
        self.all_entity_colors = {}

        # Assign standard colors
        for entity in standard_entities:
            self.all_entity_colors[entity.upper()] = STANDARD_COLORS.get(entity, '#CCCCCC')

        # Assign custom colors
        for i, entity in enumerate(custom_entities):
            if i < len(CUSTOM_COLOR_PALETTE):
                self.all_entity_colors[entity.upper()] = CUSTOM_COLOR_PALETTE[i]
            else:
                # Generate random color if we run out
                self.all_entity_colors[entity.upper()] = f"#{random.randint(0, 0xFFFFFF):06x}"

        return self.all_entity_colors

    def extract_entities_by_model(self, text, entity_types, model_name, threshold=0.3):
        """Extract entities using the specified model"""
        if model_name == 'spacy_en_core_web_sm':
            return self.extract_spacy_entities(text, entity_types)
        elif 'flair' in model_name:
            return self.extract_flair_entities(text, entity_types, model_name)
        elif 'gliner' in model_name:
            return self.extract_gliner_entities(text, entity_types, threshold, is_custom=False)
        else:
            return []

    def extract_flair_entities(self, text, entity_types, model_name):
        """Extract entities using Flair"""
        model = self.load_flair_model(model_name)
        if model is None:
            return []

        try:
            from flair.data import Sentence
            sentence = Sentence(text)
            model.predict(sentence)
            entities = []
            for entity in sentence.get_spans('ner'):
                # Map Flair labels to our standard set
                label = entity.tag
                if label == 'PERSON':
                    label = 'PER'
                elif label == 'ORGANIZATION':
                    label = 'ORG'
                elif label == 'LOCATION':
                    label = 'LOC'
                elif label == 'MISCELLANEOUS':
                    label = 'MISC'

                if label in entity_types:
                    entities.append({
                        'text': entity.text,
                        'label': label,
                        'start': entity.start_position,
                        'end': entity.end_position,
                        'confidence': entity.score,
                        'source': f'Flair-{model_name.split("-")[-1]}'
                    })
            return entities
        except Exception as e:
            print(f"Error with Flair extraction: {str(e)}")
            return []

    def extract_gliner_entities(self, text, entity_types, threshold=0.3, is_custom=True):
        """Extract entities using GLiNER"""
        model = self.load_gliner_model()
        if model is None:
            return []

        try:
            entities = model.predict_entities(text, entity_types, threshold=threshold)
            result = []
            for entity in entities:
                result.append({
                    'text': entity['text'],
                    'label': entity['label'].upper(),
                    'start': entity['start'],
                    'end': entity['end'],
                    'confidence': entity.get('score', 0.0),
                    'source': 'GLiNER-Custom' if is_custom else 'GLiNER-Standard'
                })
            return result
        except Exception as e:
            print(f"Error with GLiNER extraction: {str(e)}")
            return []

def find_overlapping_entities(entities):
    """Find and share overlapping entities"""
    if not entities:
        return []

    # Sort entities by start position
    sorted_entities = sorted(entities, key=lambda x: x['start'])
    shared_entities = []

    i = 0
    while i < len(sorted_entities):
        current_entity = sorted_entities[i]
        overlapping_entities = [current_entity]

        # Find all entities that overlap with current entity
        j = i + 1
        while j < len(sorted_entities):
            next_entity = sorted_entities[j]

            # Check if entities overlap
            if (current_entity['start'] <= next_entity['start'] < current_entity['end'] or
                next_entity['start'] <= current_entity['start'] < next_entity['end'] or
                current_entity['text'].lower() == next_entity['text'].lower()):
                overlapping_entities.append(next_entity)
                sorted_entities.pop(j)
            else:
                j += 1

        # Create shared entity
        if len(overlapping_entities) == 1:
            shared_entities.append(overlapping_entities[0])
        else:
            shared_entity = share_entities(overlapping_entities)
            shared_entities.append(shared_entity)

        i += 1

    return shared_entities

def share_entities(entity_list):
    """Share multiple overlapping entities into one"""
    if len(entity_list) == 1:
        return entity_list[0]

    # Use the entity with the longest text span as the base
    base_entity = max(entity_list, key=lambda x: len(x['text']))

    # Collect all labels and sources
    labels = [entity['label'] for entity in entity_list]
    sources = [entity['source'] for entity in entity_list]
    confidences = [entity['confidence'] for entity in entity_list]

    return {
        'text': base_entity['text'],
        'start': base_entity['start'],
        'end': base_entity['end'],
        'labels': labels,
        'sources': sources,
        'confidences': confidences,
        'is_shared': True,
        'entity_count': len(entity_list)
    }

def create_highlighted_html(text, entities, entity_colors):
    """Create HTML with highlighted entities"""
    if not entities:
        return f"<div style='padding: 15px; border: 1px solid #ddd; border-radius: 5px; background-color: #fafafa;'><p>{text}</p></div>"

    # Find and share overlapping entities
    shared_entities = find_overlapping_entities(entities)

    # Sort by start position
    sorted_entities = sorted(shared_entities, key=lambda x: x['start'])

    # Create HTML with highlighting
    html_parts = []
    last_end = 0

    for entity in sorted_entities:
        # Add text before entity
        html_parts.append(text[last_end:entity['start']])

        if entity.get('is_shared', False):
            # Handle shared entity with multiple colors
            html_parts.append(create_shared_entity_html(entity, entity_colors))
        else:
            # Handle single entity
            html_parts.append(create_single_entity_html(entity, entity_colors))

        last_end = entity['end']

    # Add remaining text
    html_parts.append(text[last_end:])

    highlighted_text = ''.join(html_parts)

    return f"""
    <div style='padding: 15px; border: 2px solid #ddd; border-radius: 8px; background-color: #fafafa; margin: 10px 0;'>
        <h4 style='margin: 0 0 15px 0; color: #333;'>📝 Text with Highlighted Entities</h4>
        <div style='line-height: 1.8; font-size: 16px; background-color: white; padding: 15px; border-radius: 5px;'>{highlighted_text}</div>
    </div>
    """

def create_single_entity_html(entity, entity_colors):
    """Create HTML for a single entity"""
    label = entity['label']
    color = entity_colors.get(label.upper(), '#CCCCCC')
    confidence = entity.get('confidence', 0.0)
    source = entity.get('source', 'Unknown')

    return (f'<span style="background-color: {color}; padding: 2px 4px; '
            f'border-radius: 3px; margin: 0 1px; '
            f'border: 1px solid {color}; color: white; font-weight: bold;" '
            f'title="{label} ({source}) - confidence: {confidence:.2f}">'
            f'{entity["text"]}</span>')

def create_shared_entity_html(entity, entity_colors):
    """Create HTML for a shared entity with multiple colors"""
    labels = entity['labels']
    sources = entity['sources']
    confidences = entity['confidences']

    # Get colors for each label
    colors = []
    for label in labels:
        color = entity_colors.get(label.upper(), '#CCCCCC')
        colors.append(color)

    # Create gradient background
    if len(colors) == 2:
        gradient = f"linear-gradient(to right, {colors[0]} 50%, {colors[1]} 50%)"
    else:
        # For more colors, create equal segments
        segment_size = 100 / len(colors)
        gradient_parts = []
        for i, color in enumerate(colors):
            start = i * segment_size
            end = (i + 1) * segment_size
            gradient_parts.append(f"{color} {start}%, {color} {end}%")
        gradient = f"linear-gradient(to right, {', '.join(gradient_parts)})"

    # Create tooltip
    tooltip_parts = []
    for i, label in enumerate(labels):
        tooltip_parts.append(f"{label} ({sources[i]}) - {confidences[i]:.2f}")
    tooltip = " | ".join(tooltip_parts)

    return (f'<span style="background: {gradient}; padding: 2px 4px; '
            f'border-radius: 3px; margin: 0 1px; '
            f'border: 2px solid #333; color: white; font-weight: bold;" '
            f'title="SHARED: {tooltip}">'
            f'{entity["text"]} 🔗</span>')

def create_entity_table_html(entities, entity_colors):
    """Create HTML table with tabbed interface like the original"""
    if not entities:
        return "<p>No entities found.</p>"

    # Share overlapping entities
    shared_entities = find_overlapping_entities(entities)

    # Group entities by type
    entity_groups = {}
    for entity in shared_entities:
        if entity.get('is_shared', False):
            key = 'SHARED_ENTITIES'
        else:
            key = entity['label']
        
        if key not in entity_groups:
            entity_groups[key] = []
        entity_groups[key].append(entity)

    if not entity_groups:
        return "<p>No entities found.</p>"

    # Create tabbed interface
    tab_html = "<div style='margin: 20px 0;'>"

    # Tab headers
    tab_html += "<div style='border-bottom: 2px solid #ddd; margin-bottom: 20px;'>"
    tab_headers = []

    for i, entity_type in enumerate(sorted(entity_groups.keys())):
        count = len(entity_groups[entity_type])

        if entity_type == 'SHARED_ENTITIES':
            color = '#666666'
            icon = "🔗"
            display_name = "SHARED"
        else:
            color = entity_colors.get(entity_type.upper(), '#f0f0f0')
            # Determine if it's standard or custom
            is_standard = entity_type in STANDARD_ENTITIES
            icon = "🎯" if is_standard else "✨"
            display_name = entity_type

        active_style = f"background-color: #f8f9fa; border-bottom: 3px solid {color};" if i == 0 else "background-color: #fff;"
        tab_headers.append(f"""
        <button onclick="showTab('{entity_type}')" id="tab-{entity_type}" 
                style="padding: 12px 24px; margin-right: 5px; border: 1px solid #ddd;
                       border-bottom: none; cursor: pointer; font-weight: bold; {active_style}">
            {icon} {display_name} ({count})
        </button>
        """)

    tab_html += ''.join(tab_headers)
    tab_html += "</div>"

    # Tab content
    for i, entity_type in enumerate(sorted(entity_groups.keys())):
        entities_of_type = entity_groups[entity_type]
        display_style = "display: block;" if i == 0 else "display: none;"

        if entity_type == 'SHARED_ENTITIES':
            color = '#666666'
            header_text = f"🔗 Shared Entities ({len(entities_of_type)} found)"
        else:
            color = entity_colors.get(entity_type.upper(), '#f0f0f0')
            source_type = entities_of_type[0].get('source', 'Unknown')
            is_standard = entity_type in STANDARD_ENTITIES
            source_icon = "🎯 Standard NER" if is_standard else "✨ Custom GLiNER"
            header_text = f"{source_icon} - {entity_type} Entities ({len(entities_of_type)} found)"

        tab_html += f"""
        <div id="content-{entity_type}" style="{display_style}">
            <h4 style="color: {color}; margin-bottom: 15px;">{header_text}</h4>
            <table style="width: 100%; border-collapse: collapse; margin-bottom: 20px;">
                <thead>
        """

        if entity_type == 'SHARED_ENTITIES':
            tab_html += f"""
                    <tr style="background-color: {color}; color: white;">
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">Entity Text</th>
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">All Labels</th>
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">Sources</th>
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">Count</th>
                    </tr>
                </thead>
                <tbody>
            """

            for entity in entities_of_type:
                labels_text = " | ".join(entity['labels'])
                sources_text = " | ".join(entity['sources'])

                tab_html += f"""
                        <tr style="background-color: #fff;">
                            <td style="padding: 10px; border: 1px solid #ddd; font-weight: bold;">{entity['text']}</td>
                            <td style="padding: 10px; border: 1px solid #ddd;">{labels_text}</td>
                            <td style="padding: 10px; border: 1px solid #ddd;">{sources_text}</td>
                            <td style="padding: 10px; border: 1px solid #ddd; text-align: center;">
                                <span style='background-color: #28a745; color: white; padding: 2px 6px; border-radius: 10px; font-size: 11px;'>
                                    {entity['entity_count']}
                                </span>
                            </td>
                        </tr>
                """
        else:
            tab_html += f"""
                    <tr style="background-color: {color}; color: white;">
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">Entity Text</th>
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">Confidence</th>
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">Type</th>
                        <th style="padding: 12px; text-align: left; border: 1px solid #ddd;">Source</th>
                    </tr>
                </thead>
                <tbody>
            """

            # Sort by confidence score
            entities_of_type.sort(key=lambda x: x.get('confidence', 0), reverse=True)

            for entity in entities_of_type:
                confidence = entity.get('confidence', 0.0)
                confidence_color = "#28a745" if confidence > 0.7 else "#ffc107" if confidence > 0.4 else "#dc3545"
                source = entity.get('source', 'Unknown')
                source_badge = f"<span style='background-color: #007bff; color: white; padding: 2px 6px; border-radius: 10px; font-size: 11px;'>{source}</span>"

                tab_html += f"""
                        <tr style="background-color: #fff;">
                            <td style="padding: 10px; border: 1px solid #ddd; font-weight: bold;">{entity['text']}</td>
                            <td style="padding: 10px; border: 1px solid #ddd;">
                                <span style="color: {confidence_color}; font-weight: bold;">
                                    {confidence:.3f}
                                </span>
                            </td>
                            <td style="padding: 10px; border: 1px solid #ddd;">{entity['label']}</td>
                            <td style="padding: 10px; border: 1px solid #ddd;">{source_badge}</td>
                        </tr>
                """

        tab_html += """
                </tbody>
            </table>
        </div>
        """

    # JavaScript for tab switching
    tab_html += """
    <script>
    function showTab(entityType) {
        // Hide all content
        var contents = document.querySelectorAll('[id^="content-"]');
        contents.forEach(function(content) {
            content.style.display = 'none';
        });

        // Reset all tab styles
        var tabs = document.querySelectorAll('[id^="tab-"]');
        tabs.forEach(function(tab) {
            tab.style.backgroundColor = '#fff';
            tab.style.borderBottom = 'none';
        });

        // Show selected content
        document.getElementById('content-' + entityType).style.display = 'block';

        // Highlight selected tab
        var activeTab = document.getElementById('tab-' + entityType);
        activeTab.style.backgroundColor = '#f8f9fa';
        activeTab.style.borderBottom = '3px solid #4ECDC4';
    }
    </script>
    """

    tab_html += "</div>"
    return tab_html

def create_legend_html(entity_colors, standard_entities, custom_entities):
    """Create a legend showing entity colors"""
    if not entity_colors:
        return ""

    html = "<div style='margin: 15px 0; padding: 15px; background-color: #f8f9fa; border-radius: 8px;'>"
    html += "<h4 style='margin: 0 0 15px 0;'>🎨 Entity Type Legend</h4>"

    if standard_entities:
        html += "<div style='margin-bottom: 15px;'>"
        html += "<h5 style='margin: 0 0 8px 0;'>🎯 Standard Entities:</h5>"
        html += "<div style='display: flex; flex-wrap: wrap; gap: 8px;'>"
        for entity_type in standard_entities:
            color = entity_colors.get(entity_type.upper(), '#ccc')
            html += f"<span style='background-color: {color}; padding: 4px 8px; border-radius: 15px; color: white; font-weight: bold; font-size: 12px;'>{entity_type}</span>"
        html += "</div></div>"

    if custom_entities:
        html += "<div>"
        html += "<h5 style='margin: 0 0 8px 0;'>✨ Custom Entities:</h5>"
        html += "<div style='display: flex; flex-wrap: wrap; gap: 8px;'>"
        for entity_type in custom_entities:
            color = entity_colors.get(entity_type.upper(), '#ccc')
            html += f"<span style='background-color: {color}; padding: 4px 8px; border-radius: 15px; color: white; font-weight: bold; font-size: 12px;'>{entity_type}</span>"
        html += "</div></div>"

    html += "</div>"
    return html

# Initialize the NER manager
ner_manager = HybridNERManager()

def process_text(text, standard_entities, custom_entities_str, confidence_threshold, selected_model):
    """Main processing function for Gradio interface"""
    if not text.strip():
        return "❌ Please enter some text to analyze", "", ""

    # Parse custom entities
    custom_entities = []
    if custom_entities_str.strip():
        custom_entities = [entity.strip() for entity in custom_entities_str.split(',') if entity.strip()]

    # Parse standard entities
    selected_standard = [entity for entity in standard_entities if entity]

    if not selected_standard and not custom_entities:
        return "❌ Please select at least one standard entity type OR enter custom entity types", "", ""

    all_entities = []

    # Extract standard entities using selected model
    if selected_standard and selected_model:
        standard_entities_results = ner_manager.extract_entities_by_model(text, selected_standard, selected_model, confidence_threshold)
        all_entities.extend(standard_entities_results)

    # Extract custom entities using GLiNER
    if custom_entities:
        custom_entity_results = ner_manager.extract_gliner_entities(text, custom_entities, confidence_threshold, is_custom=True)
        all_entities.extend(custom_entity_results)

    if not all_entities:
        return "❌ No entities found. Try lowering the confidence threshold or using different entity types.", "", ""

    # Assign colors
    entity_colors = ner_manager.assign_colors(selected_standard, custom_entities)

    # Create outputs
    legend_html = create_legend_html(entity_colors, selected_standard, custom_entities)
    highlighted_html = create_highlighted_html(text, all_entities, entity_colors)
    table_html = create_entity_table_html(all_entities, entity_colors)

    # Create summary with shared entities terminology
    total_entities = len(all_entities)
    shared_entities = find_overlapping_entities(all_entities)
    final_count = len(shared_entities)
    shared_count = sum(1 for e in shared_entities if e.get('is_shared', False))

    summary = f"""
    ## 📊 Analysis Summary
    - **Total entities found:** {total_entities}
    - **Final entities displayed:** {final_count}
    - **Shared entities:** {shared_count}
    - **Average confidence:** {sum(e.get('confidence', 0) for e in all_entities) / total_entities:.3f}
    """

    return summary, legend_html + highlighted_html, table_html

# Create Gradio interface
def create_interface():
    with gr.Blocks(title="Hybrid NER + GLiNER Tool", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🎯 Hybrid NER + Custom GLiNER Entity Recognition Tool
        
        Combine standard NER categories with your own custom entity types! This tool uses both traditional NER models and GLiNER for comprehensive entity extraction.
        
        ## 🔗 NEW: Overlapping entities are automatically shared with split-color highlighting!
        
        ### How to use:
        1. **📝 Enter your text** in the text area below
        2. **🎯 Select a model** from the dropdown for standard entities
        3. **☑️ Select standard entities** you want to find (PER, ORG, LOC, etc.)
        4. **✨ Add custom entities** (comma-separated) like "relationships, occupations, skills"
        5. **⚙️ Adjust confidence threshold** 
        6. **🔍 Click "Analyze Text"** to see results with tabbed output
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                text_input = gr.Textbox(
                    label="📝 Text to Analyze",
                    placeholder="Enter your text here...",
                    lines=6,
                    max_lines=10
                )
            
            with gr.Column(scale=1):
                confidence_threshold = gr.Slider(
                    minimum=0.1,
                    maximum=0.9,
                    value=0.3,
                    step=0.1,
                    label="🎚️ Confidence Threshold"
                )
        
        with gr.Row():
            with gr.Column():
                gr.Markdown("### 🎯 Standard Entity Types")
                
                # Model selector
                model_dropdown = gr.Dropdown(
                    choices=ner_manager.model_names,
                    value=ner_manager.model_names[0],
                    label="Select Model for Standard Entities",
                    info="Choose which model to use for standard NER"
                )
                
                # Standard entities with select all functionality
                standard_entities = gr.CheckboxGroup(
                    choices=STANDARD_ENTITIES,
                    value=['PER', 'ORG', 'LOC', 'MISC'],  # Default selection
                    label="Select Standard Entities"
                )
                
                # Select/Deselect All button
                with gr.Row():
                    select_all_btn = gr.Button("🔘 Deselect All", size="sm")
                    
                # Function for select/deselect all
                def toggle_all_entities(current_selection):
                    if len(current_selection) > 0:
                        # If any are selected, deselect all
                        return [], "☑️ Select All"
                    else:
                        # If none selected, select all
                        return STANDARD_ENTITIES, "🔘 Deselect All"
                
                select_all_btn.click(
                    fn=toggle_all_entities,
                    inputs=[standard_entities],
                    outputs=[standard_entities, select_all_btn]
                )
            
            with gr.Column():
                gr.Markdown("### ✨ Custom Entity Types")
                custom_entities = gr.Textbox(
                    label="Custom Entities (comma-separated)",
                    placeholder="e.g. relationships, occupations, skills, emotions",
                    lines=3
                )
                gr.Markdown("""
                **Examples:**
                - relationships, occupations, skills
                - emotions, actions, objects  
                - medical conditions, treatments
                - financial terms, business roles
                """)
        
        analyze_btn = gr.Button("🔍 Analyze Text", variant="primary", size="lg")
        
        # Output sections
        with gr.Row():
            summary_output = gr.Markdown(label="Summary")
        
        with gr.Row():
            highlighted_output = gr.HTML(label="Highlighted Text")
        
        with gr.Row():
            table_output = gr.HTML(label="Detailed Results (Tabbed)")
        
        # Connect the button to the processing function
        analyze_btn.click(
            fn=process_text,
            inputs=[
                text_input,
                standard_entities,
                custom_entities,
                confidence_threshold,
                model_dropdown
            ],
            outputs=[summary_output, highlighted_output, table_output]
        )
        
        # Add examples
        gr.Examples(
            examples=[
                [
                    "John Smith works at Google in New York. He graduated from Stanford University in 2015 and specializes in artificial intelligence research. His wife Sarah is a doctor at Mount Sinai Hospital.",
                    ["PER", "ORG", "LOC", "DATE"],
                    "relationships, occupations, educational background",
                    0.3,
                    "spacy_en_core_web_sm"
                ],
                [
                    "The meeting between CEO Jane Doe and the board of directors at Microsoft headquarters in Seattle discussed the Q4 financial results and the new AI strategy for 2024.",
                    ["PER", "ORG", "LOC", "DATE"],
                    "corporate roles, business events, financial terms",
                    0.4,
                    "flair_ner-ontonotes-large"
                ],
                [
                    "Dr. Emily Watson published a research paper on machine learning algorithms at MIT. She collaborates with her colleague Prof. David Chen on natural language processing projects.",
                    ["PER", "ORG", "WORK_OF_ART"],
                    "academic titles, research topics, collaborations",
                    0.3,
                    "gliner_medium-v2.1"
                ]
            ],
            inputs=[
                text_input,
                standard_entities,
                custom_entities,
                confidence_threshold,
                model_dropdown
            ]
        )

    return demo

if __name__ == "__main__":
    demo = create_interface()
    demo.launch()