import gradio as gr
import torch
from gliner import GLiNER
import pandas as pd
import warnings
import random
import re
import time
warnings.filterwarnings('ignore')
# Common NER entity types (using full names)
STANDARD_ENTITIES = [
'DATE', 'EVENT', 'FACILITY', 'GEOPOLITICAL ENTITY', 'LANGUAGE', 'LOCATION',
'MISCELLANEOUS', 'NATIONALITIES/GROUPS', 'ORGANIZATION', 'PERSON', 'PRODUCT', 'WORK OF ART'
]
# Colour schemes (updated to match full names)
STANDARD_COLORS = {
'DATE': '#FF6B6B', # Red
'EVENT': '#4ECDC4', # Teal
'FACILITY': '#45B7D1', # Blue
'GEOPOLITICAL ENTITY': '#F9CA24', # Yellow
'LANGUAGE': '#6C5CE7', # Purple
'LOCATION': '#A0E7E5', # Light Cyan
'MISCELLANEOUS': '#FD79A8', # Pink
'NATIONALITIES/GROUPS': '#8E8E93', # Grey
'ORGANIZATION': '#55A3FF', # Light Blue
'PERSON': '#00B894', # Green
'PRODUCT': '#E17055', # Orange-Red
'WORK OF ART': '#DDA0DD' # Plum
}
# Entity definitions for glossary (alphabetically ordered with full name (abbreviation) format)
ENTITY_DEFINITIONS = {
'DATE': 'Date (DATE): Absolute or relative dates or periods',
'EVENT': 'Event (EVENT): Named hurricanes, battles, wars, sports events, etc.',
'FACILITY': 'Facility (FAC): Buildings, airports, highways, bridges, etc.',
'GEOPOLITICAL ENTITY': 'Geopolitical Entity (GPE): Countries, cities, states',
'LANGUAGE': 'Language (LANG): Any named language',
'LOCATION': 'Location (LOC): Non-GPE locations - Mountain ranges, bodies of water',
'MISCELLANEOUS': 'Miscellaneous (MISC): Entities that don\'t fit elsewhere',
'NATIONALITIES/GROUPS': 'Nationalities/Groups (NORP): Nationalities or religious or political groups',
'ORGANIZATION': 'Organization (ORG): Companies, agencies, institutions, etc.',
'PERSON': 'Person (PER): People, including fictional characters',
'PRODUCT': 'Product (PRODUCT): Objects, vehicles, foods, etc. (Not services)',
'WORK OF ART': 'Work of Art (Work of Art): Titles of books, songs, movies, paintings, etc.'
}
# Additional colours 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 = [
'flair_ner-large',
'spacy_en_core_web_trf',
'flair_ner-ontonotes-large',
'gliner_knowledgator/modern-gliner-bi-large-v1.0'
]
# Mapping from full names to abbreviations for model compatibility
self.entity_mapping = {
'DATE': 'DATE',
'EVENT': 'EVENT',
'FACILITY': 'FAC',
'GEOPOLITICAL ENTITY': 'GPE',
'LANGUAGE': 'LANG',
'LOCATION': 'LOC',
'MISCELLANEOUS': 'MISC',
'NATIONALITIES/GROUPS': 'NORP',
'ORGANIZATION': 'ORG',
'PERSON': 'PER',
'PRODUCT': 'PRODUCT',
'WORK OF ART': 'Work of Art'
}
# Reverse mapping for display
self.abbrev_to_full = {v: k for k, v in self.entity_mapping.items()}
def load_model(self, model_name):
"""Load the specified model"""
try:
if 'spacy' in model_name:
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 common NER"""
if self.spacy_model is None:
try:
import spacy
try:
# Try transformer model first, fallback to small model
self.spacy_model = spacy.load("en_core_web_trf")
print("✓ spaCy transformer model loaded successfully")
except OSError:
try:
self.spacy_model = spacy.load("en_core_web_sm")
print("✓ spaCy common 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")
print("✓ Flair OntoNotes model loaded successfully")
else:
model = SequenceTagger.load("flair/ner-english-large")
print("✓ Flair large model loaded successfully")
self.flair_models[model_name] = model
except Exception as e:
print(f"Error loading {model_name}: {str(e)}")
# Fallback to GLiNER
return self.load_gliner_model()
return self.flair_models[model_name]
def load_gliner_model(self):
"""Load GLiNER model for custom entities"""
if self.gliner_model is None:
try:
# Try the modern GLiNER model first, fallback to stable model
self.gliner_model = GLiNER.from_pretrained("knowledgator/gliner-bi-large-v1.0")
print("✓ GLiNER knowledgator model loaded successfully")
except Exception as e:
print(f"Primary GLiNER model failed: {str(e)}")
try:
# Fallback to stable model
self.gliner_model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")
print("✓ GLiNER fallback model loaded successfully")
except Exception as e2:
print(f"Error loading GLiNER model: {str(e2)}")
return None
return self.gliner_model
def assign_colours(self, standard_entities, custom_entities):
"""Assign colours to all entity types"""
self.all_entity_colors = {}
# Assign common colours
for entity in standard_entities:
self.all_entity_colors[entity.upper()] = STANDARD_COLORS.get(entity.upper(), '#CCCCCC')
# Assign custom colours
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 colour 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"""
# Convert full names to abbreviations for model processing
abbrev_types = []
for entity in entity_types:
if entity in self.entity_mapping:
abbrev_types.append(self.entity_mapping[entity])
else:
abbrev_types.append(entity)
if 'spacy' in model_name:
return self.extract_spacy_entities(text, abbrev_types)
elif 'flair' in model_name:
return self.extract_flair_entities(text, abbrev_types, model_name)
elif 'gliner' in model_name:
return self.extract_gliner_entities(text, abbrev_types, threshold, is_custom=False)
else:
return []
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:
# Convert abbreviation back to full name for display
display_label = self.abbrev_to_full.get(ent.label_, ent.label_)
entities.append({
'text': ent.text,
'label': display_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 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 common 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:
# Convert abbreviation back to full name for display
display_label = self.abbrev_to_full.get(label, label)
entities.append({
'text': entity.text,
'label': display_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:
# Convert abbreviation back to full name for display if not custom
if not is_custom:
display_label = self.abbrev_to_full.get(entity['label'].upper(), entity['label'].upper())
else:
display_label = entity['label'].upper()
result.append({
'text': entity['text'],
'label': display_label,
'start': entity['start'],
'end': entity['end'],
'confidence': entity.get('score', 0.0),
'source': 'GLiNER-Custom' if is_custom else 'GLiNER-Common'
})
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 - specifically entities found by BOTH common NER models AND custom 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 (same text span or overlapping positions)
if (current_entity['start'] <= next_entity['start'] < current_entity['end'] or
next_entity['start'] <= current_entity['start'] < current_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 only if we have BOTH common and custom entities
if len(overlapping_entities) == 1:
shared_entities.append(overlapping_entities[0])
else:
# Check if this is a true "shared" entity (common + custom)
has_common = False
has_custom = False
for entity in overlapping_entities:
source = entity.get('source', '')
if source in ['spaCy', 'GLiNER-Common'] or source.startswith('Flair-'):
has_common = True
elif source == 'GLiNER-Custom':
has_custom = True
if has_common and has_custom:
# This is a true shared entity (common + custom)
shared_entity = share_entities(overlapping_entities)
shared_entities.append(shared_entity)
else:
# These are just overlapping entities from the same source type, keep separate
shared_entities.extend(overlapping_entities)
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"
"
# 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 colours
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"""
📝 Text with Highlighted Entities
{highlighted_text}
"""
def create_single_entity_html(entity, entity_colors):
"""Create HTML for a single entity"""
label = entity['label']
colour = entity_colors.get(label.upper(), '#CCCCCC')
confidence = entity.get('confidence', 0.0)
source = entity.get('source', 'Unknown')
return (f''
f'{entity["text"]}')
def create_shared_entity_html(entity, entity_colors):
"""Create HTML for a shared entity with multiple colours"""
labels = entity['labels']
sources = entity['sources']
confidences = entity['confidences']
# Get colours for each label
colours = []
for label in labels:
colour = entity_colors.get(label.upper(), '#CCCCCC')
colours.append(colour)
# Create gradient background
if len(colours) == 2:
gradient = f"linear-gradient(to right, {colours[0]} 50%, {colours[1]} 50%)"
else:
# For more colours, create equal segments
segment_size = 100 / len(colours)
gradient_parts = []
for i, colour in enumerate(colours):
start = i * segment_size
end = (i + 1) * segment_size
gradient_parts.append(f"{colour} {start}%, {colour} {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''
f'{entity["text"]} 🤝')
def create_entity_table_html(entities_of_type, entity_type, colour, is_shared=False):
"""Create HTML table for a specific entity type"""
if is_shared:
table_html = f"""
| Entity Text |
All Labels |
Sources |
Count |
"""
for entity in entities_of_type:
labels_text = " | ".join(entity['labels'])
sources_text = " | ".join(entity['sources'])
table_html += f"""
| {entity['text']} |
{labels_text} |
{sources_text} |
{entity['entity_count']}
|
"""
else:
table_html = f"""
| Entity Text |
Confidence |
Type |
Source |
"""
# 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_colour = "#28a745" if confidence > 0.7 else "#ffc107" if confidence > 0.4 else "#dc3545"
source = entity.get('source', 'Unknown')
source_badge = f"{source}"
table_html += f"""
| {entity['text']} |
{confidence:.3f}
|
{entity['label']} |
{source_badge} |
"""
table_html += "
"
return table_html
def create_all_entity_tables(entities, entity_colors):
"""Create all entity tables in a single container"""
if not entities:
return "No entities found.
"
# 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 "No entities found.
"
# Create container with all tables
all_tables_html = """
"""
# Create quick navigation
all_tables_html += '
'
all_tables_html += '
Quick Navigation:'
# Sort entity groups to show shared entities first
sorted_groups = []
if 'SHARED_ENTITIES' in entity_groups:
sorted_groups.append(('SHARED_ENTITIES', entity_groups['SHARED_ENTITIES']))
for entity_type, entities_list in sorted(entity_groups.items()):
if entity_type != 'SHARED_ENTITIES':
sorted_groups.append((entity_type, entities_list))
for entity_type, entities_list in sorted_groups:
if entity_type == 'SHARED_ENTITIES':
icon = '🤝'
label = 'Shared'
else:
icon = '🎯' if entity_type in STANDARD_ENTITIES else '✨'
label = entity_type
all_tables_html += f'
{icon} {label} ({len(entities_list)})'
all_tables_html += '
'
# Add shared entities section if any
if 'SHARED_ENTITIES' in entity_groups:
shared_entities_list = entity_groups['SHARED_ENTITIES']
all_tables_html += f"""
{create_entity_table_html(shared_entities_list, 'SHARED_ENTITIES', '#666666', is_shared=True)}
"""
# Add other entity types
for entity_type, entities_of_type in sorted(entity_groups.items()):
if entity_type == 'SHARED_ENTITIES':
continue
colour = entity_colors.get(entity_type.upper(), '#f0f0f0')
is_standard = entity_type in STANDARD_ENTITIES
icon = "🎯" if is_standard else "✨"
type_label = "Common NER" if is_standard else "Custom GLiNER"
all_tables_html += f"""
{create_entity_table_html(entities_of_type, entity_type, colour)}
"""
all_tables_html += "
"
return all_tables_html
def create_legend_html(entity_colors, standard_entities, custom_entities):
"""Create a legend showing entity colours"""
if not entity_colors:
return ""
html = ""
html += "
🎨 Entity Type Legend
"
if standard_entities:
html += "
"
html += "
🎯 Common Entities:
"
html += "
"
for entity_type in standard_entities:
colour = entity_colors.get(entity_type.upper(), '#ccc')
html += f"{entity_type}"
html += "
"
if custom_entities:
html += "
"
html += "
✨ Custom Entities:
"
html += "
"
for entity_type in custom_entities:
colour = entity_colors.get(entity_type.upper(), '#ccc')
html += f"{entity_type}"
html += "
"
html += "
"
return html
# Initialize the NER manager
ner_manager = HybridNERManager()
def process_text(text, standard_entities, custom_entities_str, confidence_threshold, selected_model, progress=gr.Progress()):
"""Main processing function for Gradio interface with progress tracking"""
if not text.strip():
return "❌ Please enter some text to analyse", "", "", gr.update(visible=False)
progress(0.1, desc="Initialising...")
# 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 common 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 common entity type OR enter custom entity types", "", "", gr.update(visible=False)
progress(0.2, desc="Loading models...")
all_entities = []
# Extract common entities using selected model
if selected_standard and selected_model:
progress(0.4, desc="Extracting common entities...")
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:
progress(0.6, desc="Extracting 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.", "", "", gr.update(visible=False)
progress(0.8, desc="Processing results...")
# Assign colours
entity_colors = ner_manager.assign_colours(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)
results_html = create_all_entity_tables(all_entities, entity_colors)
progress(0.9, desc="Creating summary...")
# 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}
"""
progress(1.0, desc="Complete!")
return summary, legend_html + highlighted_html, results_html, gr.update(visible=True)
# Create Gradio interface
def create_interface():
with gr.Blocks(title="Hybrid NER + GLiNER Tool", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# Named Entity Recognition (NER) Explorer Tool
Combine common Named Entity Recognition (NER) categories with your own custom entity types!
This tool uses both traditional NER models and GLiNER for comprehensive entity extraction, allowing you to explore what NER looks like in practice.
### How to use this tool:
1. **📝 Enter your text** in the text area below
2. **🎯 Select a model** from the dropdown menu
3. **☑️ Select common entity types** you want to identify (PERSON, ORGANIZATION, LOCATION, etc.)
4. **✨ Add custom entities** (comma-separated) like "relationships, occupations, skills"
5. **⚙️ Adjust confidence threshold**
6. **🔍 Click "Analyse Text"** to see results
(NB: common/custom entities which overlap are shown with split-colour highlighting)
7. **🔄 Refresh the page** to try again with new text
""")
# Add tip box
gr.HTML("""
💡 Top tip: No model is perfect - all can miss and/or incorrectly identify entity types.
""")
with gr.Row():
with gr.Column(scale=2):
text_input = gr.Textbox(
label="📝 Text to Analyse",
placeholder="Enter your text here...",
lines=12,
max_lines=20
)
with gr.Column(scale=1):
confidence_threshold = gr.Slider(
minimum=0.1,
maximum=0.9,
value=0.3,
step=0.1,
label="🎚️ Confidence Threshold"
)
# Add confidence threshold explanation
gr.HTML("""
ℹ️ Understanding the Confidence Threshold
The confidence threshold controls how certain the model needs to be before identifying an entity:
- Lower values (0.1-0.3): More entities detected, but may include false positives
- Medium values (0.4-0.6): Balanced detection with moderate confidence
- Higher values (0.7-0.9): Only highly confident entities detected, may miss some valid entities
Top Tip: Start with 0.3 for comprehensive detection, then adjust based on your needs.
""")
with gr.Row():
with gr.Column():
gr.Markdown("### 🎯 Common Entity Types")
# Model selector
model_dropdown = gr.Dropdown(
choices=ner_manager.model_names,
value=ner_manager.model_names[0],
label="Select NER Model for Common Entities"
)
# Common entities with select all functionality
standard_entities = gr.CheckboxGroup(
choices=STANDARD_ENTITIES,
value=['PERSON', 'ORGANIZATION', 'LOCATION', 'MISCELLANEOUS'], # Default selection with full names
label="Select Common 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 - Powered by GLiNER")
custom_entities = gr.Textbox(
label="Custom Entities (comma-separated)",
placeholder="e.g. relationships, civilian occupations, emotions",
lines=3
)
gr.Markdown("""
**Examples:**
- relationships, occupations, skills
- emotions, actions, objects
- medical conditions, treatments
""")
# Add glossary (alphabetically ordered)
gr.HTML("""
ℹ️ Entity Type Definitions
- Date (DATE):
- Absolute or relative dates or periods
- Event (EVENT):
- Named hurricanes, battles, wars, sports events, etc.
- Facility (FAC):
- Buildings, airports, highways, bridges, etc.
- Geopolitical Entity (GPE):
- Countries, cities, states
- Language (LANG):
- Any named language
- Location (LOC):
- Non-GPE locations - Mountain ranges, bodies of water
- Miscellaneous (MISC):
- Entities that don't fit elsewhere
- Nationalities/Groups (NORP):
- Nationalities or religious or political groups
- Organization (ORG):
- Companies, agencies, institutions, etc.
- Person (PER):
- People, including fictional characters
- Product (PRODUCT):
- Objects, vehicles, foods, etc. (Not services)
- Work of Art (Work of Art):
- Titles of books, songs, movies, paintings, etc.
""")
analyse_btn = gr.Button("🔍 Analyse Text", variant="primary", size="lg")
# Add horizontal line separator after the button
gr.HTML("
")
# Output sections
with gr.Row():
summary_output = gr.Markdown(label="Summary")
with gr.Row():
highlighted_output = gr.HTML(label="Highlighted Text")
# Results section (initially hidden)
with gr.Row(visible=False) as results_row:
with gr.Column():
gr.Markdown("### 📋 Detailed Results")
results_output = gr.HTML(label="Entity Results")
# Connect the button to the processing function
analyse_btn.click(
fn=process_text,
inputs=[
text_input,
standard_entities,
custom_entities,
confidence_threshold,
model_dropdown
],
outputs=[summary_output, highlighted_output, results_output, results_row]
)
# Updated examples text with reduced spacing
with gr.Column():
gr.Markdown("""
### 💡 No example text to test? No problem!
Simply click on one of the examples provided below, and the fields will be populated for you.
""", elem_id="examples-heading")
gr.Examples(
examples=[
[
"On June 6, 1944, Allied forces launched Operation Overlord, the invasion of Normandy. General Dwight D. Eisenhower commanded the operation, while Field Marshal Bernard Montgomery led ground forces. The BBC broadcast coded messages to the French Resistance, including the famous line 'The long sobs of autumn violins.'",
["PERSON", "ORGANIZATION", "LOCATION", "DATE", "EVENT"],
"military operations, military ranks, historical battles",
0.3,
"spacy_en_core_web_trf"
],
[
"In Jane Austen's 'Pride and Prejudice', Elizabeth Bennet first meets Mr. Darcy at the Meryton assembly. The novel, published in 1813, explores themes of marriage and social class in Regency England. Austen wrote to her sister Cassandra about the manuscript while staying at Chawton Cottage.",
["PERSON", "LOCATION", "DATE", "WORK OF ART"],
"literary themes, relationships, literary periods",
0.3,
"gliner_knowledgator/modern-gliner-bi-large-v1.0"
],
[
"Charles Darwin arrived at the Galápagos Islands aboard HMS Beagle in September 1835. During his five-week visit, Darwin collected specimens of finches, tortoises, and mockingbirds. His observations of these species' variations across different islands later contributed to his theory of evolution by natural selection, published in 'On the Origin of Species' in 1859.",
["PERSON", "LOCATION", "DATE", "WORK OF ART", "PRODUCT"],
"scientific concepts, species, research methods",
0.3,
"flair_ner-large"
]
],
inputs=[
text_input,
standard_entities,
custom_entities,
confidence_threshold,
model_dropdown
],
label="Examples"
)
# Add custom CSS to make Examples label black and improve entity label readability
gr.HTML("""
""")
# Add model information links
gr.HTML("""
📚 Model Information & Documentation (incl. details on usage terms)
Learn more about the models used in this tool:
This NER Explorer Tool was created as part of the
Digital Scholarship at Oxford (DiSc)
funded research project:
Extracting Keywords from Crowdsourced Collections.
The code for this tool was built with the aid of Claude Opus 4.
""")
return demo
if __name__ == "__main__":
demo = create_interface()
demo.launch()