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Update app.py
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app.py
CHANGED
@@ -30,22 +30,6 @@ STANDARD_COLORS = {
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'WORK OF ART': '#DDA0DD' # Plum
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}
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# Entity definitions for glossary
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ENTITY_DEFINITIONS = {
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'DATE': 'Absolute or relative dates or periods',
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'EVENT': 'Named hurricanes, battles, wars, sports events, etc.',
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'FAC': 'Facilities - Buildings, airports, highways, bridges, etc.',
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'GPE': 'Geopolitical entities - Countries, cities, states',
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'LANG': 'Any named language',
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'LOC': 'Non-GPE locations - Mountain ranges, bodies of water',
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'MISC': 'Miscellaneous entities - Things that don\'t fit elsewhere',
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'NORP': 'Nationalities or religious or political groups',
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'ORG': 'Organizations - Companies, agencies, institutions, etc.',
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'PER': 'People, including fictional characters',
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'PRODUCT': 'Objects, vehicles, foods, etc. (Not services)',
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'Work of Art': 'Titles of books, songs, movies, paintings, etc.'
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}
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# Additional colours for custom entities
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CUSTOM_COLOR_PALETTE = [
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'#FF9F43', '#10AC84', '#EE5A24', '#0FBC89', '#5F27CD',
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@@ -746,40 +730,15 @@ def process_text(text, standard_entities, custom_entities_str, confidence_thresh
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return summary, legend_html + highlighted_html, results_html
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# Create colored checkbox labels
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def create_colored_checkbox_labels():
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"""Create checkbox labels with color indicators"""
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labels = []
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for entity in STANDARD_ENTITIES:
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colour_key = "WORK OF ART" if entity == "Work of Art" else entity.upper()
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colour = STANDARD_COLORS.get(colour_key, '#CCCCCC')
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# Create label with color dot
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labels.append(f"β {entity}")
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return labels
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# Create Gradio interface
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def create_interface():
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custom_css = """
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/* Color the checkbox labels based on entity type */
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"""
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for i, entity in enumerate(STANDARD_ENTITIES):
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colour_key = "WORK OF ART" if entity == "Work of Art" else entity.upper()
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colour = STANDARD_COLORS.get(colour_key, '#CCCCCC')
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custom_css += f"""
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label[for="checkbox-{i}"] span:first-child {{
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color: {colour} !important;
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font-weight: bold;
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}}
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"""
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with gr.Blocks(title="Hybrid NER + GLiNER Tool", theme=gr.themes.Soft(), css=custom_css) as demo:
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gr.Markdown("""
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# π―
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Combine common NER categories with your own custom entity types! This tool uses both traditional NER models and GLiNER for comprehensive entity extraction.
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## π€
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### How to use:
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1. **π Enter your text** in the text area below
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@@ -812,14 +771,6 @@ def create_interface():
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with gr.Column():
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gr.Markdown("### π― Common Entity Types")
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# Add the inline help text
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gr.HTML("""
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<div style="background-color: #e3f2fd; padding: 10px; border-radius: 5px; margin-bottom: 10px; font-size: 13px;">
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<strong>Quick Guide:</strong> PER = People | ORG = Organizations | LOC = Locations |
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GPE = Countries/Cities | FAC = Facilities | DATE = Dates | EVENT = Named Events
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</div>
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""")
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# Model selector
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model_dropdown = gr.Dropdown(
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choices=ner_manager.model_names,
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info="Choose which model to use for common NER"
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)
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#
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glossary_html = """
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<details style="margin: 10px 0; padding: 10px; background-color: #f8f9fa; border-radius: 8px; border: 1px solid #ddd;">
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<summary style="cursor: pointer; font-weight: bold; padding: 5px; color: #1976d2;">
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βΉοΈ Detailed Entity Type Definitions (Click to expand)
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</summary>
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<div style="margin-top: 10px; padding: 10px;">
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<dl style="margin: 0; font-size: 14px;">
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"""
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for entity, definition in ENTITY_DEFINITIONS.items():
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colour_key = "WORK OF ART" if entity == "Work of Art" else entity.upper()
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colour = STANDARD_COLORS.get(colour_key, '#CCCCCC')
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glossary_html += f"""
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<div style="margin-bottom: 8px;">
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<dt style="font-weight: bold; display: inline; color: {colour};">{entity}:</dt>
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<dd style="display: inline; margin-left: 5px;">{definition}</dd>
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</div>
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"""
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glossary_html += """
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</dl>
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</div>
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</details>
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"""
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gr.HTML(glossary_html)
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# Common entities checkboxes with colored labels
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standard_entities = gr.CheckboxGroup(
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choices=
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value=[
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label="Select Common Entities"
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elem_id="standard-entities-checkbox"
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)
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# Select/Deselect All button
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@@ -874,7 +797,7 @@ def create_interface():
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return [], "βοΈ Select All"
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else:
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# If none selected, select all
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return
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select_all_btn.click(
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fn=toggle_all_entities,
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@@ -883,7 +806,7 @@ def create_interface():
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)
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with gr.Column():
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gr.Markdown("### β¨ Custom Entity Types
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custom_entities = gr.Textbox(
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label="Custom Entities (comma-separated)",
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placeholder="e.g. relationships, occupations, skills, emotions",
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@@ -895,8 +818,6 @@ def create_interface():
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- emotions, actions, objects
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- medical conditions, treatments
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- financial terms, business roles
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*GLiNER model will extract these custom entity types from your text*
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""")
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analyse_btn = gr.Button("π Analyse Text", variant="primary", size="lg")
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gr.Markdown("### π Detailed Results")
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results_output = gr.HTML(label="Entity Results")
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# Function to process with colored labels
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def process_with_colored_labels(text, colored_labels, custom_entities, confidence_threshold, selected_model, progress=gr.Progress()):
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# Remove the color dots from labels
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standard_entities = [label.replace("β ", "") for label in colored_labels]
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return process_text(text, standard_entities, custom_entities, confidence_threshold, selected_model, progress)
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# Connect the button to the processing function
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analyse_btn.click(
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fn=
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inputs=[
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text_input,
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standard_entities,
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@@ -938,21 +853,21 @@ def create_interface():
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examples=[
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[
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"John Smith works at Google in New York. He graduated from Stanford University in 2015 and specialises in artificial intelligence research. His wife Sarah is a doctor at Mount Sinai Hospital.",
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[
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"relationships, occupations, educational background",
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0.3,
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"entities_spacy_en_core_web_trf"
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],
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[
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"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.",
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[
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"corporate roles, business events, financial terms",
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0.4,
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"entities_flair_ner-ontonotes-large"
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],
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[
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"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.",
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[
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"academic titles, research topics, collaborations",
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0.3,
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"entities_gliner_knowledgator/modern-gliner-bi-large-v1.0"
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model_dropdown
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]
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)
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# Add model information links
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gr.HTML("""
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<hr style="margin-top: 40px; margin-bottom: 20px;">
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'WORK OF ART': '#DDA0DD' # Plum
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}
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# Additional colours for custom entities
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CUSTOM_COLOR_PALETTE = [
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'#FF9F43', '#10AC84', '#EE5A24', '#0FBC89', '#5F27CD',
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return summary, legend_html + highlighted_html, results_html
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# Create Gradio interface
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def create_interface():
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with gr.Blocks(title="Hybrid NER + GLiNER Tool", theme=gr.themes.Soft()) as demo:
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gr.Markdown("""
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# π― Named Entity Recognition (NER) Explorer Tool
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Combine common NER categories with your own custom entity types! This tool uses both traditional NER models and GLiNER for comprehensive entity extraction.
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## π€ Overlapping entities are automatically shared with split-colour highlighting!
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### How to use:
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1. **π Enter your text** in the text area below
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with gr.Column():
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gr.Markdown("### π― Common Entity Types")
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# Model selector
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model_dropdown = gr.Dropdown(
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choices=ner_manager.model_names,
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info="Choose which model to use for common NER"
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)
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# Common entities with select all functionality
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standard_entities = gr.CheckboxGroup(
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choices=STANDARD_ENTITIES,
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value=['PER', 'ORG', 'LOC', 'MISC'], # Default selection
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label="Select Common Entities"
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)
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# Select/Deselect All button
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return [], "βοΈ Select All"
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else:
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# If none selected, select all
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return STANDARD_ENTITIES, "π Deselect All"
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select_all_btn.click(
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fn=toggle_all_entities,
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)
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with gr.Column():
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gr.Markdown("### β¨ Custom Entity Types")
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custom_entities = gr.Textbox(
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label="Custom Entities (comma-separated)",
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placeholder="e.g. relationships, occupations, skills, emotions",
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- emotions, actions, objects
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- medical conditions, treatments
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- financial terms, business roles
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""")
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analyse_btn = gr.Button("π Analyse Text", variant="primary", size="lg")
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gr.Markdown("### π Detailed Results")
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results_output = gr.HTML(label="Entity Results")
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# Connect the button to the processing function
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analyse_btn.click(
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fn=process_text,
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inputs=[
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text_input,
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standard_entities,
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examples=[
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[
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"John Smith works at Google in New York. He graduated from Stanford University in 2015 and specialises in artificial intelligence research. His wife Sarah is a doctor at Mount Sinai Hospital.",
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["PER", "ORG", "LOC", "DATE"],
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"relationships, occupations, educational background",
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0.3,
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"entities_spacy_en_core_web_trf"
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],
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[
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"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.",
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["PER", "ORG", "LOC", "DATE"],
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"corporate roles, business events, financial terms",
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0.4,
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"entities_flair_ner-ontonotes-large"
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],
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[
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"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.",
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["PER", "ORG", "Work of Art"],
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"academic titles, research topics, collaborations",
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0.3,
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"entities_gliner_knowledgator/modern-gliner-bi-large-v1.0"
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model_dropdown
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]
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)
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# Add model information links
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gr.HTML("""
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<hr style="margin-top: 40px; margin-bottom: 20px;">
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