app.py

from typing import Dict, Union
from gliner import GLiNER
import gradio as gr

jp_model = GLiNER.from_pretrained("vumichien/ner-jp-gliner")
meal_model = GLiNER.from_pretrained("vumichien/meals-gliner")


def merge_tokens(entities, text):
    # Remove spaces from the text
    merged_text = text.replace(" ", "")

    updated_entities = []
    for entity in entities:
        # Calculate the new start and end positions
        start = entity['start']
        end = entity['end']

        # Get the text without spaces
        entity_text = entity['text'].replace(" ", "")

        # Find the new start and end in the merged text
        new_start = merged_text.find(entity_text)
        new_end = new_start + len(entity_text)

        # Update the entity with new positions
        updated_entities.append({
            'start': new_start,
            'end': new_end,
            'text': entity_text,
            'label': entity['label'],
            'score': entity['score']
        })

    return updated_entities


examples = [
    [
        "SPRiNGSと最も仲の良いライバルグループ。",
        "ner_jp",
        "その他の組織名, 法人名, 地名, 人名",
        0.3,
        True,
    ],
    [
        "レッドフォックス株式会社は、東京都千代田区に本社を置くITサービス企業である",
        "ner_jp",
        "その他の組織名, 法人名, 地名, 人名",
        0.3,
        False,
    ],
    [
        "The aromatic flavors of Bangladesh's popular dessert, Mishti Doi, are elevated with a rich chocolate syrup. The velvety texture and subtle sweetness of the ice cream pair perfectly with the crunch of chopped nuts.",
        "ner_meals",
        "food, location, ingredient, dessert",
        0.5,
        True,
    ],
    [
        "The newly opened bakery in Lahore, Pakistan is famous for its freshly baked naan bread. The aroma of warm garlic and coriander wafts through the air, enticing customers to try their signature dish - the 'Lahori Naan'.",
        "ner_meals",
        "food, location, ingredient, dessert",
        0.5,
        False,
    ],
]


def ner(
    text, models:str, labels: str, threshold: float, nested_ner: bool
) -> Dict[str, Union[str, int, float]]:
    labels = labels.split(",")
    if models == "ner_jp":
        model = jp_model
        tokenized_text = " ".join(list(text))
        entities = model.predict_entities(tokenized_text, labels, flat_ner=not nested_ner, threshold=threshold)
        updated_entities = merge_tokens(entities, tokenized_text)
        return {
            "text": text,
            "entities": [
                {
                    "entity": entity["label"],
                    "word": entity["text"],
                    "start": entity["start"],
                    "end": entity["end"],
                    "score": 0,
                }
                for entity in updated_entities
            ],
        }
    else:
        model = meal_model
        return {
            "text": text,
            "entities": [
                {
                    "entity": entity["label"],
                    "word": entity["text"],
                    "start": entity["start"],
                    "end": entity["end"],
                    "score": 0,
                }
                for entity in model.predict_entities(
                    text, labels, flat_ner=not nested_ner, threshold=threshold
                )
            ],
        }


with gr.Blocks(title="GLiNER-M-v2.1") as demo:
    gr.Markdown(
        """
        # GLiNER-Japanese
        GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
        ## Links
        * Model: https://huggingface.co/vumichien/ner-jp-gliner
        * All GLiNER models: https://huggingface.co/models?library=gliner
        * Paper: https://arxiv.org/abs/2311.08526
        * Repository for finetune: https://github.com/vumichien/gliner-medium
        """
    )
    with gr.Accordion("How to run this model locally", open=False):
        gr.Markdown(
            """
            ## Installation
            To use this model, you must install the GLiNER Python library:
            ```
            !pip install gliner
            ```
         
            ## Usage
            Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
            """
        )
        gr.Code(
            '''
from gliner import GLiNER
model = GLiNER.from_pretrained("vumichien/meals-gliner", load_tokenizer=True)
text = """
The aromatic flavors of Bangladesh's popular dessert, Mishti Doi, are elevated with a rich chocolate syrup. The velvety texture and subtle sweetness of the ice cream pair perfectly with the crunch of chopped nuts.
"""
labels = ["food", "location", "ingredient", "dessert"]
entities = model.predict_entities(text, labels)
for entity in entities:
    print(entity["text"], "=>", entity["label"])
            ''',
            language="python",
        )
        gr.Code(
            """
Bangladesh => location
Mishti Doi => food
chocolate syrup => ingredient
ice cream => dessert
chopped nuts => ingredient
            """
        )

    input_text = gr.Textbox(
        value=examples[0][0], label="Text input", placeholder="Enter your text here"
    )
    with gr.Row() as row:
        models = gr.Dropdown(
            choices=["ner_meals", "ner_jp"],
            value="ner_jp",
            label="Models",
            scale=2,
        )
        labels = gr.Textbox(
            value=examples[0][2],
            label="Labels",
            placeholder="Enter your labels here (comma separated)",
            scale=2,
        )
        threshold = gr.Slider(
            0,
            1,
            value=0.3,
            step=0.01,
            label="Threshold",
            info="Lower the threshold to increase how many entities get predicted.",
            scale=1,
        )
        nested_ner = gr.Checkbox(
            value=examples[0][2],
            label="Nested NER",
            info="Allow for nested NER?",
            scale=0,
        )
    output = gr.HighlightedText(label="Predicted Entities")
    submit_btn = gr.Button("Submit")
    examples = gr.Examples(
        examples,
        fn=ner,
        inputs=[input_text, models, labels, threshold, nested_ner],
        outputs=output,
        cache_examples=True,
    )

    # Submitting
    input_text.submit(
        fn=ner, inputs=[input_text, models, labels, threshold, nested_ner], outputs=output
    )
    models.input(
        fn=ner, inputs=[input_text, models, labels, threshold, nested_ner], outputs=output
    )
    labels.submit(
        fn=ner, inputs=[input_text, models, labels, threshold, nested_ner], outputs=output
    )
    threshold.release(
        fn=ner, inputs=[input_text, models, labels, threshold, nested_ner], outputs=output
    )
    submit_btn.click(
        fn=ner, inputs=[input_text, models, labels, threshold, nested_ner], outputs=output
    )
    nested_ner.change(
        fn=ner, inputs=[input_text, models, labels, threshold, nested_ner], outputs=output
    )

demo.queue()
demo.launch()