app.py

from typing import Dict, Union
import sys

sys.path.extend(["./GLiNER"])
from GLiNER.model import GLiNER
import gradio as gr

model = GLiNER.from_pretrained("urchade/gliner_multi")

examples = [
    [
        "Der Nationale Volkskongress hat für die chinesische Öffentlichkeit ebenso beruhigende wie finstere Nachrichten zu bieten. Leider werfen die schlechten Nachrichten ihre Schatten auf die guten: Ja, der Präsident und KP-Chef Xi Jinping unternimmt Anstrengungen, die schwächelnde Wirtschaft zu retten – nur hat er dabei noch mehr Macht an sich gerissen.  ",
        True,
    ],

]


def ner(text, labels: str, nested_ner: bool) -> Dict[str, Union[str, int, float]]:
    labels = labels.split(",")
    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)
        ],
    }


with gr.Blocks(title="GLiNER-multi") as demo:
    gr.Markdown(
        """
        # GLiNER-multi

        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

        * Paper: https://arxiv.org/abs/2311.08526
        * Repository: https://github.com/urchade/GLiNER
        """
    )
    with gr.Accordion("How to run this model locally", open=False):
        gr.Markdown(
            """
            ## Installation
            To use this model, you must download the GLiNER repository and install its dependencies:
            ```
            !git clone https://github.com/urchade/GLiNER.git
            %cd GLiNER
            !pip install -r requirements.txt
            ```
         
            ## Usage
            Once you've downloaded the GLiNER repository, you can import the GLiNER class from the `model` file. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
            """
        )
        gr.Code(
            '''
from model import GLiNER

model = GLiNER.from_pretrained("urchade/gliner_base")

text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""

labels = ["person", "award", "date", "competitions", "teams"]

entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["text"], "=>", entity["label"])
            ''',
            language="python",
        )
        gr.Code(
            """
Cristiano Ronaldo dos Santos Aveiro => person
5 February 1985 => date
Al Nassr => teams
Portugal national team => teams
Ballon d'Or => award
UEFA Men's Player of the Year Awards => award
European Golden Shoes => award
UEFA Champions Leagues => competitions
UEFA European Championship => competitions
UEFA Nations League => competitions
Champions League => competitions
European Championship => competitions
            """
        )

    input_text = gr.Textbox(
        value=examples[0][0], label="Text input", placeholder="Enter your text here"
    )
    with gr.Row() as row:
        labels = gr.Textbox(
            value=examples[0][1],
            label="Labels",
            placeholder="Enter your labels here (comma separated)",
            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, labels, nested_ner],
        outputs=output,
        cache_examples=True,
    )

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

demo.queue()
demo.launch(debug=True)