app.py

import random
from gliner import GLiNER
import gradio as gr
from datasets import load_dataset

# Load the subset dataset from Hugging Face Hub
subset_dataset = load_dataset("TheBritishLibrary/blbooks", split="train", streaming=True, trust_remote_code=True)

# Load the GLiNER model
model = GLiNER.from_pretrained("max-long/textile_machines_3_oct", trust_remote_code=True)

# Define the NER function
def ner(text: str, labels: str, threshold: float, nested_ner: bool):
    labels = [label.strip() for label in labels.split(",")]
    entities = model.predict_entities(text, labels, flat_ner=not nested_ner, threshold=threshold)
    
    # Filter for "textile machinery" entities
    textile_entities = [
        {
            "entity": ent["label"],
            "word": ent["text"],
            "start": ent["start"],
            "end": ent["end"],
            "score": ent.get("score", 0),
        }
        for ent in entities
        if ent["label"].lower() == "textile machinery"
    ]
    
    # Highlight entities with HTML
    highlighted_text = text
    for ent in sorted(textile_entities, key=lambda x: x['start'], reverse=True):
        highlighted_text = (
            highlighted_text[:ent['start']] +
            f"<span style='background-color: yellow'>{highlighted_text[ent['start']:ent['end']]}</span>" +
            highlighted_text[ent['end']:]
        )
    
    return gr.HTML(highlighted_text), textile_entities

# Build Gradio interface
with gr.Blocks(title="Textile Machinery NER Demo") as demo:
    gr.Markdown(
        """
        # Textile Machinery Entity Recognition Demo
        This demo selects a random text snippet from the British Library's books dataset and identifies "textile machinery" entities using a fine-tuned GLiNER model.
        """
    )
    
    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("max-long/textile_machines_3_oct")
text = "Your sample text here."
labels = ["textile machinery"]
entities = model.predict_entities(text, labels)
for entity in entities:
    print(entity["text"], "=>", entity["label"])
            ''',
            language="python",
        )
        gr.Code(
            """
Textile Machine 1 => textile machinery
Textile Machine 2 => textile machinery
            """
        )
    
    input_text = gr.Textbox(
        value="Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.",
        label="Text input",
        placeholder="Enter your text here",
        lines=5
    )
    
    with gr.Row():
        labels = gr.Textbox(
            value="textile machinery",
            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=False,
            label="Nested NER",
            info="Allow for nested NER?",
            scale=0,
        )
    
    output = gr.HighlightedText(label="Predicted Entities")
    
    submit_btn = gr.Button("Analyze Random Snippet")
    refresh_btn = gr.Button("Get New Snippet")
    
    # Function to fetch a new random snippet
    def get_new_snippet():
        # WARNING: Streaming datasets may have performance implications
        try:
            sample = next(iter(subset_dataset))['text']
            return sample
        except StopIteration:
            return "No more snippets available."
    
    refresh_btn.click(fn=get_new_snippet, outputs=input_text)
    
    submit_btn.click(
        fn=ner,
        inputs=[input_text, labels, threshold, nested_ner],
        outputs=[output, gr.JSON(label="Entities")]
    )
    
    examples = [
        [
            "However, both models lack other frequent DM symptoms including the fibre-type dependent atrophy, myotonia, cataract and male-infertility.",
            "textile machinery",
            0.3,
            False,
        ],
        # Add more examples as needed
    ]
    
    gr.Examples(
        examples=examples,
        inputs=[input_text, labels, threshold, nested_ner],
        outputs=[output, gr.JSON(label="Entities")],
        fn=ner,
        label="Examples",
        cache_examples=True,
    )

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