app.py

import gradio as gr
from gliner import GLiNER
import torch

# Load the model
device = "cuda" if torch.cuda.is_available() else "cpu"
model = GLiNER.from_pretrained("Rishi2455/gliner-floatbot-ai").to(device)

def extract_entities(text, labels, threshold):
    """Extract named entities from text using GLiNER."""
    if not text or not labels:
        return {"entities": []}

    # Split comma-separated labels
    label_list = [label.strip().lower() for label in labels.split(",") if label.strip()]

    # Perform prediction
    entities = model.predict_entities(text, label_list, threshold=threshold)

    # Format results
    results = []
    for entity in entities:
        results.append({
            "text": entity["text"],
            "label": entity["label"],
            "score": round(entity["score"], 4)
        })

    return {"entities": results}

# Build Gradio interface
demo = gr.Interface(
    fn=extract_entities,
    inputs=[
        gr.Textbox(label="Text", placeholder="Enter text to analyze...", lines=5),
        gr.Textbox(label="Labels (comma-separated)", placeholder="person, organization, location"),
        gr.Slider(minimum=0.0, maximum=1.0, value=0.4, step=0.05, label="Confidence Threshold"),
    ],
    outputs=gr.JSON(label="Extracted Entities"),
    title="GLiNER Named Entity Recognition",
    description="Extract named entities from text using GLiNER. Enter your text, specify entity labels (comma-separated), and adjust the confidence threshold.",
    examples=[
        ["Elon Musk founded SpaceX in Hawthorne, California.", "person, organization, location", 0.4],
        ["Apple released the iPhone 15 in September 2023.", "company, product, date", 0.4],
    ],
)

if __name__ == "__main__":
    demo.launch()