Create app.py

app.py

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+from typing import Dict, Union
+from gliner import GLiNER
+import gradio as gr
+
+model = GLiNER.from_pretrained("wjbmattingly/gliner-hrd")
+
+examples = [
+    [
+        "We discovered a collection of deer bones next to a burial mound. Inside the mound we found the finger of a human and twelve ribs from a separate person. We also found a bat wing and a cow skull. There was even a mummified human head.",
+        "human remains, animal remains, general remains",
+        0.3,
+        True,
+    ]
+]
+
+def ner(
+    text, labels: str, threshold: float, 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, threshold=threshold
+            )
+        ],
+    }
+
+
+with gr.Blocks(title="GLiNER-M-v2.1") as demo:
+    gr.Markdown(
+        """
+        # GLiNER-base
+        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/urchade/gliner_multi-v2.1
+        * All GLiNER models: https://huggingface.co/models?library=gliner
+        * Paper: https://arxiv.org/abs/2311.08526
+        * Repository: https://github.com/urchade/GLiNER
+        """
+    )
+    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=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, labels, threshold, nested_ner],
+        outputs=output,
+        cache_examples=True,
+    )
+
+    # Submitting
+    input_text.submit(
+        fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
+    )
+    labels.submit(
+        fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
+    )
+    threshold.release(
+        fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
+    )
+    submit_btn.click(
+        fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
+    )
+    nested_ner.change(
+        fn=ner, inputs=[input_text, labels, threshold, nested_ner], outputs=output
+    )
+
+demo.queue()
+demo.launch(debug=True)