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app.py

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+import time
+import gradio as gr
+from gliner import GLiNER
+
+model = GLiNER.from_pretrained("Ihor/gliner-biomed-base-v1.0")
+
+MAX_LABELS = 12
+
+PALETTE = [
+    "#FEF0C3", "#E8D5B7", "#F5E6CC", "#FFDDBE", "#D2E3FC", "#C8E6C9",
+    "#EADDFF", "#F9DEDC", "#B2DFDB", "#FFE0B2", "#F0F4C3", "#BBDEFB",
+]
+
+DEFAULT_LABELS = ["patient_name", "age", "sex", "symptom", "diagnosis", "medication", "vital_sign", "procedure"]
+
+
+def extract(text, labels_list, threshold):
+    labels = [l for l in labels_list if l][:MAX_LABELS]
+    if not labels or not text.strip():
+        return None, [], ""
+
+    color_map = {l: PALETTE[i % len(PALETTE)] for i, l in enumerate(labels)}
+
+    start = time.perf_counter()
+    entities = model.predict_entities(text, labels, threshold=threshold)
+    latency_ms = (time.perf_counter() - start) * 1000
+
+    entities = sorted(entities, key=lambda e: e["start"])
+
+    hl_entities = [{"entity": e["label"], "start": e["start"], "end": e["end"]} for e in entities]
+    table = [[e["label"], e["text"], f"{e['score']:.2f}"] for e in entities]
+
+    return (
+        gr.HighlightedText(value={"text": text, "entities": hl_entities}, color_map=color_map),
+        table,
+        f"{latency_ms:.1f} ms | {len(entities)} entities",
+    )
+
+
+EXAMPLES = [
+    [
+        """Patient: Jane Doe, 58-year-old female.
+Chief Complaint: Chest pain and shortness of breath for 2 days.
+
+History of Present Illness:
+Patient presents with substernal chest pain radiating to the left arm,
+rated 7/10, worsening with exertion. She reports associated dyspnea and
+diaphoresis. She has a history of Type 2 Diabetes Mellitus diagnosed in
+2015 and Hypertension diagnosed in 2018.
+
+Current Medications:
+- Metformin 1000mg PO BID for diabetes
+- Lisinopril 20mg PO daily for hypertension
+- Aspirin 81mg PO daily for cardiac prophylaxis
+
+Vitals: BP 158/92, HR 96, SpO2 94%, Temp 98.6F
+
+Assessment:
+1. Acute coronary syndrome - rule out myocardial infarction
+2. Uncontrolled hypertension
+3. Type 2 Diabetes Mellitus - stable on current regimen
+
+Plan:
+- Stat ECG and troponin levels
+- Start Heparin drip 18 units/kg/hr IV
+- Nitroglycerin 0.4mg sublingual PRN chest pain
+- Cardiology consult
+- Continue home medications""",
+        DEFAULT_LABELS,
+        0.4,
+    ],
+    [
+        """DISCHARGE SUMMARY
+Patient: Robert Chen, 72-year-old male.
+Admission Date: 2024-01-15. Discharge Date: 2024-01-19.
+
+Principal Diagnosis: Community-acquired pneumonia, right lower lobe.
+Secondary Diagnoses: COPD, Atrial fibrillation.
+
+Hospital Course:
+Patient admitted with fever 101.8F, productive cough with purulent sputum,
+and oxygen saturation of 88% on room air. Chest X-ray confirmed right lower
+lobe consolidation. Started on Ceftriaxone 1g IV daily and Azithromycin
+500mg PO daily. Supplemental O2 via nasal cannula at 3L/min.
+
+Discharge Medications:
+- Amoxicillin-Clavulanate 875mg PO BID x 5 days
+- Albuterol inhaler 2 puffs q4-6h PRN
+- Warfarin 5mg PO daily
+- Metoprolol 50mg PO BID
+
+Follow-up: Pulmonology clinic in 2 weeks. Repeat chest X-ray in 6 weeks.""",
+        DEFAULT_LABELS,
+        0.4,
+    ],
+    [
+        """ED Note - 03/10/2024 22:45
+Chief Complaint: Laceration to right hand.
+
+HPI: 34-year-old male presents after cutting his right palm on broken glass
+approximately 1 hour ago. Reports moderate bleeding controlled with direct
+pressure. Denies numbness or weakness in fingers. No foreign body sensation.
+Tetanus up to date.
+
+Exam: 3cm linear laceration to right thenar eminence, clean edges, no tendon
+involvement, neurovascular intact distally.
+
+Procedure: Wound irrigated with normal saline. Repaired with 4-0 nylon,
+5 interrupted sutures. Sterile dressing applied.
+
+Disposition: Home with wound care instructions. Suture removal in 10 days.""",
+        DEFAULT_LABELS + ["body_part", "wound"],
+        0.4,
+    ],
+]
+
+with gr.Blocks(title="GLiNER Biomedical NER") as demo:
+    gr.Markdown("# GLiNER Biomedical NER\nZero-shot named entity recognition with `gliner-biomed-base-v1.0`")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(label="Clinical Text", lines=12)
+            labels_input = gr.Dropdown(
+                label="Entity Labels",
+                choices=DEFAULT_LABELS,
+                value=DEFAULT_LABELS,
+                multiselect=True,
+                allow_custom_value=True,
+                max_choices=MAX_LABELS,
+            )
+            threshold = gr.Slider(0.0, 1.0, value=0.4, step=0.05, label="Confidence Threshold")
+            run_btn = gr.Button("Extract", variant="primary")
+
+        with gr.Column(scale=3):
+            latency_output = gr.Textbox(label="Latency")
+            highlight_output = gr.HighlightedText(label="Entities", combine_adjacent=False, show_legend=True)
+            table_output = gr.Dataframe(headers=["Label", "Text", "Score"], label="Extracted Entities")
+
+    run_btn.click(extract, inputs=[text_input, labels_input, threshold], outputs=[highlight_output, table_output, latency_output])
+    gr.Examples(EXAMPLES, inputs=[text_input, labels_input, threshold])
+
+demo.launch()