Upload folder using huggingface_hub

README.md

@@ -1,13 +1,36 @@
 ---
-title: Cliniguard Diagnosis Icd Ner
-emoji: 📈
+title: CliniGuard Diagnosis ICD NER
+emoji: 🏥
 colorFrom: blue
-colorTo: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.20.0
-python_version: '3.12'
+sdk_version: 6.15.1
 app_file: app.py
-pinned: false
+short_description: PubMedBERT clinical diagnosis entity extraction NER
+python_version: "3.12"
+startup_duration_timeout: 30m
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CliniGuard Diagnosis ICD NER
+
+Interactive demo for [genzeonplatform/cliniguard-diagnosis-icd-ner](https://huggingface.co/genzeonplatform/cliniguard-diagnosis-icd-ner),
+a transformer-based clinical Named Entity Recognition model built on **PubMedBERT** (`BertForTokenClassification`)
+for automated extraction of diagnoses, conditions, and coding-support entities from unstructured clinical text.
+
+The model recognizes **9 diagnosis and coding entity types** (19 BIO labels):
+
+| Entity | Description |
+|--------|-------------|
+| `PRIMARY_DIAGNOSIS` | Principal reason for encounter |
+| `SECONDARY_DIAGNOSIS` | Additional diagnoses |
+| `DIFFERENTIAL_DIAGNOSIS` | Diagnoses under consideration |
+| `COMORBIDITY` | Co-existing conditions |
+| `COMPLICATION` | Hospital-acquired or treatment complications |
+| `CHRONIC_CONDITION` | Long-term conditions |
+| `ACUTE_CONDITION` | Acute episodes or events |
+| `DIAGNOSIS_STATUS` | Current clinical status (active, resolved, improving) |
+| `DIAGNOSIS_DATE` | Date of diagnosis or onset |
+
+Enter clinical text (discharge summaries, progress notes, clinical narratives) and the model will
+highlight and structure the recognized diagnosis entities using the standard HuggingFace
+`token-classification` pipeline with `aggregation_strategy="simple"`.

app.py

@@ -0,0 +1,186 @@
+import spaces  # MUST be first — ZeroGPU patches torch.cuda before transformers imports torch
+import torch
+import gradio as gr
+import json
+from transformers import pipeline as hf_pipeline
+
+MODEL_ID = "genzeonplatform/cliniguard-diagnosis-icd-ner"
+
+# Entity type -> human-readable description (9 diagnosis/coding categories)
+ENTITY_INFO = {
+    "PRIMARY_DIAGNOSIS": "Principal reason for encounter",
+    "SECONDARY_DIAGNOSIS": "Additional diagnoses",
+    "DIFFERENTIAL_DIAGNOSIS": "Diagnoses under consideration",
+    "COMORBIDITY": "Co-existing conditions",
+    "COMPLICATION": "Hospital-acquired or treatment complications",
+    "CHRONIC_CONDITION": "Long-term conditions",
+    "ACUTE_CONDITION": "Acute episodes or events",
+    "DIAGNOSIS_STATUS": "Current clinical status (active, resolved, improving)",
+    "DIAGNOSIS_DATE": "Date of diagnosis or onset",
+}
+
+# Color map for HighlightedText (9 distinct colors)
+COLOR_MAP = {
+    "PRIMARY_DIAGNOSIS": "#ef4444",
+    "SECONDARY_DIAGNOSIS": "#f97316",
+    "DIFFERENTIAL_DIAGNOSIS": "#eab308",
+    "COMORBIDITY": "#22c55e",
+    "COMPLICATION": "#3b82f6",
+    "CHRONIC_CONDITION": "#a855f7",
+    "ACUTE_CONDITION": "#ec4899",
+    "DIAGNOSIS_STATUS": "#14b8a6",
+    "DIAGNOSIS_DATE": "#6b7280",
+}
+
+# Load the token-classification pipeline at module scope.
+# aggregation_strategy="simple" merges B-/I- subword tokens into full entity spans,
+# exactly as documented in the model card.
+nlp = hf_pipeline(
+    "token-classification",
+    model=MODEL_ID,
+    aggregation_strategy="simple",
+)
+# Move the underlying model to CUDA eagerly (ZeroGPU hijack intercepts this).
+nlp.model.to("cuda")
+
+
+@spaces.GPU(duration=60)
+def extract_entities(text: str):
+    """Extract diagnosis entities from unstructured clinical text.
+
+    Recognizes 9 diagnosis/coding entity types: PRIMARY_DIAGNOSIS,
+    SECONDARY_DIAGNOSIS, DIFFERENTIAL_DIAGNOSIS, COMORBIDITY, COMPLICATION,
+    CHRONIC_CONDITION, ACUTE_CONDITION, DIAGNOSIS_STATUS, and DIAGNOSIS_DATE.
+
+    Args:
+        text: Clinical text to analyze (discharge summaries, progress notes, etc.)
+
+    Returns:
+        A tuple of (highlighted_text, structured_json, entity_summary_table).
+    """
+    # Run inference; pipeline handles tokenization + aggregation
+    raw_entities = nlp(text)
+
+    # Build HighlightedText format: {"text": ..., "entities": [{"start","end","entity"}]}
+    entities = [
+        {
+            "start": ent["start"],
+            "end": ent["end"],
+            "entity": ent["entity_group"],
+        }
+        for ent in raw_entities
+    ]
+    highlighted = {"text": text, "entities": entities}
+
+    # Build structured JSON output
+    structured = [
+        {
+            "text": ent["word"],
+            "type": ent["entity_group"],
+            "description": ENTITY_INFO.get(ent["entity_group"], ""),
+            "score": round(float(ent["score"]), 4),
+            "start": ent["start"],
+            "end": ent["end"],
+        }
+        for ent in raw_entities
+    ]
+
+    # Build entity summary table (type, count, examples, description)
+    summary = {}
+    for ent in raw_entities:
+        label = ent["entity_group"]
+        if label not in summary:
+            summary[label] = []
+        summary[label].append(ent["word"])
+
+    table_data = []
+    for label, mentions in sorted(summary.items()):
+        table_data.append([
+            label,
+            len(mentions),
+            ", ".join(mentions[:5]) + ("..." if len(mentions) > 5 else ""),
+            ENTITY_INFO.get(label, ""),
+        ])
+
+    return highlighted, json.dumps(structured, indent=2), table_data
+
+
+# --- Gradio UI ---
+
+CSS = """
+#col-container { max-width: 1100px; margin: 0 auto; }
+.dark .gradio-container { color: var(--body-text-color); }
+"""
+
+EXAMPLES = [
+    ["Discharge Diagnosis: Primary: acute myocardial infarction. "
+     "Secondary: type 2 diabetes mellitus, essential hypertension. "
+     "Status: improving. Complication: acute kidney injury."],
+    ["Patient admitted with community-acquired pneumonia. "
+     "PMH: COPD, coronary artery disease, hyperlipidemia. "
+     "Differential: pulmonary embolism vs pneumonia. Status: suspected. Date: 03/15/2024."],
+    ["Assessment: sepsis is worsening with acute respiratory failure. "
+     "Chronic conditions include type 2 diabetes and chronic kidney disease stage 3. "
+     "Comorbidity: obesity. Complication: hospital-acquired pneumonia."],
+    ["Principal Dx: congestive heart failure with acute respiratory failure. "
+     "Secondary: atrial fibrillation, chronic kidney disease. "
+     "Status: active. Date of onset: 01/20/2024."],
+]
+
+with gr.Blocks(theme=gr.themes.Citrus(), css=CSS) as demo:
+    gr.Markdown(
+        """
+        # 🏥 CliniGuard Diagnosis ICD NER
+
+        Extract diagnosis entities — primary/secondary/differential diagnoses, comorbidities,
+        complications, chronic/acute conditions, status, and dates — from unstructured clinical text
+        using a **PubMedBERT**-based token-classification model
+        ([genzeonplatform/cliniguard-diagnosis-icd-ner](https://huggingface.co/genzeonplatform/cliniguard-diagnosis-icd-ner)).
+        """
+    )
+
+    with gr.Column(elem_id="col-container"):
+        with gr.Row():
+            input_text = gr.Textbox(
+                label="Clinical Text",
+                placeholder="Enter clinical text (discharge summaries, progress notes, clinical narratives)...",
+                lines=8,
+                scale=4,
+            )
+            run_btn = gr.Button("Extract Entities", variant="primary", scale=1)
+
+        highlighted_output = gr.HighlightedText(
+            label="Annotated Clinical Text",
+            combine_adjacent=True,
+            show_legend=True,
+            color_map=COLOR_MAP,
+        )
+
+        with gr.Accordion("Structured Output", open=False):
+            json_output = gr.JSON(label="Extracted Entities (JSON)")
+
+        entity_table = gr.Dataframe(
+            headers=["Entity Type", "Count", "Examples", "Description"],
+            label="Entity Summary",
+            interactive=False,
+            wrap=True,
+        )
+
+        gr.Examples(
+            examples=EXAMPLES,
+            inputs=[input_text],
+            outputs=[highlighted_output, json_output, entity_table],
+            fn=extract_entities,
+            cache_examples=True,
+            cache_mode="lazy",
+        )
+
+    run_btn.click(
+        fn=extract_entities,
+        inputs=[input_text],
+        outputs=[highlighted_output, json_output, entity_table],
+        api_name="extract_entities",
+    )
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch