Update app.py

app.py

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+# app_offline_ner_min.py
+import os
+os.environ["TRANSFORMERS_OFFLINE"] = "1"   # force offline per HF docs
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import torch
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+import gradio as gr
+
+# point to your local snapshot downloaded by prepare_model.py
+# path: ./models/biomedical-ner-all
+HERE = os.path.dirname(os.path.abspath(__file__))
+LOCAL_MODEL_DIR = os.path.join(HERE, "models", "biomedical-ner-all")
+
+# load strictly from disk
+tokenizer = AutoTokenizer.from_pretrained(LOCAL_MODEL_DIR, local_files_only=True)
+model = AutoModelForTokenClassification.from_pretrained(LOCAL_MODEL_DIR, local_files_only=True)
+
+device = 0 if torch.cuda.is_available() else -1
+ner_pipe = pipeline(
+    task="token-classification",                # NER
+    model=model,
+    tokenizer=tokenizer,
+    aggregation_strategy="simple",              # merge subword tokens into entities
+    device=device
+)
+
+def run_ner(text: str):
+    if not text.strip():
+        return {"text": "", "entities": []}, []
+    out = ner_pipe(text)
+    # Gradio HighlightedText format
+    highlighted = {
+        "text": text,
+        "entities": [
+            {"entity": r["entity_group"], "start": int(r["start"]), "end": int(r["end"]), "score": float(r["score"])}
+            for r in out
+        ],
+    }
+    # also return raw rows for inspection
+    rows = [
+        {"entity": r["entity_group"], "word": r["word"], "score": float(r["score"]), "start": int(r["start"]), "end": int(r["end"])}
+        for r in out
+    ]
+    return highlighted, rows
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🩺 Biomedical NER (offline, local model)")
+    inp = gr.Textbox(label="Enter text", value="Patient has a history of asthma treated with albuterol.")
+    ner_view = gr.HighlightedText(label="Entities", combine_adjacent=True)
+    table = gr.Dataframe(label="Raw predictions", interactive=False)
+    inp.change(run_ner, inp, [ner_view, table])
+
+demo.launch(debug=True)