重构代码以实现命名实体识别功能，替换问候函数，添加 Gradio 接口支持

app.py

@@ -1,14 +1,43 @@
 import gradio as gr
 import spaces
 import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
+
+MODEL_ID = "lujin/search-ner-lora-model"
+DEVICE = 0 if torch.cuda.is_available() else -1
+
+# Load model and tokenizer once so the interface stays responsive.
+model = AutoModelForTokenClassification.from_pretrained(MODEL_ID)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+ner_pipe = pipeline(
+    "token-classification",
+    model=model,
+    tokenizer=tokenizer,
+    aggregation_strategy="simple",
+    device=DEVICE,
+)
 
-zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
 
 @spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
+def run_ner(text: str):
+    """Return aggregated entity predictions for the provided text."""
+    text = (text or "").strip()
+    if not text:
+        return []
+    return ner_pipe(text)
+
+
+demo = gr.Interface(
+    fn=run_ner,
+    inputs=gr.Textbox(lines=4, label="Input Text"),
+    outputs=gr.JSON(label="Entities"),
+    title="Search NER",
+    description="Named-entity recognition using lujin/search-ner-lora-model.",
+    examples=[
+        ["OpenAI总部位于旧金山。"],
+        ["小明毕业于清华大学，现在在阿里巴巴工作。"],
+    ],
+)
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-demo.launch()
+demo.launch()