Update app.py

app.py

@@ -1,66 +1,60 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 
+from transformers import AutoModel, AutoTokenizer
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+https://huggingface.co/spaces/jclian91/Chinese_Late_Chunking/blob/main/app.py
+
+
 """
 
 pretrained_model = "ykallan/SkuInfo-Qwen2.5-3B-Instruct"
 
-client = InferenceClient(pretrained_model)
+# load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model, trust_remote_code=True)
+model = AutoModel.from_pretrained(pretrained_model, trust_remote_code=True)
+
 
 def respond(
-    message,
+    sku_name,
     history: list[tuple[str, str]],
     system_message,
     max_tokens,
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    messages = [
+        {"role": "system", "content": "在以下商品名称中抽取出品牌、型号、主商品，并以JSON格式返回。"},
+        {"role": "user", "content": sku_name}
+    ]
+    input_ids = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 
-    messages.append({"role": "user", "content": message})
+    model_inputs = tokenizer([input_ids], return_tensors="pt", padding=True).to(device)
+    generate_config = {
+        "max_new_tokens": 128
+    }
+    generated_ids = model.generate(model_inputs.input_ids, **generate_config)
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return response
 
-    response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
 
-        response += token
-        yield response
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        sku_name = gr.TextArea(lines=1, placeholder="your query", label="skuName")
 
+        submit = gr.Button("Submit")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="在以下商品名称中抽取出品牌、型号、主商品，并以JSON格式返回。", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+        
+        examples = gr.Examples(
 
+            inputs=[sku_name]
+        )
 
-if __name__ == "__main__":
+        submit.click(fn=response,
+                     inputs=[sku_name],
+                     )
     demo.launch()