Update app.py

app.py

@@ -1,43 +1,38 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, 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
 """
-client = InferenceClient("ykallan/SkuInfo-Qwen2.5-3B-Instruct")
+
+pretrained_model = "ykallan/SkuInfo-Qwen2.5-3B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(pretrained_model)
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
 
 
 def respond(
     message,
-    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。"}]
 
     messages.append({"role": "user", "content": message})
-
-    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
+    input_ids = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer([input_ids], return_tensors="pt", padding=True)
+    
+    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
 
 
 """