Update app.py

app.py

@@ -1,70 +1,40 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    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(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    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,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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)",
-        ),
-    ],
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# 加载模型和分词器
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    trust_remote_code=True
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
+# 创建聊天管道
+chat_pipeline = pipeline(
+    "conversational",
+    model=model,
+    tokenizer=tokenizer
+)
 
+# 聊天函数
+def chat_with_model(message, history):
+    # 把历史对话转换成模型需要的格式
+    history_transformer_format = []
+    for user_msg, bot_msg in history:
+        history_transformer_format.append({"role": "user", "content": user_msg})
+        history_transformer_format.append({"role": "assistant", "content": bot_msg})
+    # 添加当前用户消息
+    history_transformer_format.append({"role": "user", "content": message})
+    
+    # 获取模型回复
+    response = chat_pipeline(history_transformer_format)
+    return response[-1]["content"]
+
+# 启动Gradio聊天界面
 if __name__ == "__main__":
-    demo.launch()
+    gr.ChatInterface(
+        fn=chat_with_model,
+        title="TinyLlama 聊天助手",
+        description="基于 TinyLlama-1.1B-Chat 的轻量聊天助手"
+    ).launch()