Update app.py

app.py

@@ -1,67 +1,28 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-# Load model directly
-from transformers import AutoModelForCausalLM
-client = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM3-4B", trust_remote_code=True)
-
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
-
-
-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.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
-
-
-"""
-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="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
+import torch
+
+# 使用 MiniCPM-3 模型和分词器
+model_name = "IDEA-CCNL/Wenzhong-GPT2-3.5B"  # 此为 MiniCPM-3 公开版模型的名称，可以替换成你的模型名称
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# 定义生成回复的函数
+def generate_response(input_text):
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    # 为生成模型配置生成参数
+    outputs = model.generate(inputs, max_length=200, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# 使用 Gradio 创建聊天界面
+iface = gr.Interface(
+    fn=generate_response,
+    inputs="text",
+    outputs="text",
+    title="MiniCPM-3 中文聊天机器人",
+    description="这是一个基于 MiniCPM-3 的简单聊天机器人，可以进行中文对话"
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# 启动 Gradio 应用
+iface.launch()