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app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 加载 DialoGPT 模型和 Tokenizer
+model_name = "microsoft/DialoGPT-medium"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# 存储对话历史
+conversation_history = []
+
+
+# 对话生成函数
+def respond_to_input(user_input):
+    global conversation_history
+
+    # 编码用户输入并将其附加到对话历史
+    conversation_history.append(f"User: {user_input}")
+
+    # 将历史对话作为模型输入
+    input_text = " ".join(conversation_history[-5:])  # 只传递最近的5条对话，避免过长
+    input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt")
+
+    # 生成对话的响应
+    response_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+    # 解码模型生成的响应
+    bot_response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+    # 将机器人响应添加到对话历史
+    conversation_history.append(f"Bot: {bot_response}")
+
+    # 返回更新后的对话历史
+    chat_history = "\n".join(conversation_history[-10:])  # 显示最近的10条对话
+    return chat_history, ""  # 更新对话历史并清空输入框
+
+
+# 创建 Gradio 界面
+iface = gr.Interface(
+    fn=respond_to_input,
+    inputs=gr.Textbox(label="", placeholder="Type here...", lines=1, scale=2),
+    outputs=[gr.Textbox(label="Conversation History", lines=15, interactive=False), gr.Textbox()],
+    title="ChatGPT-like Chatbot",
+    description="Chat with a bot powered by DialoGPT. Type your question below!",
+    theme="default",  # 使用默认的主题
+    live=True,
+    allow_flagging="never",  # 禁用标记按钮
+    css=".output-textbox { height: 400px; }"  # 自定义输出框高度
+)
+
+# 启动应用
+iface.launch()