$ python
>>> import openai

# 设置API密钥
openai.api_key = “your-api-key”

# 生成文本
response = openai.ChatCompletion.create(
model=”gpt-4″,
messages=[
{“role”: “system”, “content”: “你是一个专业的AI技术顾问”},
{“role”: “user”, “content”: “请分析当前AI技术的主要趋势”}
],
temperature=0.7
)

print(response[“choices”][0][“message”][“content”])

$ python
>>> from diffusers import StableDiffusionPipeline
import torch

# 加载模型
pipe = StableDiffusionPipeline.from_pretrained(“stabilityai/stable-diffusion-2”, torch_dtype=torch.float16)
pipe = pipe.to(“cuda”)

# 生成图像
prompt = “未来城市的AI技术中心，高科技，未来感，详细”
image = pipe(prompt).images[0]

# 保存图像
image.save(“future_ai_center.png”)
print(“图像生成完成并保存为 future_ai_center.png”)

$ python
>>> from qiskit import QuantumCircuit, Aer
from qiskit.utils import algorithm_globals
from qiskit_machine_learning.algorithms import QSVC
from qiskit_machine_learning.kernels import QuantumKernel
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# 设置随机种子
algorithm_globals.random_seed = 42

# 创建数据集
X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建量子核
feature_map = QuantumCircuit(2, name=”feature_map”)
feature_map.h([0, 1])
feature_map.cx(0, 1)

quantum_kernel = QuantumKernel(feature_map=feature_map, quantum_instance=Aer.get_backend(‘statevector_simulator’))

# 创建量子支持向量机
qsvc = QSVC(quantum_kernel=quantum_kernel)

# 训练模型
qsvc.fit(X_train, y_train)

# 评估模型
accuracy = qsvc.score(X_test, y_test)
print(f”Quantum SVC accuracy: {accuracy:.4f}”)

$ python
>>> import paho.mqtt.client as mqtt
import tensorflow as tf
import numpy as np

# 加载预训练模型
model = tf.keras.models.load_model(‘anomaly_detection_model.h5’)

# MQTT回调函数
def on_message(client, userdata, msg):
# 解析传感器数据
data = np.frombuffer(msg.payload, dtype=np.float32).reshape(1, -1)

# 使用AI模型检测异常
prediction = model.predict(data)

if prediction > 0.5:
print(f”异常检测: {prediction[0][0]:.4f}”)
# 触发警报
client.publish(“alerts”, “异常检测到！”)
else:
print(f”正常: {prediction[0][0]:.4f}”)

# 设置MQTT客户端
client = mqtt.Client()
client.on_message = on_message

# 连接到MQTT broker
client.connect(“fgedudb”, 1883, 60)

# 订阅传感器主题
client.subscribe(“sensors/data”)

# 开始循环
client.loop_forever()