Python使用DeepSeek进行联网搜索功能详解

pip install deepseek  # 假设的DeepSeek库安装命令
pip install requests
pip install beautifulsoup4

import requests
from bs4 import BeautifulSoup
 
def fetch_images_from_web(query, max_images=10):
    url = f"https://www.google.com/search?tbm=isch&q={query}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    images = []
    for img_tag in soup.find_all('img')[:max_images]:
        img_url = img_tag['src']
        images.append(requests.get(img_url).content)
    return images
 
# 示例调用
images = fetch_images_from_web("cat", 5)

from tensorflow.keras.datasets import cifar10
import numpy as np
 
# 加载CIFAR-10数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
 
# 假设我们已经有了一个包含额外猫类图像数据的NumPy数组extra_cat_images
# 和一个包含这些图像对应标签的NumPy数组extra_cat_labels（全部为猫类标签）
# 这里我们省略了加载这些额外数据的代码
 
# 将额外猫类图像数据添加到训练集中
x_train = np.vstack((x_train, extra_cat_images))
y_train = np.hstack((y_train, extra_cat_labels))
 
# 数据标准化
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
 
def create_cnn_model(input_shape):
    model = Sequential([
        Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        MaxPooling2D((2, 2)),
        Conv2D(64, (3, 3), activation='relu'),
        MaxPooling2D((2, 2)),
        Flatten(),
        Dense(64, activation='relu'),
        Dense(10, activation='softmax')
    ])
    return model
 
# 创建模型
model = create_cnn_model(x_train.shape[1:])
 
# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
 
# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))

# 模型评估
loss, accuracy = model.evaluate(x_test, y_test)
print(f"Test accuracy: {accuracy}")
 
# 保存模型
model.save("cnn_model.h5")

# 示例数据
sentences = ["Barack Obama was born in Hawaii.", "Apple is a famous company."]
labels = [["PERSON", "O", "O", "O", "LOCATION", "O"], ["ORG", "O", "O", "O", "O"]]

from deepseek.models import BiLSTMCRF  # 假设deepseek库提供了BiLSTMCRF模型
 
# 创建模型
model = BiLSTMCRF()
 
# 训练模型
model.train(sentences, labels)

# 预测
test_sentence = "Elon Musk founded SpaceX."
predicted_labels = model.predict(test_sentence)
print(predicted_labels)

pip install flask

from flask import Flask, request, jsonify
from tensorflow.keras.models import load_model
import numpy as np
from PIL import Image
import base64
from io import BytesIO
 
app = Flask(__name__)
 
# 加载训练好的模型
model = load_model("cnn_model.h5")
 
@app.route('/predict', methods=['POST'])
def predict():
    # 从请求中获取图像数据（假设图像数据以base64编码的形式传递）
    image_data = request.json.get('image_data')
    image = Image.open(BytesIO(base64.b64decode(image_data)))
    image = image.resize((32, 32))  # 假设模型输入尺寸为32x32
    image = np.array(image).astype('float32') / 255.0
    image = np.expand_dims(image, axis=0)
 
    # 使用模型进行预测
    prediction = model.predict(image)
    predicted_class = np.argmax(prediction, axis=1)[0]
 
    # 返回预测结果
    return jsonify({'predicted_class': predicted_class})
 
if __name__ == '__main__':
    app.run(debug=True)

python app.py