锦中人工智能助手

import deepseek

# 初始化DeepSeek客户端

client = deepseek.DeepSeekClient(api_key='your_api_key')

# 定义提示词

prompt = "请帮我写一段关于校园智能体系统的介绍。"

# 调用模型生成文本

response = client.generate(prompt)

# 输出结果

print(response.text)

import pandas as pd

# 读取原始数据

data = pd.read_csv('student_data.csv')

# 清洗数据

data.dropna(inplace=True) # 删除缺失值

data['age'] = data['age'].astype(int) # 转换年龄为整数

# 标准化课程名称

data['course_name'] = data['course_name'].str.strip().str.lower()

# 保存处理后的数据

data.to_csv('processed_student_data.csv', index=False)

from deepseek import DeepSeekModel

import torch

from torch.utils.data import Dataset, DataLoader

class StudentDataset(Dataset):

def __init__(self, data_path):

self.data = pd.read_csv(data_path)

def __len__(self):

return len(self.data)

def __getitem__(self, idx):

text = self.data.iloc[idx]['text']

label = self.data.iloc[idx]['label']

return text, label

# 加载数据

dataset = StudentDataset('processed_student_data.csv')

dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# 初始化模型

model = DeepSeekModel.from_pretrained('deepseek-1')

# 训练模型

optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)

for epoch in range(10): # 训练10个周期

for texts, labels in dataloader:

outputs = model(texts)

loss = model.loss(outputs, labels)

loss.backward()

optimizer.step()

optimizer.zero_grad()

from flask import Flask, request, jsonify

import deepseek

import torch

app = Flask(__name__)

model = deepseek.DeepSeekModel.from_pretrained('deepseek-1')

@app.route('/predict', methods=['POST'])

def predict():

data = request.json

text = data.get('text')

with torch.no_grad():

output = model.predict(text)

return jsonify({'prediction': output})

if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)