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我最近完成了一個軟體工程訓練營，開始研究 LeetCode 的簡單問題，並覺得如果我每天都有解決問題的提醒，這將有助於讓我負責。我決定使用按 24 小時計劃運行的不和諧機器人（當然是在我值得信賴的樹莓派上）來實現此操作，該機器人將執行以下操作：
我意識到每天去 LeetCode 解決一個問題可能會更容易，但在 ChatGPT 的這個迷你專案的幫助下，我學到了很多關於 Python 和 Discord 的知識。這也是我第一次嘗試寫草圖，請多包涵哈哈
1.使用python虛擬環境
2.安裝依賴
3. 建立Leetcode易題資料庫
4.設定環境變數
5. 建立 Discord 應用程式
6. 運行機器人！
我建議使用Python虛擬環境，因為當我最初在Ubuntu 24.04上測試它時，遇到了以下錯誤
設定相對簡單，只要執行以下指令，瞧，你就進入了 python 虛擬環境！
python3 -m venv ~/py_envs ls ~/py_envs # to confirm the environment was created source ~/py_envs/bin/activate
需要以下相依性：
透過執行以下命令安裝 AWS CLI：
curl -O 'https://awscli.amazonaws.com/awscli-exe-linux-aarch64.zip' unzip awscli-exe-linux-aarch64.zip sudo ./aws/install aws --version
然後執行aws configure 以新增所需的憑證。請參閱配置 AWS CLI 文件。
可以透過執行 pip install -rrequirements.txt 來使用需求檔案安裝下列 pip 相依性。
# requirements.txt discord.py # must install this version of numpy to prevent conflict with # pandas, both of which are required by leetscrape numpy==1.26.4 leetscrape python-dotenv
Leetscrape 對於這一步驟至關重要。要了解更多信息，請參閱 Leetscrape 文件。
我只想解決 leetcode 簡單的問題（對我來說，它們甚至相當困難），所以我做了以下操作：
from leetscrape import GetQuestionsList ls = GetQuestionsList() ls.scrape() # Scrape the list of questions ls.questions.head() # Get the list of questions ls.to_csv(directory="path/to/csv/file")
import csv
import boto3
from botocore.exceptions import BotoCoreError, ClientError
# Initialize the DynamoDB client
dynamodb = boto3.resource('dynamodb')
def filter_and_format_csv_for_dynamodb(input_csv):
result = []
with open(input_csv, mode='r') as file:
csv_reader = csv.DictReader(file)
for row in csv_reader:
# Filter based on difficulty and paidOnly fields
if row['difficulty'] == 'Easy' and row['paidOnly'] == 'False':
item = {
'QID': {'N': str(row['QID'])},
'titleSlug': {'S': row['titleSlug']},
'topicTags': {'S': row['topicTags']},
'categorySlug': {'S': row['categorySlug']},
'posted': {'BOOL': False}
}
result.append(item)
return result
def upload_to_dynamodb(items, table_name):
table = dynamodb.Table(table_name)
try:
with table.batch_writer() as batch:
for item in items:
batch.put_item(Item={
'QID': int(item['QID']['N']),
'titleSlug': item['titleSlug']['S'],
'topicTags': item['topicTags']['S'],
'categorySlug': item['categorySlug']['S'],
'posted': item['posted']['BOOL']
})
print(f"Data uploaded successfully to {table_name}")
except (BotoCoreError, ClientError) as error:
print(f"Error uploading data to DynamoDB: {error}")
def create_table():
try:
table = dynamodb.create_table(
TableName='leetcode-easy-qs',
KeySchema=[
{
'AttributeName': 'QID',
'KeyType': 'HASH' # Partition key
}
],
AttributeDefinitions=[
{
'AttributeName': 'QID',
'AttributeType': 'N' # Number type
}
],
ProvisionedThroughput={
'ReadCapacityUnits': 5,
'WriteCapacityUnits': 5
}
)
# Wait until the table exists
table.meta.client.get_waiter('table_exists').wait(TableName='leetcode-easy-qs')
print(f"Table {table.table_name} created successfully!")
except Exception as e:
print(f"Error creating table: {e}")
# Call function to create the table
create_table()
# Example usage
input_csv = 'getql.pyquestions.csv' # Your input CSV file
table_name = 'leetcode-easy-qs' # DynamoDB table name
# Step 1: Filter and format the CSV data
questions = filter_and_format_csv_for_dynamodb(input_csv)
# Step 2: Upload data to DynamoDB
upload_to_dynamodb(questions, table_name)
建立.env檔來儲存環境變數
DISCORD_BOT_TOKEN=*****
請依照 Discord 開發人員文件中的說明建立具有足夠權限的 Discord 應用程式和機器人。請確保至少為機器人授權以下 OAuth 權限：
以下是可以使用 python3 Discord-leetcode-qs.py 指令運行的機器人程式碼。
import os
import discord
import boto3
from leetscrape import GetQuestion
from discord.ext import tasks
from dotenv import load_dotenv
import re
load_dotenv()
# Discord bot token
TOKEN = os.getenv('DISCORD_TOKEN')
# Set the intents for the bot
intents = discord.Intents.default()
intents.message_content = True # Ensure the bot can read messages
# Initialize the bot
bot = discord.Client(intents=intents)
# DynamoDB setup
dynamodb = boto3.client('dynamodb')
TABLE_NAME = 'leetcode-easy-qs'
CHANNEL_ID = 1211111111111111111 # Replace with the actual channel ID
# Function to get the first unposted item from DynamoDB
def get_unposted_item():
response = dynamodb.scan(
TableName=TABLE_NAME,
FilterExpression='posted = :val',
ExpressionAttributeValues={':val': {'BOOL': False}},
)
items = response.get('Items', [])
if items:
return items[0]
return None
# Function to mark the item as posted in DynamoDB
def mark_as_posted(qid):
dynamodb.update_item(
TableName=TABLE_NAME,
Key={'QID': {'N': str(qid)}},
UpdateExpression='SET posted = :val',
ExpressionAttributeValues={':val': {'BOOL': True}}
)
MAX_MESSAGE_LENGTH = 2000
AUTO_ARCHIVE_DURATION = 2880
# Function to split a question into words by spaces or newlines
def split_question(question, max_length):
parts = []
while len(question) > max_length:
split_at = question.rfind(' ', 0, max_length)
if split_at == -1:
split_at = question.rfind('\n', 0, max_length)
if split_at == -1:
split_at = max_length
parts.append(question[:split_at].strip())
# Continue with the remaining text
question = question[split_at:].strip()
if question:
parts.append(question)
return parts
def clean_question(question):
first_line, _, remaining_question = message.partition('\n')
return re.sub(r'\n{3,}', '\n', remaining_question)
def extract_first_line(question):
lines = question.splitlines()
return lines[0] if lines else ""
# Task that runs on a schedule
@tasks.loop(minutes=1440)
async def scheduled_task():
channel = bot.get_channel(CHANNEL_ID)
item = get_unposted_item()
if item:
title_slug = item['titleSlug']['S']
qid = item['QID']['N']
question = "%s" % (GetQuestion(titleSlug=title_slug).scrape())
first_line = extract_first_line(question)
cleaned_question = clean_message(question)
parts = split_message(cleaned_question, MAX_MESSAGE_LENGTH)
thread = await channel.create_thread(
name=first_line,
type=discord.ChannelType.public_thread
)
for part in parts:
await thread.send(part)
mark_as_posted(qid)
else:
print("No unposted items found.")
@bot.event
async def on_ready():
print(f'{bot.user} has connected to Discord!')
scheduled_task.start()
@bot.event
async def on_thread_create(thread):
await thread.send("\nYour challenge starts here! Good Luck!")
# Run the bot
bot.run(TOKEN)
運行機器人有多種選項。現在，我只是在 tmux shell 中運行它，但您也可以在 Docker 容器中或在來自 AWS、Azure、DigitalOcean 或其他雲端提供者的 VPC 上運行它。
現在我只需要嘗試解決 Leetcode 問題...
以上是在 Raspberry Pi 上執行 Discord 機器人的詳細內容。更多資訊請關注PHP中文網其他相關文章！
