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在 Raspberry Pi 上執行 Discord 機器人

Susan Sarandon
Susan Sarandon原創
2024-10-01 12:11:02700瀏覽

Unsplash 上 Daniel Tafjord 的封面照片

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:

  • 前往預先定義的簡單 Leetcode 問題資料庫
  • 取得尚未發佈到 Discord 頻道的問題
  • 將 leetcode 問題作為主題發佈到不和諧頻道中(這樣您就可以輕鬆添加您的解決方案)
  • 問題被標記為已發布,以避免再次將其發佈到頻道

Running a Discord Bot on Raspberry Pi

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

Running a Discord Bot on Raspberry Pi

設定

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!

1.使用python虛擬環境

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

Running a Discord Bot on Raspberry Pi

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

2.安裝依賴

需要以下相依性:

  • AWS CLI

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。

  • pip 相依性

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

3.建立leetcode易題庫

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:

  • 使用 leetscrape 從 leetcode 取得所有問題清單並將清單儲存到 csv
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")
  • 建立一個 Amazon DynamoDB 表,並使用從上一個步驟儲存的 csv 中篩選出的簡單問題清單填入該表。
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)

4.設定環境變數

建立.env檔來儲存環境變數

DISCORD_BOT_TOKEN=*****

5.創建Discord應用程式

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:

  • 發送訊息
  • 建立公共線程
  • 在話題中發送訊息

6. 運行機器人!

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...

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