Slack上でアプリにメンションを飛ばすとChatGPTのAPIからの返信が返ってくるコード。SlackのBoltフレームワークとOpenAIのAPIを使用。app.pyと同じフォルダ.envというAPIのTOKENやAPI Keyを入れるファイルも必要。importに使っているdotenv-python, slack_bolt, openaiはpipなどでインストールすること。

app.py

import os
import base64
import requests
from io import BytesIO
from PIL import Image, UnidentifiedImageError
from dotenv import load_dotenv
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
from slack_sdk import WebClient
from openai import OpenAI

# 環境変数ロード
load_dotenv()
SLACK_BOT_TOKEN = os.environ["SLACK_BOT_TOKEN"]
SLACK_APP_TOKEN = os.environ["SLACK_APP_TOKEN"]
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]

# クライアント初期化
app = App(token=SLACK_BOT_TOKEN)
client = WebClient(token=SLACK_BOT_TOKEN)
openai_client = OpenAI(api_key=OPENAI_API_KEY)

# BotユーザーID取得
bot_user_id = client.auth_test()["user_id"]

# サポートされているMIMEタイプと拡張子マップ
SUPPORTED_IMAGE_TYPES = ["image/png", "image/jpeg", "image/webp", "image/gif"]
MIMETYPE_TO_EXTENSION = {
    "image/jpeg": "jpeg",
    "image/png": "png",
    "image/webp": "webp",
    "image/gif": "gif"
}
SYS_PROMPT = """
## PERSISTENCE
You are an agent - please keep going until the user's query is completely 
resolved, before ending your turn and yielding back to the user. Only 
terminate your turn when you are sure that the problem is solved.

## TOOL CALLING
If you are not sure about file content or codebase structure pertaining to 
the user's request, use your tools to read files and gather the relevant 
information: do NOT guess or make up an answer.

## PLANNING
You MUST plan extensively before each function call, and reflect 
extensively on the outcomes of the previous function calls. DO NOT do this 
entire process by making function calls only, as this can impair your 
ability to solve the problem and think insightfully.

## SLACK MARKDOWN FORMAT
Format text for Slack messages, use the following rules:
Use *text* for bold, _text_ for italics, ~text~ for strikethrough, `text` for inline code, and triple backticks (```text```) for code blocks; use > text for block quotes; - item or * item for unordered lists, 1. item for ordered lists; represent emojis with :emoji_name:; and always escape &, <, and > respectively to avoid rendering issues.

## Chain of Thought
First, think carefully step by step about what documents are needed to answer the query. 
Then, print out the TITLE and ID of each document. Then, format the IDs into a list.
"""

# 画像リサイズ関数
def resize_image_for_openai(image_bytes):
    try:
        with Image.open(BytesIO(image_bytes)) as img:
            width, height = img.size
            short_side = min(width, height)
            long_side = max(width, height)

            # High-res条件に合わせる
            if short_side > 768 or long_side > 2000:
                if width < height:
                    new_width = 768
                    new_height = int((768 / width) * height)
                else:
                    new_height = 768
                    new_width = int((768 / height) * width)
                img = img.resize((min(new_width, 2000), min(new_height, 2000)), Image.Resampling.LANCZOS)

            buffered = BytesIO()
            img.save(buffered, format="PNG")
            return buffered.getvalue()
    except UnidentifiedImageError:
        return None

# メンションイベント
@app.event("app_mention")
def handle_app_mention(event, say):
    channel = event["channel"]
    thread_ts = event.get("thread_ts") or event.get("ts")

    # システムプロンプト
    system_prompt = "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."

    # メッセージ履歴
    messages = [{"role": "system", "content": [{"type": "input_text", "text": system_prompt}]}]
    history = client.conversations_replies(channel=channel, ts=thread_ts, limit=6)
    for message in history["messages"]:
        if "subtype" in message and message["subtype"] == "bot_message":
            continue  # Slackのシステムボットメッセージは無視
        content = message.get("text", "").replace(f"<@{bot_user_id}>", "").strip()
        if content:
            if message.get("user") == bot_user_id:
                messages.append({
                    "role": "assistant",
                    "content": [{"type": "output_text", "text": content}]
                })
            else:
                messages.append({
                    "role": "user",
                    "content": [{"type": "input_text", "text": content}]
                })
            
    # ファイルチェック
    files = event.get("files", [])
    content_blocks = []

    user_text = event.get("text", "").replace(f"<@{bot_user_id}>", "").strip()
    if user_text:
        content_blocks.append({"type": "input_text", "text": user_text})

    if files:
        for file in files:
            mimetype = file.get("mimetype", "")
            if mimetype not in SUPPORTED_IMAGE_TYPES:
                say(text="申し訳ありません、対応している画像形式はPNG, JPEG, WEBP, 非アニメGIFのみです。", thread_ts=thread_ts, channel=channel)
                return

            image_url = file["url_private_download"]
            headers = {"Authorization": f"Bearer {SLACK_BOT_TOKEN}"}
            response = requests.get(image_url, headers=headers)
            if response.status_code == 200:
                image_bytes = response.content
                resized_image = resize_image_for_openai(image_bytes)
                if resized_image is None:
                    say(text="画像の処理中にエラーが発生しました。", thread_ts=thread_ts, channel=channel)
                    return
                image_base64 = base64.b64encode(resized_image).decode("utf-8")

                # MIMEタイプから正しい拡張子を取得
                extension = MIMETYPE_TO_EXTENSION.get(mimetype, "png")  # fallbackにpng（ありえないが安全策）
                content_blocks.append({
                    "type": "input_image",
                    "image_url": f"data:image/{extension};base64,{image_base64}"
                })

    if content_blocks:
        messages.append({"role": "user", "content": content_blocks})

    # OpenAI Responses API 呼び出し
    response = openai_client.responses.create(
        instructions=SYS_PROMPT,
        model="gpt-4.1",
        input=messages,
        tools=[
            {
                "type": "web_search_preview",
                "user_location": {
                    "type": "approximate",
                    "country": "JP"
                },
                "search_context_size": "medium"
            }
        ]
    )

    output_text = response.output[-1].content[0].text
    say(text=output_text, thread_ts=thread_ts, channel=channel)

# 翻訳リアクション対応
@app.event("reaction_added")
def handle_reaction(event, say):
    reaction = event["reaction"]
    channel = event["item"]["channel"]
    target_ts = event["item"]["ts"]  # リアクション付いたメッセージのtimestamp

    if reaction not in ["to_english", "to_japanese"]:
        return

    system_prompt = {
        "to_english": "You are a translator. Translate the following text accurately into English. Output only the translated text.",
        "to_japanese": "あなたは翻訳家です。以下のテキストを正確に日本語に翻訳してください。翻訳結果のみ出力してください。"
    }[reaction]

    response = client.conversations_replies(channel=channel, ts=target_ts, inclusive=True, limit=1)
    if not response["messages"]:
        say(text="対象のメッセージが見つかりませんでした。", channel=channel)
        return

    target_message = response["messages"][0]
    thread_ts = target_message.get("thread_ts", target_ts)
    original_text = target_message.get("text", "").replace(f"<@{bot_user_id}>", "").strip()

    if not original_text:
        say(text="翻訳対象のテキストが見つかりませんでした。", thread_ts=thread_ts, channel=channel)
        return

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": [{"type": "input_text", "text": original_text}]}
    ]
    translation_response = openai_client.responses.create(
        model="gpt-4.1",
        temperature=0.2,
        input=messages
    )
    output_text = translation_response.output[-1].content[0].text
    say(text=output_text, thread_ts=thread_ts, channel=channel)


# アプリ起動
if __name__ == "__main__":
    handler = SocketModeHandler(app, SLACK_APP_TOKEN)
    handler.start()