PythonとChatGPT APIでGoogleシートの各行にAIの処理を適用させる方法

!pip install openai
import pandas as pd
from google.colab import auth, drive
from google.colab import userdata
import gspread
from google.auth import default
from openai import OpenAI
import os # Import os to manage environment variables

# Googleシートを認証する
drive.mount('/content/drive', force_remount=True)
auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)

# Googleシートのファイルを開ける
filename = "sample-gsc-data"
ss = gc.open(filename)
# Googleシートのシートを読み込む
def load_sheet_data(sheet_name):
    worksheet = ss.worksheet(sheet_name)
    data = worksheet.get_all_values()
    return pd.DataFrame(data[1:], columns=data[0])
sheet = "sample_gsc_data_ja"
df = load_sheet_data(sheet)

# OpenAI APIを設定する
OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')
client = OpenAI(api_key=OPENAI_API_KEY)
gpt_model = "gpt-4o-mini"
def apply_chatgpt(keyword):
    response = client.chat.completions.create(
        model= gpt_model,
        messages=[{"role": "user", "content":
        f"""
        Classify the following keyword as either 'Branded' or ''Generic'
        A branded keyword is a keyword related to the company 'Nidas(ナイダス)',
        including its name in English or Japanese, common synonyms, or product      
        names.
        No explanation needed, just answer by either 'Branded' or 'Generic'
        Keyword: {keyword}.
        """
        }],
        max_tokens=100)
    result = response.choices[0].message.content
    return result.strip()

# 処理を実行する
start_row = 4
worksheet = ss.worksheet(sheet)
for index, row in df.iterrows():
    if index + 2 < start_row: # Skip rows before the start row
        continue
    result = apply_chatgpt(row['Query'])
    df.at[index, 'Category'] = result
    worksheet.update_cell(index + 2, df.columns.get_loc('Category') + 1, result)