persona_beta.py

import openai
import os

from openai import OpenAI
client = OpenAI(
    api_key= os.environ["gptkey"]
)

def generate_persona(gender, age_range, life_stage, education_level, job_title, annual_income, ad_agreement, brand_consumption_behavior, social_interaction, financial_planning, behavioral_motives, personal_traits, social_media_activity, interests, personality_traits):

    total_prompt_tokens_used = 0
    total_completion_tokens_used = 0

    messages_base = [
        {"role": "system", "content": "請扮演一個具備側寫專長的資深行銷企劃，協助我創建一個虛構的人物誌。"}
    ]

    # Creating a prompt with a structured format for the Persona in Traditional Chinese
    prompt_text = f"創建一個詳細的人物誌檔案，除非有另外說明否則排除行銷背景:\n\n" \
                  f"- 性別: {gender}\n" \
                  f"- 年齡階層: {age_range}\n" \
                  f"- 生命階段: {life_stage}\n" \
                  f"- 教育程度: {education_level}\n" \
                  f"- 工作職稱: {job_title}\n" \
                  f"- 個人年收入: {annual_income}\n" \
                  f"- 對廣告的看法: {ad_agreement}\n" \
                  f"- 品牌與消費行為: {brand_consumption_behavior}\n" \
                  f"- 人際互動: {social_interaction}\n" \
                  f"- 金錢規劃: {financial_planning}\n" \
                  f"- 行為動機與處事準則: {behavioral_motives}\n" \
                  f"- 個人特質: {personal_traits}\n" \
                  f"- 網路社群活動: {social_media_activity}\n" \
                  f"- 興趣愛好: {interests}\n" \
                  f"- 性格特質: {personality_traits}\n\n" \
                  f"請根據這些屬性，提供一個全面的人物誌描述，包括以下欄位：\n\n" \
                  f"[名稱]:\n" \
                  f"[年齡]:\n" \
                  f"[性別]:\n" \
                  f"[生活背景]:(請用長篇幅豐富完整)\n" \
                  f"[自我介紹](第一人稱介紹自己，並附上自傳與人生簡歷):\n" \
                  f"[生活方式]:\n" \
                  f"[人際關係]:\n" \
                  f"[個人目標與動機]:\n" \
                  f"[面臨的挑戰]:\n" \


    messages_base.append({"role": "user", "content": prompt_text})
    messages_base.extend([{"role": "user", "content":  "請盡可能詳盡豐富，使用繁體中文"}])   

    full_text = ""
    total_price = 0


    #for _ in range(loop):
    response = client.chat.completions.create(
       model='gpt-4-turbo',
       max_tokens=4096,
       temperature=0.5,
       messages=messages_base
     )
    completed_text = response.choices[0].message.content
    total_prompt_tokens_used += response.usage.prompt_tokens
    total_completion_tokens_used += response.usage.completion_tokens
    price = total_prompt_tokens_used*0.03/1000 + total_completion_tokens_used*0.06/1000

    full_text += completed_text + "\n\n----------\n\n"
    total_price += price


    price = "price:" + str(total_prompt_tokens_used*0.03/1000 + total_completion_tokens_used*0.06/1000) + "$"

    full_text += "\n\n" + "price:" + str(total_price)


    #return response.choices[0].message.content
    return full_text