backend/gpt_method.py

"""
GPT-based researcher implementation.
Uses the latest Responses API with text_format for structured outputs.
"""
from openai import OpenAI
from backend.pydantic_schema import ImageAdEssentialsOutput
from backend.prompt import get_system_prompt, get_user_prompt
from dotenv import load_dotenv
import os

load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")


def researcher_gpt(target_audience: str, product_category: str, product_description: str):
    """
    GPT-based researcher function using the Responses API.

    Args:
        target_audience: Target audience from the predefined list
        product_category: Product category (e.g., "ring", "bangles")
        product_description: Description of the product

    Returns:
        list[ImageAdEssentials]: List of psychology triggers, angles, and concepts
    """
    # Initialize GPT client
    gpt_client = OpenAI(api_key=OPENAI_API_KEY)

    # Get prompts
    system_prompt = get_system_prompt()
    user_prompt = get_user_prompt(target_audience, product_category, product_description)

    # Use the Responses API with text_format for structured output
    response = gpt_client.responses.parse(
        model="gpt-4o",
        instructions=system_prompt,
        input=[
            {
                "role": "user",
                "content": user_prompt
            }
        ],
        text_format=ImageAdEssentialsOutput,
    )

    # output_parsed returns the Pydantic model directly
    if response.output_parsed:
        return response.output_parsed.output
    else:
        raise ValueError("GPT returned an unparseable response.")