ponix-generator / llm_wrapper.py
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# import logging
# from PIL import Image
# from io import BytesIO
# import requests, os, json, time
# from google import genai
# prompt_base_path = "src/llm_wrapper/prompt"
# client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
# def encode_image(image_source):
# """
# ์ด๋ฏธ์ง€ ๊ฒฝ๋กœ๊ฐ€ URL์ด๋“  ๋กœ์ปฌ ํŒŒ์ผ์ด๋“  Pillow Image ๊ฐ์ฒด์ด๋“  ๋™์ผํ•˜๊ฒŒ ์ฒ˜๋ฆฌํ•˜๋Š” ํ•จ์ˆ˜.
# ์ด๋ฏธ์ง€๋ฅผ ์—ด์–ด google.genai.types.Part ๊ฐ์ฒด๋กœ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค.
# Pillow์—์„œ ์ง€์›๋˜์ง€ ์•Š๋Š” ํฌ๋งท์— ๋Œ€ํ•ด์„œ๋Š” ์˜ˆ์™ธ๋ฅผ ๋ฐœ์ƒ์‹œํ‚ต๋‹ˆ๋‹ค.
# """
# try:
# # ์ด๋ฏธ Pillow ์ด๋ฏธ์ง€ ๊ฐ์ฒด์ธ ๊ฒฝ์šฐ ๊ทธ๋Œ€๋กœ ์‚ฌ์šฉ
# if isinstance(image_source, Image.Image):
# image = image_source
# else:
# # URL์—์„œ ์ด๋ฏธ์ง€ ๋‹ค์šด๋กœ๋“œ
# if isinstance(image_source, str) and (
# image_source.startswith("http://")
# or image_source.startswith("https://")
# ):
# response = requests.get(image_source)
# image = Image.open(BytesIO(response.content))
# # ๋กœ์ปฌ ํŒŒ์ผ์—์„œ ์ด๋ฏธ์ง€ ์—ด๊ธฐ
# else:
# image = Image.open(image_source)
# # ์ด๋ฏธ์ง€ ํฌ๋งท์ด None์ธ ๊ฒฝ์šฐ (๋ฉ”๋ชจ๋ฆฌ์—์„œ ์ƒ์„ฑ๋œ ์ด๋ฏธ์ง€ ๋“ฑ)
# if image.format is None:
# image_format = "JPEG"
# else:
# image_format = image.format
# # ์ด๋ฏธ์ง€ ํฌ๋งท์ด ์ง€์›๋˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ ์˜ˆ์™ธ ๋ฐœ์ƒ
# if image_format not in Image.registered_extensions().values():
# raise ValueError(f"Unsupported image format: {image_format}.")
# buffered = BytesIO()
# # PIL์—์„œ ์ง€์›๋˜์ง€ ์•Š๋Š” ํฌ๋งท์ด๋‚˜ ๋‹ค์–‘ํ•œ ์ฑ„๋„์„ RGB๋กœ ๋ณ€ํ™˜ ํ›„ ์ €์žฅ
# if image.mode in ("RGBA", "P", "CMYK"): # RGBA, ํŒ”๋ ˆํŠธ, CMYK ๋“ฑ์€ RGB๋กœ ๋ณ€ํ™˜
# image = image.convert("RGB")
# image.save(buffered, format="JPEG")
# return genai.types.Part.from_bytes(data=buffered.getvalue(), mime_type="image/jpeg")
# except requests.exceptions.RequestException as e:
# raise ValueError(f"Failed to download the image from URL: {e}")
# except IOError as e:
# raise ValueError(f"Failed to process the image file: {e}")
# except ValueError as e:
# raise ValueError(e)
# def run_gemini(
# target_prompt: str,
# prompt_in_path: str,
# img_in_data: str = None,
# model: str = "gemini-2.0-flash",
# ) -> str:
# """
# GEMINI API๋ฅผ ๋™๊ธฐ ๋ฐฉ์‹์œผ๋กœ ํ˜ธ์ถœํ•˜์—ฌ ๋ฌธ์ž์—ด ์‘๋‹ต์„ ๋ฐ›์Šต๋‹ˆ๋‹ค.
# retry ๋…ผ๋ฆฌ๋Š” ์ œ๊ฑฐ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.
# """
# with open(os.path.join(prompt_base_path, prompt_in_path), "r", encoding="utf-8") as file:
# prompt_dict = json.load(file)
# system_prompt = prompt_dict["system_prompt"]
# user_prompt_head = prompt_dict["user_prompt"]["head"]
# user_prompt_tail = prompt_dict["user_prompt"]["tail"]
# user_prompt_text = "\n".join([user_prompt_head, target_prompt, user_prompt_tail])
# input_content = [user_prompt_text]
# if img_in_data is not None:
# encoded_image = encode_image(img_in_data)
# input_content.append(encoded_image)
# logging.info("Requested API for chat completion response (sync call)...")
# start_time = time.time()
# # ๋™๊ธฐ ๋ฐฉ์‹: client.models.generate_content(...)
# chat_completion = client.models.generate_content(
# model=model,
# contents=input_content,
# )
# chat_output = chat_completion.parsed
# input_token = chat_completion.usage_metadata.prompt_token_count
# output_token = chat_completion.usage_metadata.candidates_token_count
# pricing = input_token / 1000000 * 0.1 * 1500 + output_token / 1000000 * 0.7 * 1500
# logging.info(
# f"[GEMINI] Request completed (sync). Time taken: {time.time()-start_time:.2f}s / Pricing(KRW): {pricing:.2f}"
# )
# return chat_output, chat_completion