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from dotenv import load_dotenv
import openai
import json
import hashlib
import os
load_dotenv()
def summarization_is_possible() -> bool:
return os.getenv("OPENAI_API_KEY")
def _load_cache():
cache_file = "summarization_cache.json"
if not os.path.exists(cache_file):
with open(cache_file, "w") as file:
json.dump({}, file)
with open(cache_file, "r") as file:
return json.load(file)
def _save_cache(cache: dict):
cache_file = "summarization_cache.json"
with open(cache_file, "w") as file:
json.dump(cache, file)
def _make_cache_key(game: str, img_urls: list[str]) -> str:
normalized_game = game.strip().lower()
img_data = json.dumps(sorted(img_urls), separators=(",", ":"))
hash_digest = hashlib.sha256(img_data.encode()).hexdigest()[:12]
return f"{normalized_game}_{hash_digest}"
def generate_headline_and_content_from_images(img_urls: list[str], game: str):
"""
Uses LLM to generate the headline and content when none provided by source, based on one or more images.
"""
cache = _load_cache()
cache_key = _make_cache_key(game, img_urls)
if cache_key in cache:
cached = cache[cache_key]
return cached["headline"], cached["content"]
tools = [
{
"type": "function",
"function": {
"name": "generate_update_text",
"description": "Generates a concise English headline and short description for a rhythm game update image.",
"parameters": {
"type": "object",
"properties": {
"headline": {
"type": "string",
"description": "A short English headline summarizing the game update.",
},
"content": {
"type": "string",
"description": "A brief English description of the new content shown in the image(s).",
},
},
"required": ["headline", "content"],
},
},
}
]
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": (
f"Given one or more update-related images for the arcade game {game}, return a short, professional English headline and a brief, stern and concise description summarizing the content. No need to repeat game name"
),
},
*[{"type": "image_url", "image_url": {"url": url}} for url in img_urls],
],
}
]
response = openai.chat.completions.create(
model="gpt-4o",
messages=messages,
tools=tools,
tool_choice={
"type": "function",
"function": {"name": "generate_update_text"},
},
)
tool_result = response.choices[0].message.tool_calls[0].function.arguments
parsed_result = json.loads(tool_result)
headline = parsed_result["headline"]
content = parsed_result["content"]
cache[cache_key] = {"headline": headline, "content": content}
_save_cache(cache)
return headline, content
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