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import os
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import openai
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from openai import OpenAI
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import base64
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from tqdm import tqdm
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import time
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import json
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from pathlib import Path
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from threading import Lock
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from typing import Any
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_json_write_lock = Lock()
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def save_json_file(
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data: Any,
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file_path: str,
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indent: int = 4,
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temp_suffix: str = ".tmp"
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) -> None:
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"""
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"""
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path = Path(file_path)
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path.parent.mkdir(parents=True, exist_ok=True)
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temp_path = f"{file_path}{temp_suffix}"
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with _json_write_lock:
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try:
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with open(temp_path, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=indent)
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f.flush()
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os.fsync(f.fileno())
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os.replace(temp_path, file_path)
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except Exception as e:
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try:
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if os.path.exists(temp_path):
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os.remove(temp_path)
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except OSError:
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pass
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raise RuntimeError(f"save json failed: {e}") from e
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def read_json_file(file_path):
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"""
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Reads a JSON file and returns the parsed data as a Python object.
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:param file_path: The path to the JSON file
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:return: The data parsed from the JSON file
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"""
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with open(file_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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return data
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def encode_image(image_path):
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with open(image_path, "rb") as image_file:
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return base64.standard_b64encode(image_file.read()).decode("utf-8")
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def merge_json_lists(folder_path):
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"""
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"""
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json_list = [
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os.path.join(folder_path, f)
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for f in os.listdir(folder_path)
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if f.lower().endswith('.json') and os.path.isfile(os.path.join(folder_path, f))
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]
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merged_list = []
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for file_path in json_list:
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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if isinstance(data, list):
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merged_list.extend(data)
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else:
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print(f"waring: {file_path} is not list. skipped")
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except Exception as e:
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print(f"processing {file_path} error: {str(e)}")
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return merged_list
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def openai_api(image_path, prompt = None):
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if prompt == None:
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prompt = "What's in this image?"
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base64_image = encode_image(image_path)
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client = OpenAI(
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base_url='your_url',
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api_key='your_key'
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)
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response = client.chat.completions.create(
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model="claude-3-7-sonnet-20250219",
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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},
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],
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}
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],
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max_tokens=5000,
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)
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return response
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if __name__ == "__main__":
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folder_path = r"your_path/poster/data"
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save_dir = r"your_path/poster/result"
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saved_josn = os.path.join(save_dir,"result.json")
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if not os.path.exists(saved_josn):
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tasks = merge_json_lists(folder_path)
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save_json_file(tasks, saved_josn)
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tasks = read_json_file(saved_josn)
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max_retries = 4
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retry_wait = 10
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for item in tqdm(tasks):
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if "response" in item: continue
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prompt = item["prompt"]
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image_path = os.path.join(folder_path, item["path"])
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for attempt in range(max_retries):
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try:
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response = openai_api(image_path, prompt)
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item["response"] = response.choices[0].message.content
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print(item["response"])
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save_json_file(tasks, saved_josn)
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break
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except Exception as e:
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print(f"[Warning] Request failed: {e}")
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if attempt < max_retries - 1:
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print(f"Retrying in {retry_wait} seconds... (attempt {attempt + 1})")
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time.sleep(retry_wait)
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else:
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print("[Error] Reached max retries. Skipping this item.")
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item["error"] = str(e)
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