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import base64
import io
import json
import os
from openai import OpenAI
from together import Together
import anthropic
from dotenv import load_dotenv
from PIL import Image
load_dotenv()
OpenAI_API_KEY = os.getenv("OpenAI_API_KEY")
Anthropic_API_KEY = os.getenv("Anthropic_API_KEY")
Gemini_API_KEY = os.getenv("Gemini_API_KEY")
Together_API_KEY = os.getenv("Together_API_KEY")
PROMPT = "What is the object outlined by the yellow border in this image? Be as specific as you can."
def init_client(args):
if "gpt" in args.model or args.model[0] == 'o':
return OpenAI(api_key=OpenAI_API_KEY)
elif "gemini" in args.model:
return OpenAI(api_key=Gemini_API_KEY, base_url="https://generativelanguage.googleapis.com/v1beta/openai/")
elif "claude" in args.model:
return anthropic.Anthropic(api_key=Anthropic_API_KEY)
else:
return Together(api_key=Together_API_KEY)
def encode_image(image_path, max_bytes=3.5 * 1024 * 1024):
with open(image_path, "rb") as f:
data = f.read()
if len(data) <= max_bytes:
return base64.b64encode(data).decode("utf-8")
img = Image.open(io.BytesIO(data))
scale = 0.9
while True:
buf = io.BytesIO()
img.save(buf, format="PNG", optimize=True)
if buf.tell() <= max_bytes:
break
w, h = img.size
img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
return base64.b64encode(buf.getvalue()).decode("utf-8")
def chat_completion(args, client, image_path):
b64 = encode_image(image_path)
media_type = "image/png"
if "claude" in args.model:
resp = client.messages.create(
model=args.model,
max_tokens=16000,
thinking={"type": "adaptive"},
messages=[{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": media_type, "data": b64}},
{"type": "text", "text": PROMPT}
]
}]
)
return next(b.text for b in resp.content if b.type == "text")
elif "gpt" in args.model or args.model[0] == 'o':
return client.chat.completions.create(
model=args.model,
max_completion_tokens=5000,
reasoning_effort="high",
messages=[{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": f"data:{media_type};base64,{b64}"}},
{"type": "text", "text": PROMPT}
]
}]
).choices[0].message.content
else:
# Gemini (thinking on by default) and Together models
return client.chat.completions.create(
model=args.model,
temperature=0,
messages=[{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": f"data:{media_type};base64,{b64}"}},
{"type": "text", "text": PROMPT}
]
}]
).choices[0].message.content
def run_eval(args, client):
with open(os.path.join(args.data_dir, "annotations.json")) as f:
entries = json.load(f)
model_name = args.model.split("/")[-1]
output_path = os.path.join(args.output_dir, f"{model_name}_transparent_results.json")
if os.path.exists(output_path):
with open(output_path) as f:
results = json.load(f)
done_ids = {r["id"] for r in results}
print(f"Resuming: {len(done_ids)} already done, {len(entries) - len(done_ids)} remaining.")
else:
results = []
done_ids = set()
for entry in entries:
if entry["id"] in done_ids:
continue
base = os.path.splitext(entry["original_file"])[0]
transparent_path = os.path.join(args.data_dir, f"{base}_transparent.png")
if not os.path.exists(transparent_path):
print(f"Missing transparent image for {entry['id']}, skipping.")
continue
print(f"Running {entry['id']}...")
response = chat_completion(args, client, transparent_path)
print(f" {response}")
results.append({
"id": entry["id"],
"ground_truth": entry.get("answer", ""),
"response": response
})
os.makedirs(args.output_dir, exist_ok=True)
with open(output_path, "w") as f:
json.dump(results, f, indent=2)
return results
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="gpt-5.4")
parser.add_argument("--data_dir", type=str, default="data/combined_output")
parser.add_argument("--output_dir", type=str, default="data/eval_results_thinking")
return parser.parse_args()
def main(args):
client = init_client(args)
os.makedirs(args.output_dir, exist_ok=True)
results = run_eval(args, client)
model_name = args.model.split("/")[-1]
print(f"Done. {len(results)} results in {os.path.join(args.output_dir, f'{model_name}_transparent_results.json')}")
if __name__ == "__main__":
args = parse_args()
main(args)
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