File size: 1,925 Bytes
fec1b45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | import json
import openai
from openai import OpenAI
import os
from tqdm import tqdm
import argparse
def parse_arguments():
parser = argparse.ArgumentParser(description='Cell Type Annotation Evaluation')
parser.add_argument('--model_name', default = "gpt-4.1-mini", help='Name of the GPT model to use')
parser.add_argument('--output_path', default= "./results/response.json", help='Path to save full output results')
parser.add_argument('--dataset_path', default= "./data/cta_scrna_full.json", help='Path to test dataset JSON file')
parser.add_argument('--openai_api_key', default= "", help='OpenAI API key')
parser.add_argument('--base_url', default= "", help='Base URL for OpenAI API')
return parser.parse_args()
args = parse_arguments()
model_name = args.model_name
output_path = args.output_path
dataset_path = args.dataset_path
os.environ["OPENAI_API_KEY"] = args.openai_api_key
os.environ["BASE_URL"] = args.base_url
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"), base_url=os.getenv("BASE_URL"))
def predict_cell_type(messages):
try:
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.0
)
return response.choices[0].message.content
except Exception as e:
print(f"API Error: {e}")
return ""
with open(dataset_path, 'r') as f:
dataset = json.load(f)
output_data = []
for item in tqdm(dataset, desc="Processing Answers", unit="sample"):
messages = item['messages'][:-1]
ground_truth = item['messages'][-1]['content']
prediction = predict_cell_type(messages)
output_item = {
"messages": item['messages'],
"model_response": prediction,
"ground_truth": ground_truth
}
output_data.append(output_item)
with open(output_path, 'w') as f:
json.dump(output_data, f, indent=2)
print("Done")
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