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import json
from tqdm import tqdm
import os
from openai import OpenAI
client = OpenAI(api_key="")
samples = []
with open("./data.json", "r") as f:
for line in f:
samples.append(eval(line.strip()))
def evaluate_prediction(client, question, answer, prediction):
prompt = (f"Please judge whether the generated answer is right or wrong. We require that the correct answer "
f"to the prediction gives a clear answer, not just a calculation process or a disassembly of ideas. "
f"The question is {question}. The true answer is \n {answer}. \n The predicted answer is \n {prediction}.\n "
f"If the predicted answer is right, please output True. Otherwise output Flase. "
f"Don't output any other text content. You only can output True or False.")
response = client.chat.completions.create(
model="gpt-4o-2024-05-13",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
}
]
}
],
temperature=0,
max_tokens=256,
top_p=1,
frequency_penalty=0,
presence_penalty=0
)
# print(prompt)
# print(response.choices[0].message.content)
# exit()
return response.choices[0].message.content
def read_txt(path):
with open(path, "r") as f:
return f.read()
save_path = "./save_process"
model = "gpt-3.5-turbo-0125"
# model = 'gpt-4o-2024-05-13'
# model = 'llama-3-8b-instruct'
# model = 'gpt-3.5-turbo-0125-autoagent'
# model = 'gpt-4o-2024-05-13-autoagent'
# model = 'llava-v1.5-13b'
# model = 'llama3-autoagent'
results = []
save_f = open(os.path.join(save_path, model, "results.json"), "w")
save_process = open(os.path.join(save_path, model, "results_process.json"), "w")
for sample in tqdm(samples):
result = []
if len(sample["questions"]) > 0:
# print(sample['id'])
predicts = []
with open(os.path.join(save_path, model, sample['id']+".json"), "r") as f:
for line in f:
predicts.append(eval(line.strip()))
questions = []
for id, question_name in enumerate(tqdm(sample["questions"])):
question = read_txt(os.path.join("./data", sample["id"], question_name + ".txt"))
pre = predicts[id]
try:
if not model.endswith('autoagent'):
ans = evaluate_prediction(client, question, str(sample["answers"][id]), pre['response'])
else:
ans = evaluate_prediction(client, question, str(sample["answers"][id]), pre['summary'])
except Exception as e:
print(e)
ans = "False"
# print(result)
if not model.endswith('autoagent'):
process = [sample["id"], ans, str(sample["answers"][id]), pre['response'][:]]
else:
process = [sample["id"], ans, str(sample["answers"][id]), pre['summary'][:]]
result.append(ans)
json.dump(process, save_process)
save_process.write("\n")
save_process.flush()
json.dump(result, save_f)
save_f.write("\n")
save_f.flush()
results += result
save_f.close()
save_process.close()
results_c = []
for i, result in enumerate(results):
if "true" in result.lower():
results_c.append(True)
else:
results_c.append(False)
idx = 0
score4cha = []
for sample in tqdm(samples):
if len(sample["questions"]) > 0:
score_ = sum(results_c[idx:idx+len(sample["questions"])]) / len(sample["questions"])
idx += len(sample["questions"])
score4cha.append(score_)
print(f"Accuracy for each challenge is {score4cha}")
acc = sum(results_c) / len(results_c)
print(f"Accuracy for all the {len(results_c)} questions is {acc}")
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