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from kg_rag.utility import *
import sys
from tqdm import tqdm
CHAT_MODEL_ID = sys.argv[1]
QUESTION_PATH = config_data["MCQ_PATH"]
SYSTEM_PROMPT = system_prompts["MCQ_QUESTION_PROMPT_BASED"]
SAVE_PATH = config_data["SAVE_RESULTS_PATH"]
TEMPERATURE = config_data["LLM_TEMPERATURE"]
CHAT_DEPLOYMENT_ID = CHAT_MODEL_ID
save_name = "_".join(CHAT_MODEL_ID.split("-"))+"_prompt_based_response_for_two_hop_mcq_from_monarch_and_robokop.csv"
def main():
start_time = time.time()
question_df = pd.read_csv(QUESTION_PATH)
answer_list = []
for index, row in tqdm(question_df.head(50).iterrows(), total=50):
question = "Question: "+ row["text"]
output = get_GPT_response(question, SYSTEM_PROMPT, CHAT_MODEL_ID, CHAT_DEPLOYMENT_ID, temperature=TEMPERATURE)
answer_list.append((row["text"], row["correct_node"], output))
answer_df = pd.DataFrame(answer_list, columns=["question", "correct_answer", "llm_answer"])
answer_df.to_csv(os.path.join(SAVE_PATH, save_name), index=False, header=True)
print("Completed in {} min".format((time.time()-start_time)/60))
if __name__ == "__main__":
main()
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