Initial commit

1 files changed, 31 insertions, 0 deletions

diff --git a/kg_rag/prompt_based_generation/GPT/run_mcq_qa.py b/kg_rag/prompt_based_generation/GPT/run_mcq_qa.py
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index 0000000..762242e
--- /dev/null
+++ b/kg_rag/prompt_based_generation/GPT/run_mcq_qa.py
@@ -0,0 +1,31 @@
+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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