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'''
This script takes the drug repurposing style questions from the csv file and save the result as another csv file.
Before running this script, make sure to configure the filepaths in config.yaml file.
Command line argument should be either 'gpt-4' or 'gpt-35-turbo'
'''
from kg_rag.utility import *
import sys
CHAT_MODEL_ID = sys.argv[1]
QUESTION_PATH = config_data["DRUG_REPURPOSING_PATH"]
SYSTEM_PROMPT = system_prompts["DRUG_REPURPOSING"]
CONTEXT_VOLUME = int(config_data["CONTEXT_VOLUME"])
QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD = float(config_data["QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD"])
QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY = float(config_data["QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY"])
VECTOR_DB_PATH = config_data["VECTOR_DB_PATH"]
NODE_CONTEXT_PATH = config_data["NODE_CONTEXT_PATH"]
SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL = config_data["SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL"]
SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL = config_data["SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL"]
TEMPERATURE = config_data["LLM_TEMPERATURE"]
SAVE_PATH = config_data["SAVE_RESULTS_PATH"]
CHAT_DEPLOYMENT_ID = CHAT_MODEL_ID
save_name = "_".join(CHAT_MODEL_ID.split("-"))+"_drug_repurposing_questions_response.csv"
vectorstore = load_chroma(VECTOR_DB_PATH, SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL)
embedding_function_for_context_retrieval = load_sentence_transformer(SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL)
node_context_df = pd.read_csv(NODE_CONTEXT_PATH)
def main():
start_time = time.time()
question_df = pd.read_csv(QUESTION_PATH)
answer_list = []
for index, row in question_df.iterrows():
question = row["text"]
context = retrieve_context(question, vectorstore, embedding_function_for_context_retrieval, node_context_df, CONTEXT_VOLUME, QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD, QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY)
enriched_prompt = "Context: " + context + "\n" + "Question: " + question
output = get_GPT_response(enriched_prompt, SYSTEM_PROMPT, CHAT_MODEL_ID, CHAT_DEPLOYMENT_ID, temperature=TEMPERATURE)
answer_list.append((row["disease_in_question"], row["refDisease"], row["compoundGroundTruth"], row["text"], output))
answer_df = pd.DataFrame(answer_list, columns=["disease_in_question", "refDisease", "compoundGroundTruth", "text", "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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