'''
This script takes the True/False 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["TRUE_FALSE_PATH"]
SYSTEM_PROMPT = system_prompts["TRUE_FALSE_QUESTION"]
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"]
CONTEXT_VOLUME = 100


CHAT_DEPLOYMENT_ID = CHAT_MODEL_ID

save_name = "_".join(CHAT_MODEL_ID.split("-"))+"_kg_rag_based_true_false_binary_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)
edge_evidence = False

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(row["text"], vectorstore, embedding_function_for_context_retrieval, node_context_df, CONTEXT_VOLUME, QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD, QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY, edge_evidence)
        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["text"], row["label"], output))
    answer_df = pd.DataFrame(answer_list, columns=["question", "label", "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()