'''
This script takes the True/False style questions from the csv file and save the result as another csv file. 
This script makes use of Llama model.
Before running this script, make sure to configure the filepaths in config.yaml file.
'''

from langchain import PromptTemplate, LLMChain
from kg_rag.utility import *
import sys


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"]
SAVE_PATH = config_data["SAVE_RESULTS_PATH"]
MODEL_NAME = config_data["LLAMA_MODEL_NAME"]
BRANCH_NAME = config_data["LLAMA_MODEL_BRANCH"]
CACHE_DIR = config_data["LLM_CACHE_DIR"]
CONTEXT_VOLUME = 100

save_name = "_".join(MODEL_NAME.split("/")[-1].split("-"))+"_one_hop_true_false_binary_response.csv"


INSTRUCTION = "Context:\n\n{context} \n\nQuestion: {question}"

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()
    llm = llama_model(MODEL_NAME, BRANCH_NAME, CACHE_DIR)               
    template = get_prompt(INSTRUCTION, SYSTEM_PROMPT)
    prompt = PromptTemplate(template=template, input_variables=["context", "question"])
    llm_chain = LLMChain(prompt=prompt, llm=llm)    
    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)
        output = llm_chain.run(context=context, question=question)
        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()
    
    
langchain.LLMChain langchain.PromptTemplate
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