'''
This script takes the drug repurposing 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["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"]
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"]


save_name = "_".join(MODEL_NAME.split("/")[-1].split("-"))+"_drug_repurposing_questions_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, max_new_tokens=1024)               
    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["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()