Datasets:

AAAIBenchmark
/

Multi-Opthalingua

""" Evaluate Medical Tests Classification in LLMS """
## Setup
#### Load the API key and libaries.
import json
import pandas as pd
import os
import openai
from dotenv import load_dotenv, find_dotenv
import argparse
import re
import subprocess

from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.llms import LlamaCpp
import numpy as np

#from langchain.globals import set_verbose

#set_verbose(True)

from langchain.output_parsers import ResponseSchema
from langchain.output_parsers import StructuredOutputParser
from langchain.output_parsers import OutputFixingParser

from langchain.chains import LLMChain


### Download LLAMA model:
def download_and_rename(url, filename):
    """Downloads a file from the given URL and renames it to the given new file name.
    Args:
        url: The URL of the file to download.
        new_file_name: The new file name for the downloaded file.
    """

    os.makedirs(os.path.dirname(filename), exist_ok=True)

    print(f'Downloading the weights of the model: {url} ...')
    subprocess.run(["wget", "-q", "-O", filename, url])
    print(f'Done!')



def download_hugging_face_model(model_version='Llama-2-7b', path=None):
    if model_version not in ['Llama-2-7b', 'Llama-2-13b', 'Llama-2-70b', 'Llama-3-8b', 'Mistral-7b']:
        raise ValueError("Options for Llama model should be llama-2 -7b, -13b or -70b; Mistral-7b; or llama-3 -8b")

    MODEL_URL = {
        'Llama-2-7b': 'https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q8_0.gguf', 
        'Llama-2-13b': 'https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q8_0.gguf', 
        'Llama-2-70b': 'https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF/resolve/main/llama-2-70b-chat.Q5_0.gguf',
        'Mistral-7b': 'https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q8_0.gguf',
        'Llama-3-8b': 'https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q8_0.gguf'
        #'https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF-old/resolve/main/Meta-Llama-3-8B-Instruct-Q6_K.gguf'
    }

    MODEL_URL = MODEL_URL[model_version]

    model_path = f'Models/{model_version}.gguf'

    if path:
        model_path = os.path.join(path, model_path)

    if os.path.exists(model_path):
        confirmation = input(f"The model file '{model_path}' already exists. Do you want to overwrite it? (yes/no): ").strip().lower()
        if confirmation != 'yes':
            print("Model installation aborted.")
            return model_path

    download_and_rename(MODEL_URL, model_path)

    return model_path








### Models:

# Function to validate JSON format
def is_valid_json(text, output_parser):
    try:
        output_parser.parse(text)
        return True
    except:
        return False

def get_completion_from_chain(chain, question, output_parser):
    
    #try:
    #response = chain.predict_and_parse(question=question)
    response = chain.run(question=question)
    print("response")
    print(response)
    if is_valid_json(response, output_parser):
        response = output_parser.parse(response)
        return response
    else:
        if '"response": "a"' in response.lower() or '"response":"a"' in response.lower() or ': "a"' in response.lower() or ': "a"' in response.lower() or '"response": a' in response.lower() or '"response":a' in response.lower() or ': a' in response.lower() or ':a' in response.lower() or "'response': 'a'" in response.lower() or "'response':'a'" in response.lower() or ": 'a'" in response.lower() or ":'a'" in response.lower() or "'response': a" in response.lower() or "'response':a" in response.lower():
            response = {'response': 'a'}
        elif '"response": "b"' in response.lower() or '"response":"b"' in response.lower() or ': "b"' in response.lower() or ': "b"' in response.lower() or '"response": b' in response.lower() or '"response":b' in response.lower() or ': b' in response.lower() or ':b' in response.lower() or "'response': 'b'" in response.lower() or "'response':'b'" in response.lower() or ": 'b'" in response.lower() or ":'b'" in response.lower() or "'response': b" in response.lower() or "'response':b" in response.lower():
            response = {'response': 'b'}
        elif '"response": "c"' in response.lower() or '"response":"c"' in response.lower() or ': "c"' in response.lower() or ': "c"' in response.lower() or '"response": c' in response.lower() or '"response":c' in response.lower() or ': c' in response.lower() or ':c' in response.lower() or "'response': 'c'" in response.lower() or "'response':'c'" in response.lower() or ": 'c'" in response.lower() or ":'c'" in response.lower() or "'response': c" in response.lower() or "'response':c" in response.lower():
            response = {'response': 'c'}
        elif '"response": "d"' in response.lower() or '"response":"d"' in response.lower() or ': "d"' in response.lower() or ': "d"' in response.lower() or '"response": d' in response.lower() or '"response":d' in response.lower() or ': d' in response.lower() or ':d' in response.lower() or "'response': 'd'" in response.lower() or "'response':'d'" in response.lower() or ": 'd'" in response.lower() or ":'d'" in response.lower() or "'response': d" in response.lower() or "'response':d" in response.lower():
            response = {'response': 'd'}
        else:
            response = {'response': np.nan}
        return response
        #new_parser = OutputFixingParser.from_llm(parser=output_parser, llm=ChatOpenAI())
        #response = new_parser.parse(response)
        #print("response")
        #print(response)
        #return response

    #except:
    #    print("except")
    #    response = get_completion_from_chain(chain, question, output_parser)
    #    return response
    #return response


def get_completion_from_messages(messages, 
                                 model,
                                 output_parser):

    #try:
    response = model.invoke(messages)
    # check if response is not a string
    if not isinstance(response, str):
        response = response.content
        
    
    print('response')
    print(response)
    if is_valid_json(response, output_parser):
        response = output_parser.parse(response)
        return response
    else:
        if '"response": "a"' in response.lower() or '"response":"a"' in response.lower() or ': "a"' in response.lower() or ':"a"' in response.lower() or '"response": a' in response.lower() or '"response":a' in response.lower() or ': a' in response.lower() or ':a' in response.lower() or "'response': 'a'" in response.lower() or "'response':'a'" in response.lower() or ": 'a'" in response.lower() or ":'a'" in response.lower() or "'response': a" in response.lower() or "'response':a" in response.lower():
            response = {'response': 'a'}
        elif '"response": "b"' in response.lower() or '"response":"b"' in response.lower() or ': "b"' in response.lower() or ':"b"' in response.lower() or '"response": b' in response.lower() or '"response":b' in response.lower() or ': b' in response.lower() or ':b' in response.lower() or "'response': 'b'" in response.lower() or "'response':'b'" in response.lower() or ": 'b'" in response.lower() or ":'b'" in response.lower() or "'response': b" in response.lower() or "'response':b" in response.lower():
            response = {'response': 'b'}
        elif '"response": "c"' in response.lower() or '"response":"c"' in response.lower() or ': "c"' in response.lower() or ':"c"' in response.lower() or '"response": c' in response.lower() or '"response":c' in response.lower() or ': c' in response.lower() or ':c' in response.lower() or "'response': 'c'" in response.lower() or "'response':'c'" in response.lower() or ": 'c'" in response.lower() or ":'c'" in response.lower() or "'response': c" in response.lower() or "'response':c" in response.lower():
            response = {'response': 'c'}
        elif '"response": "d"' in response.lower() or '"response":"d"' in response.lower() or ': "d"' in response.lower() or ':"d"' in response.lower() or '"response": d' in response.lower() or '"response":d' in response.lower() or ': d' in response.lower() or ':d' in response.lower() or "'response': 'd'" in response.lower() or "'response':'d'" in response.lower() or ": 'd'" in response.lower() or ":'d'" in response.lower() or "'response': d" in response.lower() or "'response':d" in response.lower():
            response = {'response': 'd'}
        else:
            response = {'response': np.nan}
        return response

    #except:
    #    response = get_completion_from_messages(messages, model=model)
    #    return response


#### Template for the Questions
def generate_prompt(LANGUAGES, REASONING, Responses=['A', 'B', 'C', 'D']):
    
    delimiter = "####"

    languages_text = ", ".join(LANGUAGES)
    
    responses_text = ", ".join(Responses)

    system_message = f"""You are an expert medical assistant.\
You will be provided with medical queries in these languages: {languages_text}. \
Answer the question as best as possible.\
    """
    #Always select an answer from the following options in a json with the defined format. Options: {responses_text}.
    
    template = system_message + "\n{format_instructions}\n{question}"


    response_schema = ResponseSchema(name="response",
                                     description=f"This is the option of the correct response. Could be only any of these: {responses_text}")

    if REASONING:
        reasoning_schema = ResponseSchema(name="reasoning",
                                    description="This is the reasons for the answer")
        response_schemas = [response_schema, 
                            reasoning_schema]
    else:
        response_schemas = [response_schema]        

    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

    format_instructions = output_parser.get_format_instructions()
    
    prompt = PromptTemplate(
        template=template,
        input_variables=["question"],
        partial_variables={"format_instructions": format_instructions}
    )
    
    return prompt, output_parser


def llm_language_evaluation(path='data/Portuguese.csv', model='gpt-3.5-turbo', temperature=0.0, n_repetitions=1, reasoning=False, languages=['english', 'portuguese'], llm_chain=False, model_path=None, local=False):
    
    model_id = model
    
    # Load API key if GPT, or Model if LLAMA
    if 'gpt' in model:
        _ = load_dotenv(find_dotenv()) # read local .env file
        openai.api_key  = os.environ['OPENAI_API_KEY']
        llm = OpenAI(temperature=temperature, model_name=model)
        
    #elif 'Llama-2' in model or ('Mistral-7b' in model) or 'Llama-3' in model:    
    #    
    #    model_path = download_hugging_face_model(model_version=model, path=model_path)
    #    llm = LlamaCpp(
    #        model_path=model_path,
    #        temperature=temperature,
    #        n_ctx=2048,
    #        verbose=False,  # VERBOSE
    #    )
    elif 'Llama-2' in model or ('Mistral' in model) or ('Llama-3' in model) or ('Mixtral' in model) or ('Qwen2' in model):
        if local:
            # Define your custom path
            import os
            os.environ['TRANSFORMERS_CACHE'] = '/scratch/liyues_root/liyues/chenweiw/hf_weigths/llama'
            
            from torch import cuda, bfloat16
            import torch

            import transformers
            
            from langchain.llms import HuggingFacePipeline
            
            # set quantization configuration to load large model with less GPU memory
            # this requires the `bitsandbytes` library
            bnb_config = transformers.BitsAndBytesConfig(
                load_in_4bit=True,
                #load_in_8bit=True,
                bnb_4bit_quant_type='nf4',
                bnb_4bit_use_double_quant=True,
                bnb_4bit_compute_dtype=bfloat16,
                #load_in_8bit_fp32_cpu_offload=True
            )

            # begin initializing HF items, need auth token for these
            model_config = transformers.AutoConfig.from_pretrained(
                model_id
            )

            model = transformers.AutoModelForCausalLM.from_pretrained(
                model_id,
                trust_remote_code=True,
                config=model_config,
                quantization_config=bnb_config,
                device_map='auto',
            )
            
            tokenizer = transformers.AutoTokenizer.from_pretrained(
                model_id
            )
            
            generate_text = transformers.pipeline(
                model=model, tokenizer=tokenizer,
                return_full_text=True,
                task='text-generation',
                do_sample=False,
                repetition_penalty=1.1
            )
            
            from langchain.llms import HuggingFacePipeline
            llm = HuggingFacePipeline(pipeline=generate_text)
        else:
            import os
            _ = load_dotenv(find_dotenv())
            together_api_key = os.environ['Together_API_KEY']
            from langchain_openai import ChatOpenAI

            llm = ChatOpenAI(
                openai_api_base="https://api.together.xyz",
                api_key=together_api_key,
                model=model_id,
                temperature=temperature,
            )
                
    else:
        print('Model should be a GPT, Llama, Mistral or any model available in Open Ai or Toghether AI')
        return 0
    
    #### Load the Constants
    PATH = path # 'data/Portuguese.csv'
    MODEL = model_id # "gpt-3.5-turbo"
    TEMPERATURE = temperature # 0.0
    N_REPETITIONS = n_repetitions # 1
    REASONING = reasoning # False
    LANGUAGES = languages # ['english', 'portuguese']

    if N_REPETITIONS <= 0 or (N_REPETITIONS != int(N_REPETITIONS)):
        print(f'N_REPETITIONS should be a positive integer, not {N_REPETITIONS}')
        print('N_REPETITIONS will be set to 1')
        N_REPETITIONS = 1

    ### Questions from a csv file:
    df = pd.read_csv(PATH)

    ### Evaluate the model in question answering per language:
    responses = {}
    reasoning = {}
    for language in LANGUAGES:
        responses[language] = [[] for n in range(N_REPETITIONS)]

        if REASONING:
            reasoning[language] = [[] for n in range(N_REPETITIONS)]
            
    prompt, output_parser = generate_prompt(LANGUAGES, REASONING)


    for row in range(df.shape[0]):

        print('*'*50)
        print(f'Question {row+1}: ')
        for language in LANGUAGES:
            print(f'Language: {language}')
            question = df[language][row]
            print('Question: ')
            print(question)
            
            if llm_chain:
                chain = LLMChain(llm=llm, prompt=prompt)
            else:
                messages = prompt.format_prompt(question=question)
                if not('gpt') in model_id.lower():
                    messages = messages.to_string()

            for n in range(N_REPETITIONS): 
                print(f'Test #{n}: ')
                if llm_chain:
                    response = get_completion_from_chain(chain, question, output_parser)
                else:
                    response = get_completion_from_messages(messages, llm, output_parser)

                print(response)
            
                # Append to the list:
                responses[language][n].append(response['response'])
                if REASONING:
                    reasoning[language][n].append(response['reasoning'])
                
        print('*'*50)

    ### Save the results in a csv file:
    for language in LANGUAGES:
        if N_REPETITIONS == 1:
            df[f'responses_{language}'] = responses[language][0]
            if REASONING:
                df[f'reasoning_{language}'] = reasoning[language][0]
                
        for n in range(N_REPETITIONS):
            df[f'responses_{language}_{n}'] = responses[language][n]
            if REASONING:
                df[f'reasoning_{language}_{n}'] = reasoning[language][n]

    if not os.path.exists('responses'):
        os.makedirs('responses')
        
    # Get the base name of the MODEL to remove any parent directories
    MODEL = os.path.basename(MODEL)
    
    if N_REPETITIONS == 1:
        df.to_csv(f"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}.csv", index=False)
    else:
        df.to_csv(f"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}_{N_REPETITIONS}Repetitions.csv", index=False)

def main():
    # Add argparse code to handle command-line arguments
    parser = argparse.ArgumentParser(description="Evaluate Medical Tests Classification in LLMS")
    parser.add_argument("--csv_file", default="data/Portuguese.csv", help="Path to the CSV file with the questions")
    parser.add_argument("--model", default="gpt-3.5-turbo", help="LLM to use e.g: gpt-3.5-turbo, gpt-4, Llama-2-7b, Llama-2-13b, or Llama-2-70b")
    parser.add_argument("--temperature", type=float, default=0.0, help="Temperature parameter of the model between 0 and 1. Used to modifiy the model's creativity. 0 is deterministic and 1 is the most creative")
    parser.add_argument("--n_repetitions", type=int, default=1, help="Number of repetitions to run each experiment. Used to measure the model's hallucinations")
    parser.add_argument("--reasoning", action="store_true", default=False, help="Enable reasoning mode. If set to True, the model will be asked to provide a reasoning for its answer. If set to True the model uses more tokens")
    parser.add_argument("--languages", nargs='+', default=['english', 'portuguese'], help="List of languages")
    parser.add_argument("--llm_chain", action="store_true", default=False, help="Enable the use of ")
    
    args = parser.parse_args()


    PATH = args.csv_file
    MODEL = args.model
    TEMPERATURE = args.temperature
    N_REPETITIONS = args.n_repetitions
    REASONING = args.reasoning
    LANGUAGES = args.languages
    llm_chain = args.llm_chain
    
    llm_language_evaluation(path=PATH, model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, reasoning=REASONING, languages=LANGUAGES, llm_chain=llm_chain)


if __name__ == "__main__":
    main()

	""" Evaluate Medical Tests Classification in LLMS """
	## Setup
	#### Load the API key and libaries.
	import json
	import pandas as pd
	import os
	import openai
	from dotenv import load_dotenv, find_dotenv
	import argparse
	import re
	import subprocess

	from langchain.prompts import PromptTemplate
	from langchain.chat_models import ChatOpenAI
	from langchain.llms import OpenAI
	from langchain.llms import LlamaCpp
	import numpy as np

	#from langchain.globals import set_verbose

	#set_verbose(True)

	from langchain.output_parsers import ResponseSchema
	from langchain.output_parsers import StructuredOutputParser
	from langchain.output_parsers import OutputFixingParser

	from langchain.chains import LLMChain


	### Download LLAMA model:
	def download_and_rename(url, filename):
	"""Downloads a file from the given URL and renames it to the given new file name.

	Args:
	url: The URL of the file to download.
	new_file_name: The new file name for the downloaded file.
	"""

	os.makedirs(os.path.dirname(filename), exist_ok=True)

	print(f'Downloading the weights of the model: {url} ...')
	subprocess.run(["wget", "-q", "-O", filename, url])
	print(f'Done!')



	def download_hugging_face_model(model_version='Llama-2-7b', path=None):
	if model_version not in ['Llama-2-7b', 'Llama-2-13b', 'Llama-2-70b', 'Llama-3-8b', 'Mistral-7b']:
	raise ValueError("Options for Llama model should be llama-2 -7b, -13b or -70b; Mistral-7b; or llama-3 -8b")

	MODEL_URL = {
	'Llama-2-7b': 'https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q8_0.gguf',
	'Llama-2-13b': 'https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q8_0.gguf',
	'Llama-2-70b': 'https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF/resolve/main/llama-2-70b-chat.Q5_0.gguf',
	'Mistral-7b': 'https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q8_0.gguf',
	'Llama-3-8b': 'https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q8_0.gguf'
	#'https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF-old/resolve/main/Meta-Llama-3-8B-Instruct-Q6_K.gguf'
	}

	MODEL_URL = MODEL_URL[model_version]

	model_path = f'Models/{model_version}.gguf'

	if path:
	model_path = os.path.join(path, model_path)

	if os.path.exists(model_path):
	confirmation = input(f"The model file '{model_path}' already exists. Do you want to overwrite it? (yes/no): ").strip().lower()
	if confirmation != 'yes':
	print("Model installation aborted.")
	return model_path

	download_and_rename(MODEL_URL, model_path)

	return model_path








	### Models:

	# Function to validate JSON format
	def is_valid_json(text, output_parser):
	try:
	output_parser.parse(text)
	return True
	except:
	return False

	def get_completion_from_chain(chain, question, output_parser):

	#try:
	#response = chain.predict_and_parse(question=question)
	response = chain.run(question=question)
	print("response")
	print(response)
	if is_valid_json(response, output_parser):
	response = output_parser.parse(response)
	return response
	else:
	if '"response": "a"' in response.lower() or '"response":"a"' in response.lower() or ': "a"' in response.lower() or ': "a"' in response.lower() or '"response": a' in response.lower() or '"response":a' in response.lower() or ': a' in response.lower() or ':a' in response.lower() or "'response': 'a'" in response.lower() or "'response':'a'" in response.lower() or ": 'a'" in response.lower() or ":'a'" in response.lower() or "'response': a" in response.lower() or "'response':a" in response.lower():
	response = {'response': 'a'}
	elif '"response": "b"' in response.lower() or '"response":"b"' in response.lower() or ': "b"' in response.lower() or ': "b"' in response.lower() or '"response": b' in response.lower() or '"response":b' in response.lower() or ': b' in response.lower() or ':b' in response.lower() or "'response': 'b'" in response.lower() or "'response':'b'" in response.lower() or ": 'b'" in response.lower() or ":'b'" in response.lower() or "'response': b" in response.lower() or "'response':b" in response.lower():
	response = {'response': 'b'}
	elif '"response": "c"' in response.lower() or '"response":"c"' in response.lower() or ': "c"' in response.lower() or ': "c"' in response.lower() or '"response": c' in response.lower() or '"response":c' in response.lower() or ': c' in response.lower() or ':c' in response.lower() or "'response': 'c'" in response.lower() or "'response':'c'" in response.lower() or ": 'c'" in response.lower() or ":'c'" in response.lower() or "'response': c" in response.lower() or "'response':c" in response.lower():
	response = {'response': 'c'}
	elif '"response": "d"' in response.lower() or '"response":"d"' in response.lower() or ': "d"' in response.lower() or ': "d"' in response.lower() or '"response": d' in response.lower() or '"response":d' in response.lower() or ': d' in response.lower() or ':d' in response.lower() or "'response': 'd'" in response.lower() or "'response':'d'" in response.lower() or ": 'd'" in response.lower() or ":'d'" in response.lower() or "'response': d" in response.lower() or "'response':d" in response.lower():
	response = {'response': 'd'}
	else:
	response = {'response': np.nan}
	return response
	#new_parser = OutputFixingParser.from_llm(parser=output_parser, llm=ChatOpenAI())
	#response = new_parser.parse(response)
	#print("response")
	#print(response)
	#return response

	#except:
	# print("except")
	# response = get_completion_from_chain(chain, question, output_parser)
	# return response
	#return response


	def get_completion_from_messages(messages,
	model,
	output_parser):

	#try:
	response = model.invoke(messages)
	# check if response is not a string
	if not isinstance(response, str):
	response = response.content


	print('response')
	print(response)
	if is_valid_json(response, output_parser):
	response = output_parser.parse(response)
	return response
	else:
	if '"response": "a"' in response.lower() or '"response":"a"' in response.lower() or ': "a"' in response.lower() or ':"a"' in response.lower() or '"response": a' in response.lower() or '"response":a' in response.lower() or ': a' in response.lower() or ':a' in response.lower() or "'response': 'a'" in response.lower() or "'response':'a'" in response.lower() or ": 'a'" in response.lower() or ":'a'" in response.lower() or "'response': a" in response.lower() or "'response':a" in response.lower():
	response = {'response': 'a'}
	elif '"response": "b"' in response.lower() or '"response":"b"' in response.lower() or ': "b"' in response.lower() or ':"b"' in response.lower() or '"response": b' in response.lower() or '"response":b' in response.lower() or ': b' in response.lower() or ':b' in response.lower() or "'response': 'b'" in response.lower() or "'response':'b'" in response.lower() or ": 'b'" in response.lower() or ":'b'" in response.lower() or "'response': b" in response.lower() or "'response':b" in response.lower():
	response = {'response': 'b'}
	elif '"response": "c"' in response.lower() or '"response":"c"' in response.lower() or ': "c"' in response.lower() or ':"c"' in response.lower() or '"response": c' in response.lower() or '"response":c' in response.lower() or ': c' in response.lower() or ':c' in response.lower() or "'response': 'c'" in response.lower() or "'response':'c'" in response.lower() or ": 'c'" in response.lower() or ":'c'" in response.lower() or "'response': c" in response.lower() or "'response':c" in response.lower():
	response = {'response': 'c'}
	elif '"response": "d"' in response.lower() or '"response":"d"' in response.lower() or ': "d"' in response.lower() or ':"d"' in response.lower() or '"response": d' in response.lower() or '"response":d' in response.lower() or ': d' in response.lower() or ':d' in response.lower() or "'response': 'd'" in response.lower() or "'response':'d'" in response.lower() or ": 'd'" in response.lower() or ":'d'" in response.lower() or "'response': d" in response.lower() or "'response':d" in response.lower():
	response = {'response': 'd'}
	else:
	response = {'response': np.nan}
	return response

	#except:
	# response = get_completion_from_messages(messages, model=model)
	# return response


	#### Template for the Questions
	def generate_prompt(LANGUAGES, REASONING, Responses=['A', 'B', 'C', 'D']):

	delimiter = "####"

	languages_text = ", ".join(LANGUAGES)

	responses_text = ", ".join(Responses)

	system_message = f"""You are an expert medical assistant.\
	You will be provided with medical queries in these languages: {languages_text}. \
	Answer the question as best as possible.\
	"""
	#Always select an answer from the following options in a json with the defined format. Options: {responses_text}.

	template = system_message + "\n{format_instructions}\n{question}"


	response_schema = ResponseSchema(name="response",
	description=f"This is the option of the correct response. Could be only any of these: {responses_text}")

	if REASONING:
	reasoning_schema = ResponseSchema(name="reasoning",
	description="This is the reasons for the answer")
	response_schemas = [response_schema,
	reasoning_schema]
	else:
	response_schemas = [response_schema]

	output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

	format_instructions = output_parser.get_format_instructions()

	prompt = PromptTemplate(
	template=template,
	input_variables=["question"],
	partial_variables={"format_instructions": format_instructions}
	)

	return prompt, output_parser


	def llm_language_evaluation(path='data/Portuguese.csv', model='gpt-3.5-turbo', temperature=0.0, n_repetitions=1, reasoning=False, languages=['english', 'portuguese'], llm_chain=False, model_path=None, local=False):

	model_id = model

	# Load API key if GPT, or Model if LLAMA
	if 'gpt' in model:
	_ = load_dotenv(find_dotenv()) # read local .env file
	openai.api_key = os.environ['OPENAI_API_KEY']
	llm = OpenAI(temperature=temperature, model_name=model)

	#elif 'Llama-2' in model or ('Mistral-7b' in model) or 'Llama-3' in model:
	#
	# model_path = download_hugging_face_model(model_version=model, path=model_path)
	# llm = LlamaCpp(
	# model_path=model_path,
	# temperature=temperature,
	# n_ctx=2048,
	# verbose=False, # VERBOSE
	# )
	elif 'Llama-2' in model or ('Mistral' in model) or ('Llama-3' in model) or ('Mixtral' in model) or ('Qwen2' in model):
	if local:
	# Define your custom path
	import os
	os.environ['TRANSFORMERS_CACHE'] = '/scratch/liyues_root/liyues/chenweiw/hf_weigths/llama'

	from torch import cuda, bfloat16
	import torch

	import transformers

	from langchain.llms import HuggingFacePipeline

	# set quantization configuration to load large model with less GPU memory
	# this requires the `bitsandbytes` library
	bnb_config = transformers.BitsAndBytesConfig(
	load_in_4bit=True,
	#load_in_8bit=True,
	bnb_4bit_quant_type='nf4',
	bnb_4bit_use_double_quant=True,
	bnb_4bit_compute_dtype=bfloat16,
	#load_in_8bit_fp32_cpu_offload=True
	)

	# begin initializing HF items, need auth token for these
	model_config = transformers.AutoConfig.from_pretrained(
	model_id
	)

	model = transformers.AutoModelForCausalLM.from_pretrained(
	model_id,
	trust_remote_code=True,
	config=model_config,
	quantization_config=bnb_config,
	device_map='auto',
	)

	tokenizer = transformers.AutoTokenizer.from_pretrained(
	model_id
	)

	generate_text = transformers.pipeline(
	model=model, tokenizer=tokenizer,
	return_full_text=True,
	task='text-generation',
	do_sample=False,
	repetition_penalty=1.1
	)

	from langchain.llms import HuggingFacePipeline
	llm = HuggingFacePipeline(pipeline=generate_text)
	else:
	import os
	_ = load_dotenv(find_dotenv())
	together_api_key = os.environ['Together_API_KEY']
	from langchain_openai import ChatOpenAI

	llm = ChatOpenAI(
	openai_api_base="https://api.together.xyz",
	api_key=together_api_key,
	model=model_id,
	temperature=temperature,
	)

	else:
	print('Model should be a GPT, Llama, Mistral or any model available in Open Ai or Toghether AI')
	return 0

	#### Load the Constants
	PATH = path # 'data/Portuguese.csv'
	MODEL = model_id # "gpt-3.5-turbo"
	TEMPERATURE = temperature # 0.0
	N_REPETITIONS = n_repetitions # 1
	REASONING = reasoning # False
	LANGUAGES = languages # ['english', 'portuguese']

	if N_REPETITIONS <= 0 or (N_REPETITIONS != int(N_REPETITIONS)):
	print(f'N_REPETITIONS should be a positive integer, not {N_REPETITIONS}')
	print('N_REPETITIONS will be set to 1')
	N_REPETITIONS = 1

	### Questions from a csv file:
	df = pd.read_csv(PATH)

	### Evaluate the model in question answering per language:
	responses = {}
	reasoning = {}
	for language in LANGUAGES:
	responses[language] = [[] for n in range(N_REPETITIONS)]

	if REASONING:
	reasoning[language] = [[] for n in range(N_REPETITIONS)]

	prompt, output_parser = generate_prompt(LANGUAGES, REASONING)


	for row in range(df.shape[0]):

	print(''50)
	print(f'Question {row+1}: ')
	for language in LANGUAGES:
	print(f'Language: {language}')
	question = df[language][row]
	print('Question: ')
	print(question)

	if llm_chain:
	chain = LLMChain(llm=llm, prompt=prompt)
	else:
	messages = prompt.format_prompt(question=question)
	if not('gpt') in model_id.lower():
	messages = messages.to_string()

	for n in range(N_REPETITIONS):
	print(f'Test #{n}: ')
	if llm_chain:
	response = get_completion_from_chain(chain, question, output_parser)
	else:
	response = get_completion_from_messages(messages, llm, output_parser)

	print(response)

	# Append to the list:
	responses[language][n].append(response['response'])
	if REASONING:
	reasoning[language][n].append(response['reasoning'])

	print(''50)

	### Save the results in a csv file:
	for language in LANGUAGES:
	if N_REPETITIONS == 1:
	df[f'responses_{language}'] = responses[language][0]
	if REASONING:
	df[f'reasoning_{language}'] = reasoning[language][0]

	for n in range(N_REPETITIONS):
	df[f'responses_{language}_{n}'] = responses[language][n]
	if REASONING:
	df[f'reasoning_{language}_{n}'] = reasoning[language][n]

	if not os.path.exists('responses'):
	os.makedirs('responses')

	# Get the base name of the MODEL to remove any parent directories
	MODEL = os.path.basename(MODEL)

	if N_REPETITIONS == 1:
	df.to_csv(f"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}.csv", index=False)
	else:
	df.to_csv(f"responses/{MODEL}_Temperature{str(TEMPERATURE).replace('.', '_')}_{N_REPETITIONS}Repetitions.csv", index=False)

	def main():
	# Add argparse code to handle command-line arguments
	parser = argparse.ArgumentParser(description="Evaluate Medical Tests Classification in LLMS")
	parser.add_argument("--csv_file", default="data/Portuguese.csv", help="Path to the CSV file with the questions")
	parser.add_argument("--model", default="gpt-3.5-turbo", help="LLM to use e.g: gpt-3.5-turbo, gpt-4, Llama-2-7b, Llama-2-13b, or Llama-2-70b")
	parser.add_argument("--temperature", type=float, default=0.0, help="Temperature parameter of the model between 0 and 1. Used to modifiy the model's creativity. 0 is deterministic and 1 is the most creative")
	parser.add_argument("--n_repetitions", type=int, default=1, help="Number of repetitions to run each experiment. Used to measure the model's hallucinations")
	parser.add_argument("--reasoning", action="store_true", default=False, help="Enable reasoning mode. If set to True, the model will be asked to provide a reasoning for its answer. If set to True the model uses more tokens")
	parser.add_argument("--languages", nargs='+', default=['english', 'portuguese'], help="List of languages")
	parser.add_argument("--llm_chain", action="store_true", default=False, help="Enable the use of ")

	args = parser.parse_args()


	PATH = args.csv_file
	MODEL = args.model
	TEMPERATURE = args.temperature
	N_REPETITIONS = args.n_repetitions
	REASONING = args.reasoning
	LANGUAGES = args.languages
	llm_chain = args.llm_chain

	llm_language_evaluation(path=PATH, model=MODEL, temperature=TEMPERATURE, n_repetitions=N_REPETITIONS, reasoning=REASONING, languages=LANGUAGES, llm_chain=llm_chain)


	if __name__ == "__main__":
	main()