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""" 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() |