""" 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()