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main.py
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main.py
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import yaml
from utils.text_processing import *
from utils.model import *
import argparse
import utils.config as config
import pandas as pd
import pickle
from tqdm import tqdm
import datetime
import sqlite3
from time import time, sleep
from utils.utils import *
import openai
def load_yaml_config(yaml_file):
with open(yaml_file, 'r') as file:
yaml_config = yaml.safe_load(file)
return yaml_config
def main():
parser = argparse.ArgumentParser(description="Model Loader")
parser.add_argument('--model', type=str, required=True, help='Name of the model to load')
parser.add_argument('--config_yaml', type=str, help='Path to the YAML configuration file')
parser.add_argument('--mode', type=str, choices=['few_shot', 'zero_shot'], default='few_shot', help='Mode to run the model: few_shot or zero_shot (default: few_shot)')
args = parser.parse_args()
# Load model
mode = args.mode
model_name = args.model
if model_name in ['tulu2', 'mixtral','llama3']:
model, tokenizer, model_config = load_model(model_name)
# Load YAML configuration
yaml_cnfig = load_yaml_config(args.config_yaml)
py_confoig = {
"DATA_FILES": config.DATA_FILES,
"DB_PATH": config.DB_PATH,
"OPEN_API_KEY": config.OPEN_API_KEY,
"API_BASE":config.API_BASE,
"API_MODEL":config.API_MODEL
}
note_types = yaml_cnfig['note_types']
result_path_=yaml_cnfig['result_path']
OPEN_API_KEY= py_confoig['OPEN_API_KEY']
API_BASE = py_confoig['API_BASE']
API_MODEL = py_confoig['API_MODEL']
df = pd.read_csv(yaml_cnfig['CSV_FILE'])
with open(py_confoig['DATA_FILES']['top_items'], 'rb') as f:
top_items = pickle.load(f)
with open(py_confoig['DATA_FILES']['predefined_mapping'], 'rb') as f:
predef_map = pickle.load(f)
sets = pd.read_csv(py_confoig['DATA_FILES']['abbreviation'])
abb_df = pd.DataFrame(sets, columns=['abbreviation', 'expansion', 'whole_set'])
lab_id = pd.read_csv(py_confoig['DATA_FILES']['lab_items'])
d_items = pd.read_csv(py_confoig['DATA_FILES']['d_items'])
prescriptions = pd.read_csv(py_confoig['DATA_FILES']['prescriptions'])
d_icd_procedures = pd.read_csv(py_confoig['DATA_FILES']['icd_procedures'])
d_icd_diagnoses = pd.read_csv(py_confoig['DATA_FILES']['icd_diagnoses'])
lower_label_itemid_dict, d_item_lower_label_itemid_dict, prescriptions_itemid_dict, d_icd_procedures_dict, d_icd_diagnoses_dict, label_keys, d_item_label_keys, prescription_keys, drug_list, d_icd_procedures_label_keys, d_icd_diagnoses_label_keys = create_dicts(lab_id, d_items, prescriptions, d_icd_procedures, d_icd_diagnoses)
for idx, row in tqdm(df.iterrows(), total=df.shape[0]):
split_dict = {}
text_line_split = row['TEXT'].split('\n')
for idx2,line2 in enumerate(text_line_split):
split_dict[idx2] = line2
total_dict={}
hadm_id = row['HADM_ID'] ##333.0
print("hadm_id: ",hadm_id)
hadm_id_string= str(int(hadm_id)) ###333
text = row['TEXT']
admission_string = row['ADMITTIME']
date_object = datetime.datetime.strptime(admission_string, "%Y-%m-%d %H:%M:%S")
admission = date_object.strftime("%Y-%m-%d")
charttime = row['CHARTDATE']
type = row['CATEGORY']
db_path_=py_confoig['DB_PATH']
db_path = f'{db_path_}/sample_{hadm_id}.db'
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
print()
if model_name in ['tulu2', 'mixtral','llama3']:
sections,sections_dict_ner=extract_sections(text, hadm_id, 0, model, tokenizer, model_config,yaml_cnfig['prompt_files']['note_segmentation'],model_name)
else:
sections,sections_dict_ner=extract_sections_gpt(text, hadm_id, API_MODEL, OPEN_API_KEY, API_BASE,yaml_cnfig['prompt_files']['note_segmentation'],0)
for sid,section in enumerate(sections):
total_list=[]
table_dict={}
query_output_dict={}
table_filling={}
fill_the_table_dict={}
self_correction_dict_={}
reformat_value={}
table_filter=[]
extracted_list = []
parsed_input ={}
print("Number of Section: ",sid)
indices_regex=[]
print()
ner_prompt_path = yaml_cnfig['prompt_files']['named_entity_recognition']
if mode == 'zero_shot':
if note_types == 'discharge':
ner_prompt = open_file(f'{ner_prompt_path}_discharge.txt').replace("<<<CLINICAL_NOTE>>>",sections_dict_ner[sid])
else:
ner_prompt = open_file(f'{ner_prompt_path}.txt').replace("<<<CLINICAL_NOTE>>>",sections_dict_ner[sid])
else:
if model_name == 'tulu2':
ner_prompt = open_file(f'{ner_prompt_path}_tulu.txt').replace("<<<CLINICAL_NOTE>>>",sections_dict_ner[sid])
else:
ner_prompt = open_file(f'{ner_prompt_path}.txt').replace("<<<CLINICAL_NOTE>>>",sections_dict_ner[sid])
if model_name == 'llama3':
ner_output = open_source_model_inference_llama(ner_prompt, model, tokenizer, model_config)
elif model_name == 'chatgpt':
ner_output = chatgpt_completion(ner_prompt, API_MODEL, OPEN_API_KEY, API_BASE)
else:
ner_output = open_source_model_inference(ner_prompt, model, tokenizer, model_config)
if note_types == 'discharge':
ner_out_=extract_entities_general_dis(ner_output)
else:
ner_out_=extract_entities_general_nur_phy(ner_output)
if ner_out_ == "Step 7 not found in the text." or ner_out_ == "Step 5 not found in the text.":
continue
final_list=filter_entities(ner_out_)
extracted_list = [item['E'] for item in final_list]
extracted_list=list(set(extracted_list))
extracted_list=remove_none_from_list(extracted_list)
print("Named Entity Recognition: ", extracted_list)
print()
for extract in extracted_list:
word_pattern = create_regex_pattern(extract)
try:
for match in re.finditer(word_pattern, section):
start_idx, end_idx = match.start(), match.end() - 1
indices_regex.append({extract: [start_idx, end_idx]})
except:
print("except: ",extract)
continue
temp_dict=remove_overlapping_spans_correctly(indices_regex)
final_dict_entity=remove_invalid_labels(section,temp_dict)
transformed_texts_list = transform_text_corrected(section, final_dict_entity)
for rid,rex in enumerate(final_dict_entity):
for key_entity, where_idx in rex.items():
print("Entity: ",key_entity)
found_line = find_line_containing_word2(section,text, where_idx, key_entity)
print()
table_iden_path = yaml_cnfig['prompt_files']['table_identification']
if mode == 'zero_shot':
if note_types == 'discharge':
table_identification_prompt = open_file(f'{table_iden_path}/table_identification_discharge.txt').replace('<<<<ENTITY>>>>',key_entity)
else:
table_identification_prompt = open_file(f'{table_iden_path}/table_identification.txt').replace('<<<<ENTITY>>>>',key_entity)
else:
table_identification_prompt = open_file(f'{table_iden_path}/table_identification.txt').replace('<<<<ENTITY>>>>',key_entity)
if model_name == 'llama3':
table_identification_output = open_source_model_inference_llama(table_identification_prompt, model, tokenizer, model_config)
elif model_name == 'chatgpt':
table_identification_output = chatgpt_completion(table_identification_prompt, API_MODEL, OPEN_API_KEY, API_BASE)
else:
table_identification_output = open_source_model_inference(table_identification_prompt, model, tokenizer, model_config)
table_identification_output=extract_section_ranges(table_identification_output)
print("Output of Table Identification: ", table_identification_output)
print()
if "none" not in table_identification_output.lower():
transformed_list = transform_to_nested_list(table_identification_output)
table_filling[extract]=transformed_list
time_filter_path = yaml_cnfig['prompt_files']['time_filtering']
if mode == 'zero_shot':
if note_types == 'discharge':
time_filter = open_file(f'{time_filter_path}/time_filtering_discharge.txt').replace('<<<CLINICAL_NOTE>>>', transformed_texts_list[rid]).replace('<<<ENTITY>>>',key_entity).replace("<<<CHARTTIME>>>",charttime).replace("<<<ADMISSION>>>",admission)
else:
time_filter = open_file(f'{time_filter_path}/time_filtering.txt').replace('<<<CLINICAL_NOTE>>>', transformed_texts_list[rid]).replace('<<<ENTITY>>>',key_entity).replace("<<<CHARTTIME>>>",charttime).replace("<<<ADMISSION>>>",admission)
else:
time_filter = open_file(f'{time_filter_path}/time_filtering.txt').replace('<<<CLINICAL_NOTE>>>', transformed_texts_list[rid]).replace('<<<ENTITY>>>',key_entity).replace("<<<CHARTTIME>>>",charttime).replace("<<<ADMISSION>>>",admission)
if model_name == 'llama3':
time_output = open_source_model_inference_llama(time_filter, model, tokenizer, model_config)
elif model_name == 'chatgpt':
time_output = chatgpt_completion(time_filter, API_MODEL, OPEN_API_KEY, API_BASE)
else:
time_output = open_source_model_inference(time_filter, model, tokenizer, model_config)
dict,time_span,time_expression,time_concept=parse_questions_with_return(time_output)
question1_value = dict.get('question1', 'Not Found')
question3_value = dict.get('question3', 'Not Found')
print("Output of Time Filtering: ",question3_value)
res_list_by_table = []
for table in transformed_list:
print()
print("Table to be examined: ",table)
print()
extracted_occurence_list_=[]
extracted_occurence_list2=[]
all_nan = True
if table == ['Microbiologyevents', 'd_items']:
table = ['Microbiologyevents']
for tab in table:
tab=tab.lower()
if tab == 'procedures_icd':
continue
if tab == 'diagnoses_icd':
continue
pseudo_table_path = yaml_cnfig['prompt_files']['pseudo_table_creation']
fill_table = open_file(f'{pseudo_table_path}/{tab}.txt').replace('<<<<CLINICAL_NOTE>>>>', transformed_texts_list[rid]).replace('<<<ENTITY>>>',key_entity).replace("<<<<Admission>>>>",admission).replace("<<<<Charttime>>>>",charttime).replace("<<<<admission>>>>",admission)
if model_name == 'llama3':
fill_table_output = open_source_model_inference_llama(fill_table, model, tokenizer, model_config)
elif model_name == 'chatgpt':
fill_table_output = chatgpt_completion(fill_table, API_MODEL, OPEN_API_KEY, API_BASE)
else:
fill_table_output = open_source_model_inference(fill_table, model, tokenizer, model_config)
extract_output=extract_occurrences(fill_table_output, tab)
print(f"Output of Pseudo Table Creation (Table: {tab}): ",extract_output)
extracted_occurence_list_.append(extract_output)
extracted_occurence_list2.append([extract_output,table])
fill_the_table_dict[key_entity]=extracted_occurence_list2
output_extracted_occurence_list= combine_lists(extracted_occurence_list_)
res_list_by_occur = []
if table == ['Microbiologyevents']:
table = ['Microbiologyevents', 'd_items']
for extracted_occurence_list in output_extracted_occurence_list:
result_dict={}
result_dict[key_entity] = {
'label': 'No result',
'position_idx': where_idx,
'position': found_line,
'table_cell' : parsed_input
}
for occurrence in extracted_occurence_list:
if any(value != 'NaN' for value in occurrence.values()):
all_nan = False
break
if not all_nan:
generated_questions=generate_formatted_output(extracted_occurence_list,key_entity)
self_correction_prompt = open_file(yaml_cnfig['prompt_files']['self_correction']).replace("<<<<CLINICAL_NOTE>>>>",transformed_texts_list[rid]).replace("<<<Questions>>>",generated_questions).replace("<<<ENTITY>>>",key_entity).replace("<<<Admission>>>",admission).replace("<<<Charttime>>>",charttime)
if model_name == 'llama3':
self_correction_output = open_source_model_inference_llama(self_correction_prompt, model, tokenizer, model_config)
elif model_name == 'chatgpt':
self_correction_output = chatgpt_completion(self_correction_prompt, API_MODEL, OPEN_API_KEY, API_BASE)
else:
self_correction_output = open_source_model_inference(self_correction_prompt, model, tokenizer, model_config)
_self_correction_dict= process_data_with_accurate_parsing(self_correction_output,extracted_occurence_list)
self_correction_dict_=process_dictionary_case_insensitive(_self_correction_dict)
self_correction_dict=correct_nan_values(self_correction_dict_)
self_correction_dict_[key_entity]=self_correction_dict
for key, value in self_correction_dict.items():
if value == 'NaN.':
self_correction_dict[key] = 'NaN'
print()
print("Output of Self Correction: ",self_correction_dict)
print()
all_nan_ = all(value == 'NaN' for value in self_correction_dict.values())
if not all_nan_:
formatted = format_input_for_tables(self_correction_dict,table)
formatted_string = "\n".join(formatted)
format_list=extract_name_item_list(formatted_string)
table_lower = [item.lower() for item in table]
table_candidate = ["chartevents","inputevents_cv","inputevents_mv", "labevents", "microbiologyevents","outputevents","prescriptions","diagnoses_icd","procedures_icd"]
overlap = [item for item in table_candidate if item in table_lower]
overlap=overlap[0]
reformat_prompt_path = yaml_cnfig['prompt_files']['value_reformatting']
reformat_prompt = open_file(f'{reformat_prompt_path}/{overlap}.txt').replace("<<<<GIVEN_DATA>>>>",formatted_string).replace("<<<<Admission>>>>",admission).replace("<<<<Charttime>>>>",charttime)
if model_name == 'llama3':
reformat_output = open_source_model_inference_llama(reformat_prompt, model, tokenizer, model_config)
elif model_name == 'chatgpt':
reformat_output = chatgpt_completion(reformat_prompt, API_MODEL, OPEN_API_KEY, API_BASE)
else:
reformat_output = open_source_model_inference(reformat_prompt, model, tokenizer, model_config)
reformat_output=reformat_output.replace(": ","=")
try:
reformat_samples=extract_matching_items_ignore_case(reformat_output,format_list)
reformat_samples=reformat_samples.replace("[**","").replace("**]","")
reformat_value[key_entity]=reformat_samples
except:
continue
reformat_samples = filter_lines(reformat_samples)
parsed_input=parse_input(reformat_samples)
print("Output of Value Reformatting: ",parsed_input)
print()
if note_types == 'discharge':
sql_number= sql_selection(question3_value,self_correction_dict)
else:
sql_number= sql_selection_not_dis(question3_value,self_correction_dict)
print("Query Type: ",sql_number)
overlap_=overlap.capitalize()
sql_generate_path = yaml_cnfig['prompt_files']['sql_generate']
if sql_number == 'SQL_0': ##no time
if overlap_.lower() == 'procedures_icd':
reformat_samples=remove_specific_lines(reformat_samples)
reformat_samples=f"d_icd_procedures.LONG_TITLE={key_entity}\n d_icd_procedures.SHORT_TITLE={key_entity} "
elif overlap_.lower() == 'diagnoses_icd':
reformat_samples=remove_specific_lines(reformat_samples)
reformat_samples=f"d_icd_diagnoses.LONG_TITLE={key_entity}\n d_icd_diagnoses.SHORT_TITLE={key_entity} "
reformat_samples=remove_specific_lines(reformat_samples)
keys = find_keys_for_tables(table)
if len(keys)>1:
join_keys=" and ".join(keys)
elif len(keys)==1:
join_keys = keys[0]
else:
join_keys = ''
time_value_=find_times_for_tables(table)
time_value=str(time_value_)
if 'Prescriptions' in table or 'prescriptions' in table:
no_time_prompt=open_file(f'{sql_generate_path}/no_time/{overlap}.txt').replace("<<<TIME_VALUES>>>",time_value).replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples).replace("3. The '<<<TABLE_1>>>' and '<<<TABLE_2>>>' tables need to be joined using the '<<<JOIN_KEY>>>' as the key for the join operation.","")
elif 'procedures_icd' in table or 'procedures_icd' in table:
no_time_prompt=open_file(f'{sql_generate_path}/no_time/procedures_icd.txt').replace("<<<TIME_VALUES>>>",time_value).replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples)
elif 'diagnoses_icd' in table or 'Diagnoses_icd' in table:
no_time_prompt=open_file(f'{sql_generate_path}/no_time/diagnoses_icd.txt').replace("<<<TIME_VALUES>>>",time_value).replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples)
else:
no_time_prompt=open_file(f'/home/yskwon/Notable_Check/mimic/openmodels/main/few_shot/discharge/prompt/sql_generate/no_time/{overlap}.txt').replace("<<<TABLE_1>>>",table[0]).replace("<<<TABLE_2>>>",table[1]).replace("<<<JOIN_KEY>>>",join_keys).replace("<<<TIME_VALUES>>>",time_value).replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples)
if model_name == 'llama3':
no_time_output = open_source_model_inference_llama(no_time_prompt, model, tokenizer, model_config)
elif model_name == 'chatgpt':
no_time_output = chatgpt_completion(no_time_prompt, API_MODEL, OPEN_API_KEY, API_BASE)
else:
no_time_output = open_source_model_inference(no_time_prompt, model, tokenizer, model_config)
no_time_output=extract_section_ranges_sql(no_time_output)
no_time_output=no_time_output.replace("[","").replace("]","").replace('"Q"',"'Q'")
try:
query_output_dict[key_entity]=clean_and_eval_query(no_time_output)['Q']
print("query: ", clean_and_eval_query(no_time_output)['Q'])
generated_sqls = total_checker(top_items,predef_map, key_entity, clean_and_eval_query(no_time_output)['Q'], lab_id, drug_list, d_items, lower_label_itemid_dict, d_item_lower_label_itemid_dict, prescriptions_itemid_dict, label_keys, d_item_label_keys, prescription_keys, d_icd_procedures, d_icd_diagnoses, n=2, fir_thr=0.5, sec_thr=0.7)
result_dict[key_entity] = {
'label': 'No result',
'position_idx': where_idx,
'position': found_line,
'table_cell' : parsed_input
}
for query in generated_sqls:
if 'error' == query[1]:
break
try:
cursor.execute(query[1].replace("%y","%Y").replace(":%m",":%M").replace("%h","%H").replace(f"%s",f"%S"))
results = cursor.fetchall()
result_dict[key_entity]['label'] = 'False'
except:
continue
if len(results) > 0:
result_dict[key_entity]['label'] = 'True'
break
else:
continue
except:
print("no_sql_generate")
continue
elif sql_number =='SQL_3':
continue
elif sql_number == 'SQL_1':
keys = find_keys_for_tables(table)
if len(keys)>1:
join_keys=" and ".join(keys)
elif len(keys)==1:
join_keys = keys[0]
else:
join_keys = ''
if 'Prescriptions' in table or 'prescriptions' in table:
exact_time_prompt=open_file(f'{sql_generate_path}/exact_time/{overlap}.txt').replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples).replace("2. The '<<<TABLE_1>>>' and '<<<TABLE_2>>>' tables need to be joined using the '<<<JOIN_KEY>>>' as the key for the join operation.",'')
else:
exact_time_prompt=open_file(f'{sql_generate_path}/exact_time/{overlap}.txt').replace("<<<TABLE_1>>>",table[0]).replace("<<<TABLE_2>>>",table[1]).replace("<<<JOIN_KEY>>>",join_keys).replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples)
if model_name == 'llama3':
exact_time_output = open_source_model_inference_llama(exact_time_prompt, model, tokenizer, config)
elif model_name == 'chatgpt':
exact_time_output = chatgpt_completion(exact_time_prompt, API_MODEL, OPEN_API_KEY, API_BASE)
else:
exact_time_output = open_source_model_inference(exact_time_prompt, model, tokenizer, config)
exact_time_output=extract_section_ranges_sql(exact_time_output)
exact_time_output=exact_time_output.replace("[","").replace("]","").replace('"Q"',"'Q'")
try:
query_output_dict[key_entity]=clean_and_eval_query(exact_time_output)['Q']
print("query: ", clean_and_eval_query(exact_time_output)['Q'])
generated_sqls =total_checker(top_items,predef_map, key_entity, clean_and_eval_query(exact_time_output)['Q'], lab_id, drug_list, d_items, lower_label_itemid_dict, d_item_lower_label_itemid_dict, prescriptions_itemid_dict, label_keys, d_item_label_keys, prescription_keys, d_icd_procedures, d_icd_diagnoses, n=2, fir_thr=0.5, sec_thr=0.7)
result_dict[key_entity] = {
'label': 'No result',
'position_idx': where_idx,
'position': found_line,
'table_cell' : parsed_input
}
for query in generated_sqls:
if 'error' == query[1]:
print("inconsistency: ", query)
break
try:
cursor.execute(query[1].replace("%y","%Y").replace(":%m",":%M").replace("%h","%H").replace(f"%s",f"%S"))
results = cursor.fetchall()
result_dict[key_entity]['label'] = 'False'
except:
continue
if len(results) > 0:
result_dict[key_entity]['label'] = 'True'
break
else:
print("inconsistency: ", query[0])
except:
print("no_sql_generate")
continue
else:
keys = find_keys_for_tables(table)
print("find_table_key?:", keys)
print("check keys: ", keys)
if len(keys)>1:
join_keys=" and ".join(keys)
elif len(keys)==1:
join_keys = keys[0]
else:
join_keys = ''
if ('startdate' in reformat_samples.lower() and 'enddate' in reformat_samples.lower()):
time_key = "Prescriptions.STARTDATE and Prescriptions.ENDDATE"
elif ('starttime' in reformat_samples.lower() and 'endtime' in reformat_samples.lower()):
time_key = "Inputevents_mv.STARTTIME and Inputevents_mv.ENDTIME"
elif ('startdate' in reformat_samples.lower() and not 'enddate' in reformat_samples.lower()):
time_key = "Prescriptions.STARTDATE"
elif ('enddate' in reformat_samples.lower() and not 'startdate' in reformat_samples.lower()):
time_key = "Prescriptions.ENDDATE"
elif ('starttime' in reformat_samples.lower() and not 'endtime' in reformat_samples.lower()):
time_key = "Inputevents_mv.STARTTIME"
elif ('endtime' in reformat_samples.lower() and not 'starttime' in reformat_samples.lower()):
time_key = "Inputevents_mv.ENDTIME"
elif ('charttime' in reformat_samples.lower()):
time_key = f"{overlap_}.CHARTTIME"
if 'Prescriptions' in table or 'prescriptions' in table:
specific_time_prompt=open_file(f'{sql_generate_path}/specific_time/{overlap}.txt').replace("<<<TIME_VALUES>>>",time_key).replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples).replace("3. The '<<<TABLE_1>>>' and '<<<TABLE_2>>>' tables need to be joined using the '<<<JOIN_KEY>>>' as the key for the join operation.","")
else:
specific_time_prompt=open_file(f'{sql_generate_path}/specific_time/{overlap}.txt').replace("<<<TABLE_1>>>",table[0]).replace("<<<TABLE_2>>>",table[1]).replace("<<<JOIN_KEY>>>",join_keys).replace("<<<TIME_VALUES>>>",time_key).replace("<<<ADMISSION>>>",admission).replace("<<<CHARTTIME>>>",charttime).replace("<<<HADM_ID_TABLE>>>",overlap_).replace("<<<HAMD_ID>>>",hadm_id_string).replace("<<<CONDITION_VALUE>>>",reformat_samples)
if model_name == 'llama3':
specific_time_outputt = open_source_model_inference_llama(specific_time_prompt, model, tokenizer, config)
elif model_name == 'chatgpt':
specific_time_outputt = chatgpt_completion(specific_time_prompt, API_MODEL, OPEN_API_KEY, API_BASE)
else:
specific_time_outputt = open_source_model_inference(specific_time_prompt, model, tokenizer, config)
specific_time_outputt=extract_section_ranges_sql(specific_time_outputt)
specific_time_outputt=specific_time_outputt.replace("[","").replace("]","").replace('"Q"',"'Q'")
#print("query_made:", specific_time_outputt)
try:
query_output_dict[key_entity]=clean_and_eval_query(specific_time_outputt)['Q']
print("query: ", clean_and_eval_query(specific_time_outputt)['Q'])
generated_sqls = total_checker(top_items,predef_map, key_entity, clean_and_eval_query(specific_time_outputt)['Q'], lab_id, drug_list, d_items, lower_label_itemid_dict, d_item_lower_label_itemid_dict, prescriptions_itemid_dict, label_keys, d_item_label_keys, prescription_keys, d_icd_procedures, d_icd_diagnoses, n=2, fir_thr=0.5, sec_thr=0.7)
result_dict[key_entity] = {
'label': 'No result',
'position_idx': where_idx,
'position': found_line,
'table_cell' : parsed_input
}
for query in generated_sqls:
if 'error' == query[1]:
print("inconsistency: ", query)
break
try:
cursor.execute(query[1].replace("%y","%Y").replace(":%m",":%M").replace("%h","%H").replace(f"%s",f"%S"))
results = cursor.fetchall()
#result_dict[key_entity] = {'label':'False'}
result_dict[key_entity]['label'] = 'False'
except:
continue
if len(results) > 0:
result_dict[key_entity]['label'] = 'True'
break
else:
print("inconsistency: ", query[0])
except:
continue
else:
print("All values are NaN, moving to the next table.")
continue
#print("result_dict: ",result_dict)
res_list_by_occur.append(result_dict)
print("Output: ",res_list_by_occur)
res_list_by_table.append(res_list_by_occur)
#print("res_list_by_table: ",res_list_by_table)
temp_result = {}
temp_result[key_entity] = {
'label': 'No result',
'position_idx': where_idx,
'position': found_line,
'table_cell' : parsed_input
}
return_list_=processing_before_store_data(res_list_by_table)
print("Final Output: ", return_list_)
print("*********************")
print()
if len(return_list_) > 0:
for temp_result in return_list_:
total_list.append(temp_result)
total_dict[sid]=total_list
with open(f'{result_path_}/results_{hadm_id}.pkl', 'wb') as handle:
pickle.dump(total_dict, handle)
if __name__ == "__main__":
main()