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AURA_functions.py
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AURA_functions.py
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import csv
import openai
from gingerit.gingerit import GingerIt
import os
import pandas
# Specify data sources
PI_data = 'PI_data.csv'
baseline_database = 'baseline_database.csv'
def read_key():
with open('key.txt') as file:
key = file.read()
return key
def send_prompt_davinci(prompt, key):
openai.api_key = key
model_engine = "text-davinci-003"
completion = openai.Completion.create(
engine=model_engine,
prompt=prompt,
max_tokens=1024,
n=1,
stop=None,
temperature=0.5
)
response = completion.choices[0].text
# print("\nPrompt to OpenAI server: " + prompt)
print(response)
def send_prompt_gpt35turbo_ref(conversation, key):
import openai
openai.api_key = key
model_id = 'gpt-3.5-turbo'
def ChatGPT_conversation(conversation):
response = openai.ChatCompletion.create(
model=model_id,
messages=conversation
)
# api_usage = response['usage']
# print('Total token consumed: {0}'.format(api_usage['total_tokens']))
# stop means complete
# print(response['choices'][0].finish_reason)
# print(response['choices'][0].index)
conversation.append({'role': response.choices[0].message.role, 'content': response.choices[0].message.content})
return conversation
conversation = []
conversation.append({'role': 'system', 'content': 'How may I help you?'})
conversation = ChatGPT_conversation(conversation)
print('{0}: {1}\n'.format(conversation[-1]['role'].strip(), conversation[-1]['content'].strip()))
while True:
prompt = input('User:')
conversation.append({'role': 'user', 'content': prompt})
conversation = ChatGPT_conversation(conversation)
print('{0}: {1}\n'.format(conversation[-1]['role'].strip(), conversation[-1]['content'].strip()))
def send_prompt_gpt35turbo(prompt, key):
# import openai
openai.api_key = key
model_id = 'gpt-3.5-turbo'
def ChatGPT_conversation(conversation):
response = openai.ChatCompletion.create(
model=model_id,
messages=conversation
)
# api_usage = response['usage']
# print('Total token consumed: {0}'.format(api_usage['total_tokens']))
# stop means complete
# print(response['choices'][0].finish_reason)
# print(response['choices'][0].index)
conversation.append({'role': response.choices[0].message.role, 'content': response.choices[0].message.content})
return conversation
conversation = []
conversation.append({'role': 'system', 'content': prompt})
conversation = ChatGPT_conversation(conversation)
print('{0}: {1}\n'.format(conversation[-1]['role'].strip(), conversation[-1]['content'].strip()))
def extract_equipment():
with open(PI_data) as file:
input_data = csv.DictReader(file) # Read PI data
equipment_list = []
# Generate list of equipment
for row in input_data:
equipment = row['equipment']
if equipment not in equipment_list:
equipment_list.append(equipment)
return equipment_list
def generate_abnormalities_sentence(abnormality_list, abnormality_count, prompt):
if abnormality_count == 1: # If only 1 issue detected
prompt = prompt + ' ' + abnormality_list[0]
elif abnormality_count == 2: # If 2 issues detected
prompt = prompt + ' ' + abnormality_list[0] + ' and ' + abnormality_list[1]
else: # If more than 2 issues detected
for issues in abnormality_list:
if issues == abnormality_list[-1]: # When reached the end of the list
prompt = prompt + ' and ' + issues
elif issues == abnormality_list[0]: # For the first issue in the list
prompt = prompt + ' ' + issues
else:
prompt = prompt + ', ' + issues
return prompt
# Input actual data for an equipment and output result whether the actual data is high, low or normal
def determine_attribute_status(equipment, attribute, actual_data):
# set default attribute status as normal
attribute_status = 'normal'
with open(baseline_database) as file:
database = csv.DictReader(file) # Generate dictionary from csv file
for row in database:
if equipment == row['equipment'] and attribute == row['attribute']:
if actual_data < int(row['baseline_min']):
attribute_status = 'low'
elif actual_data > int(row['baseline_max']):
attribute_status = 'high'
return attribute_status
def grammar_correction(prompt):
parser = GingerIt()
return parser.parse(prompt)
def generate_single_prompt(equipment):
# initialize abnormality count to 0
abnormality_count = 0
with open(PI_data) as file:
input_data = csv.DictReader(file) # Read PI data
abnormality_list = []
# equipment_list = extract_equipment()
# Initialize prompt
prompt = f'List possible causes and solutions for power plant {equipment} experiencing'
for row in input_data:
attribute = row['attribute']
actual_data = int(row['actual_data'])
status = determine_attribute_status(equipment, attribute, actual_data)
if status != 'normal':
abnormality_list.append(f'{status} {attribute}')
abnormality_count = abnormality_count + 1
# combine initialized prompt with discovered issue(s)
return generate_abnormalities_sentence(abnormality_list, abnormality_count, prompt)
def generate_multi_prompt():
prompt_list = []
equipment_list = extract_equipment();
# print(equipment_list)
for equipment in equipment_list:
prompt = generate_single_prompt(equipment)
prompt_list.append(prompt)
return prompt_list