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import re
from langchain_openai import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
import os
OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
def get_api_response(content: str, max_tokens=None):
response = None
chat = ChatOpenAI(
openai_api_key=OPENAI_API_KEY,
#model='gpt-3',
#model='gpt-3.5-turbo',
#model='gpt-3.5-turbo-0613',
#model='gpt-3.5-turbo-16k',
model='gpt-3.5-turbo-16k-0613',
#openai_proxy=OPENAI_Proxy,
#model='gpt-4',
#model='gpt-4-0613',
#model='gpt-4-32k-0613',
temperature=0.1)
# response = openai.ChatCompletion.create(
# model='gpt-3.5-turbo-16k-0613',
# messages=[{
# 'role': 'system',
# 'content': 'You are a helpful and creative assistant for writing novel.'
# }, {
# 'role': 'user',
# 'content': content,
# }],
# temperature=0.5,
# max_tokens=max_tokens
# )
# response = None
messages = [
SystemMessage(content="You are a helpful and creative assistant for writing novel."),
HumanMessage(content=content)
]
try:
response = chat(messages)
except:
raise Exception("OpenAI Error")
if response is not None:
return response.content
else:
return "Error: response not found"
def get_content_between_a_b(a,b,text):
return re.search(f"{a}(.*?)\n{b}", text, re.DOTALL).group(1).strip()
def get_init(init_text=None,text=None,response_file=None):
"""
init_text: if the title, outline, and the first 3 paragraphs are given in a .txt file, directly read
text: if no .txt file is given, use init prompt to generate
"""
if not init_text:
response = get_api_response(text)
print(response)
if response_file:
with open(response_file, 'a', encoding='utf-8') as f:
f.write(f"Init output here:\n{response}\n\n")
else:
with open(init_text,'r',encoding='utf-8') as f:
response = f.read()
f.close()
paragraphs = {
"name":"",
"Outline":"",
"Paragraph 1":"",
"Paragraph 2":"",
"Paragraph 3":"",
"Summary": "",
"Instruction 1":"",
"Instruction 2":"",
"Instruction 3":""
}
paragraphs['name'] = get_content_between_a_b('Name:','Outline',response)
paragraphs['Paragraph 1'] = get_content_between_a_b('Paragraph 1:','Paragraph 2:',response)
paragraphs['Paragraph 2'] = get_content_between_a_b('Paragraph 2:','Paragraph 3:',response)
paragraphs['Paragraph 3'] = get_content_between_a_b('Paragraph 3:','Summary',response)
paragraphs['Summary'] = get_content_between_a_b('Summary:','Instruction 1',response)
paragraphs['Instruction 1'] = get_content_between_a_b('Instruction 1:','Instruction 2',response)
paragraphs['Instruction 2'] = get_content_between_a_b('Instruction 2:','Instruction 3',response)
lines = response.splitlines()
# content of Instruction 3 may be in the same line with I3 or in the next line
if lines[-1] != '\n' and lines[-1].startswith('Instruction 3'):
paragraphs['Instruction 3'] = lines[-1][len("Instruction 3:"):]
elif lines[-1] != '\n':
paragraphs['Instruction 3'] = lines[-1]
# Sometimes it gives Chapter outline, sometimes it doesn't
for line in lines:
if line.startswith('Chapter'):
paragraphs['Outline'] = get_content_between_a_b('Outline:','Chapter',response)
break
if paragraphs['Outline'] == '':
paragraphs['Outline'] = get_content_between_a_b('Outline:','Paragraph',response)
return paragraphs
def get_chatgpt_response(model,prompt):
response = ""
for data in model.ask(prompt):
response = data["message"]
model.delete_conversation(model.conversation_id)
model.reset_chat()
return response
def parse_instructions(instructions):
output = ""
for i in range(len(instructions)):
output += f"{i+1}. {instructions[i]}\n"
return output |