Spaces:
Running
Running
File size: 1,744 Bytes
5832f57 0f381ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
import logging
from models.custom_parsers import CustomStringOutputParser
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
_ANXIETY_TEMPLATE_ = """The following is a friendly conversation between a volunter and a person in crisis;
Your name is Olivia and you are a person in crisis because you are experiencing an anxiety attack.
Do not disclose your name unless asked.
Current conversation:
{history}
helper: {input}
texter:"""
_ANSIEDAD_TEMPLATE_ = """La siguiente es una conversacion entre un voluntario y una persona en crisis;
Tu nombre es Olivia y eres una persona en crisis porque experimentas un episodio de ansiedad.
No reveles tu nombre a menos que te lo pregunten
Current conversation:
{history}
helper: {input}
texter:"""
_SUICIDE_TEMPLATE_ = """The following is a friendly conversation between a volunter and a person in crisis;
Your name is Kit and you are a person in crisis because you are experiencing suicidal thoughts.
Do not disclose your name unless asked.
Current conversation:
{history}
helper: {input}
texter:"""
role_templates = {
'Anxiety-English': _ANXIETY_TEMPLATE_,
'Anxiety-Spanish': _ANSIEDAD_TEMPLATE_,
'Suicide-English': _SUICIDE_TEMPLATE_,
}
def get_role_chain(template, memory, temperature=0.8):
PROMPT = PromptTemplate(
input_variables=['history', 'input'],
template=template
)
llm = OpenAI(
temperature=temperature,
max_tokens=150,
)
llm_chain = ConversationChain(
llm=llm,
prompt=PROMPT,
memory=memory,
output_parser=CustomStringOutputParser()
)
logging.debug(f"loaded GPT3.5 model")
return llm_chain, "helper:" |