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import pandas as pd
import streamlit as st
from streamlit.logger import get_logger
import os
import requests
from app_config import ENDPOINT_NAMES
from utils.memory_utils import change_memories
from models.model_seeds import seeds
logger = get_logger(__name__)
# TODO: Include more variable and representative names
DEFAULT_NAMES = ["Olivia", "Kit", "Abby", "Tom", "Carolyne", "Jessiny"]
DEFAULT_NAMES_DF = pd.read_csv("./utils/names.csv")
HEADERS = {
"Authorization": f"Bearer {os.environ['DATABRICKS_TOKEN']}",
"Content-Type": "application/json",
}
def get_random_name(gender="Neutral", ethnical_group="Neutral", names_df=None):
if names_df is None:
names_df = pd.DataFrame(DEFAULT_NAMES, columns=['name'])
names_df["gender"] = "Neutral"
names_df["ethnical_group"] = "Neutral"
dfi = names_df
if gender != "Neutral":
dfi = dfi.query(f"gender=='{gender}'")
if ethnical_group != "Neutral":
dfi = dfi.query(f"ethnical_group=='{ethnical_group}'")
if len(dfi) <=0 :
dfi = names_df
return dfi.sample(1)['name'].values[0]
def divide_messages(str_memory, str_ai_prefix="texter", str_human_prefix="helper", include_colon=True):
message_delimiter = "$%$"
# Split str memory in messaages according to previous prefix and flatten list
colon = ":" if include_colon else ""
str_memory = f"{message_delimiter}{str_ai_prefix}{colon}".join(str_memory.split(f"{str_ai_prefix}{colon}"))
str_memory = f"{message_delimiter}{str_human_prefix}{colon}".join(str_memory.split(f"{str_human_prefix}{colon}"))
return str_memory.split(message_delimiter)
def add_initial_message(issue, language, memory, str_ai_prefix="texter", str_human_prefix="helper", include_colon=True,
texter_name="", counselor_name=""):
initial_mem_str = seeds.get(issue, "GCT")['memory'].format(counselor_name=counselor_name, texter_name=texter_name)
message_list = divide_messages(initial_mem_str, str_ai_prefix, str_human_prefix, include_colon)
colon = ":" if include_colon else ""
for i, message in enumerate(message_list):
message = message.strip("\n")
message = message.strip()
if message is None or message == "":
pass
elif message.startswith(str_human_prefix):
memory.chat_memory.add_user_message(message.lstrip(f"{str_human_prefix}{colon}").strip())
elif message.startswith(str_ai_prefix):
memory.chat_memory.add_ai_message(message.lstrip(f"{str_ai_prefix}{colon}").strip())
def create_memory_add_initial_message(memories, issue, language, changed_source=False, texter_name="", counselor_name=""):
change_memories(memories, language, changed_source=changed_source)
for memory, _ in memories.items():
if len(st.session_state[memory].buffer_as_messages) < 1:
add_initial_message(issue, language, st.session_state[memory], texter_name=texter_name, counselor_name=counselor_name)
def is_model_alive(name, timeout=2, model_type="classificator"):
if model_type!="openai":
endpoint_url=os.environ['DATABRICKS_URL'].format(endpoint_name=name)
headers = HEADERS
if model_type == "classificator":
body_request = {
"inputs": [""]
}
elif model_type == "text-completion":
body_request = {
"prompt": "",
"temperature": 0,
"max_tokens": 1,
}
elif model_type == "text-generation":
body_request = {
"messages": [{"role":"user","content":""}],
"max_tokens": 1,
"temperature": 0
}
else:
raise Exception(f"Model Type {model_type} not supported")
try:
response = requests.post(url=endpoint_url, headers=HEADERS, json=body_request, timeout=timeout)
return str(response.status_code)
except:
return "404"
else:
endpoint_url="https://api.openai.com/v1/models"
headers = {"Authorization": f"Bearer {os.environ['OPENAI_API_KEY']}",}
try:
response = requests.get(url=endpoint_url, headers=headers, timeout=1)
return str(response.status_code)
except:
return "404"
@st.cache_data(ttl=300, show_spinner=False)
def are_models_alive():
models_alive = []
for config in ENDPOINT_NAMES.values():
models_alive.append(is_model_alive(**config))
openai = is_model_alive("openai", model_type="openai")
models_alive.append(openai)
return all([x=="200" for x in models_alive]) |