76 lines
2 KiB
Python
76 lines
2 KiB
Python
import os
|
|
import asyncio
|
|
import openai
|
|
import numpy as np
|
|
import requests
|
|
|
|
from vocode import getenv
|
|
|
|
SIMILARITY_THRESHOLD = 0.9
|
|
EMBEDDING_SIZE = 1536
|
|
GOODBYE_PHRASES = [
|
|
"bye",
|
|
"goodbye",
|
|
"see you",
|
|
"see you later",
|
|
"talk to you later",
|
|
"talk to you soon",
|
|
"have a good day",
|
|
"have a good night",
|
|
]
|
|
|
|
|
|
class GoodbyeModel:
|
|
def __init__(
|
|
self,
|
|
embeddings_cache_path=os.path.join(
|
|
os.path.dirname(__file__), "goodbye_embeddings"
|
|
),
|
|
):
|
|
openai.api_key = getenv("OPENAI_API_KEY")
|
|
self.goodbye_embeddings = self.load_or_create_embeddings(
|
|
f"{embeddings_cache_path}/goodbye_embeddings.npy"
|
|
)
|
|
|
|
def load_or_create_embeddings(self, path):
|
|
if os.path.exists(path):
|
|
return np.load(path)
|
|
else:
|
|
embeddings = self.create_embeddings()
|
|
np.save(path, embeddings)
|
|
return embeddings
|
|
|
|
def create_embeddings(self):
|
|
print("Creating embeddings...")
|
|
size = EMBEDDING_SIZE
|
|
embeddings = np.empty((size, len(GOODBYE_PHRASES)))
|
|
for i, goodbye_phrase in enumerate(GOODBYE_PHRASES):
|
|
embeddings[:, i] = self.create_embedding(goodbye_phrase)
|
|
return embeddings
|
|
|
|
async def is_goodbye(self, text: str) -> bool:
|
|
if "bye" in text.lower():
|
|
return True
|
|
embedding = self.create_embedding(text.strip().lower())
|
|
similarity_results = embedding @ self.goodbye_embeddings
|
|
return np.max(similarity_results) > SIMILARITY_THRESHOLD
|
|
|
|
def create_embedding(self, text) -> np.array:
|
|
return np.array(
|
|
openai.Embedding.create(input=text, model="text-embedding-ada-002")["data"][
|
|
0
|
|
]["embedding"]
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv()
|
|
|
|
async def main():
|
|
model = GoodbyeModel()
|
|
while True:
|
|
print(await model.is_goodbye(input("Text: ")))
|
|
|
|
asyncio.run(main())
|