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from statistics import mean
import random
import torch
from transformers import BertModel, BertTokenizerFast
import numpy as np
import torch.nn.functional as F
import gradio as gr
threshold = 0.4
tokenizer = BertTokenizerFast.from_pretrained("setu4993/LaBSE")
model = BertModel.from_pretrained("setu4993/LaBSE")
model = model.eval()
order_food_ex = [
"food",
"I am hungry, I want to order food",
"How do I order food",
"What are the food options",
"I need dinner",
"I want lunch",
"What are the menu options",
"I want a hamburger"
]
talk_to_human_ex = [
"I need to talk to someone",
"Connect me with a human",
"I need to speak with a person",
"Put me on with a human",
"Connect me with customer service",
"human"
]
def embed(text, tokenizer, model):
inputs = tokenizer(text, return_tensors="pt", padding=True)
with torch.no_grad():
outputs = model(**inputs)
return outputs.pooler_output
def similarity(embeddings_1, embeddings_2):
normalized_embeddings_1 = F.normalize(embeddings_1, p=2)
normalized_embeddings_2 = F.normalize(embeddings_2, p=2)
return torch.matmul(
normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1)
)
order_food_embed = [embed(x, tokenizer, model) for x in order_food_ex]
talk_to_human_embed = [embed(x, tokenizer, model) for x in talk_to_human_ex]
def chat(message, history):
history = history or []
message_embed = embed(message, tokenizer, model)
order_sim = []
for em in order_food_embed:
order_sim.append(float(similarity(em, message_embed)))
human_sim = []
for em in talk_to_human_embed:
human_sim.append(float(similarity(em, message_embed)))
if mean(order_sim) > threshold:
response = random.choice([
"We have hamburgers or pizza! Which one do you want?",
"Do you want a hamburger or a pizza?"])
elif mean(human_sim) > threshold:
response = random.choice([
"Sure, a customer service agent will jump into this convo shortly!",
"No problem. Let me forward on this conversation to a person that can respond."])
else:
response = "Sorry, I didn't catch that. Could your rephrase?"
history.append((message, response))
return history, history
iface = gr.Interface(
chat,
["text", "state"],
["chatbot", "state"],
allow_screenshot=False,
allow_flagging="never",
)
iface.launch()
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