File size: 2,135 Bytes
b185968 962784e 607469a 962784e 607469a 962784e 607469a 962784e 607469a 962784e 607469a |
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 |
import gradio as gr
import torch
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
# Load the model
revision = None # Replace with the specific revision to ensure reproducibility if the model is updated.
model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision)
# Precompute reference embeddings
ref_texts = [
"Theatro App: Hello John. Hey John. Hi John. Call John",
"Theatro App: Message John. Message for John. Leave a message for John",
"Theatro App: Play messages. Listen to messages",
"Theatro App: What time is it?",
"Theatro App: What time is it?",
"Theatro App: Cashier Backup. Backup Cashier. Register backup. Register assistance.",
"Theatro App: repeat",
"Theatro App: Check inventory",
"Theatro App: Check Sales",
"Theatro App: Curbside Pickup",
"Theatro App: Replay last message.",
"Theatro App: Post it. Post it for group"
"Theatro App: Announcement. Announcement for the group",
"Open question: This is about products sold in TractorSupply.",
"Open question: This is about pet care.",
"Open question: What is the weather like?",
"Open question: What's 15% off from $79.99?",
"Open question: Can you look up the skew for 1091784?",
]
ref_embeddings = model.encode(ref_texts, convert_to_tensor=True)
def find_query_type(query):
query_embeddings = model.encode([query], convert_to_tensor=True)
scores = F.cosine_similarity(query_embeddings, ref_embeddings, dim=-1)
max_index = torch.argmax(scores).item()
ref_text = ref_texts[max_index]
query_type = ref_text.split(": ")[0]
return query_type
import gradio as gr
def predict(query):
query_type = find_query_type(query)
return query_type
iface = gr.Interface(fn=predict,
inputs=gr.Textbox(lines=2, placeholder="Enter your query here..."),
outputs="text",
title="Query Type Classifier",
description="This model classifies the type of your query. Just input your query and get the predicted category.")
iface.launch()
|