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import gradio as gr
import torch
import os
from transformers import AutoTokenizer, T5ForConditionalGeneration
model_id = 'ksabeh/gavi'
max_input_length = 512
max_target_length = 10
auth_token = os.environ.get('TOKEN')
model = T5ForConditionalGeneration.from_pretrained(model_id, use_auth_token=auth_token)
tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=auth_token)
def predict(title, category):
input = f"{title} <hl> {category} <hl>"
model_input = tokenizer(input, max_length=max_input_length, truncation=True,
padding="max_length")
model_input = {k:torch.unsqueeze(torch.tensor(v),dim=0) for k,v in model_input.items()}
predictions = model.generate(**model_input, num_beams=8, do_sample=True, max_length=10)
return tokenizer.batch_decode(predictions, skip_special_tokens=True)[0]
iface = gr.Interface(
predict,
inputs=["text", "text"],
outputs=['text'],
title="GAVI",
examples=[["Arriba Salsa Garlic and Cilantro, 16 oz", "Food"],
["MV Verholen Black GPS Ball Mount for BMW K1200S K1200R K1300S K1300R Black GPS Ball Mount VER-4901-10181", "Toys"],
["Mitsubishi 3000GT License Plate Frame (Zince Metal)", "Automotive"],
["Fun Fire Truck Pinata Personalized", "Toys"],
["White Chocolate Caramel Gourmet Popcorn Kelly", "Food"]
]
)
iface.launch() |