/
app.py
25 lines (22 loc) · 946 Bytes
/
app.py
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
import gradio as gr
import torch
from transformers import AutoTokenizer, EncoderDecoderModel
tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/es-seq2seq-gender-encoder", model_max_length=256)
model = EncoderDecoderModel.from_encoder_decoder_pretrained(
"monsoon-nlp/es-seq2seq-gender-encoder",
"monsoon-nlp/es-seq2seq-gender-decoder",
max_length=40,
)
def flip(content):
input_ids = torch.tensor(tokenizer.encode(content)).unsqueeze(0)
generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
op = tokenizer.decode(generated.tolist()[0][1:])
if '[SEP]' in op:
return op[:op.index('[SEP]')]
return op
iface = gr.Interface(fn=flip,
inputs=gr.inputs.Textbox(label="Original Spanish text"),
outputs=gr.outputs.Textbox(label="Flipped"),
description="seq2seq built from BETO model - see https://huggingface.co/monsoon-nlp/es-seq2seq-gender-encoder",
)
iface.launch()