File size: 1,049 Bytes
5a9a1ef f1b26cb 5a9a1ef bf91928 f1b26cb 7b3398a f1b26cb 81c11f2 2b734c6 f1b26cb 2b734c6 9d0f6a3 2b734c6 5a9a1ef 331c1b3 b0151d2 059e909 13b5e43 b0151d2 059e909 13b5e43 bf91928 |
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 |
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
def greet(name):
tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen")
model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen")
sf_input = tokenizer(name, return_tensors="pt")
# beam search
molecules = model.generate(input_ids=sf_input["input_ids"],
attention_mask=sf_input["attention_mask"],
max_length=15,
min_length=5,
num_return_sequences=5,
num_beams=5)
sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
return sf_output
examples = [
['[C][=C][C][=C][C][=C][Ring1][=Branch1]'],['[C]']
]
iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Molecular Language Model as Multi-task Generator",
examples=examples, )
iface.launch() |