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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() |