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()