import gradio as gr

from rdkit.Chem import Draw
from rdkit import Chem
import selfies as sf

sf_input
def greet1(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
    
def greet2(name):
    smis = [sf.decoder(i) for i in sf_output]
    
    mols = []
    for smi in smis:
        mol = Chem.MolFromSmiles(smi)
        mols.append(mol)
    
    img = Draw.MolsToGridImage(
        mols,
        molsPerRow=4,
        subImgSize=(200,200),
        legends=['' for x in mols]
    )
   
    return img




greeter_1 = gr.Interface(greet1, inputs="text", outputs="text")
greeter_2 = gr.Interface(greet2 , inputs="text", outputs="image")
demo = gr.Parallel(greeter_1, greeter_2)
 

demo.launch()
#iface = gr.Interface(fn=greet, inputs="text", outputs="image", title="Molecular Language Model as Multi-task Generator",
#    ) 
#iface.launch()