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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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from rdkit.Chem import Draw |
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from rdkit import Chem |
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import selfies as sf |
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sf_input |
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def greet1(name): |
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tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen") |
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model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen") |
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sf_input = tokenizer(name, return_tensors="pt") |
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molecules = model.generate(input_ids=sf_input["input_ids"], |
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attention_mask=sf_input["attention_mask"], |
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max_length=15, |
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min_length=5, |
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num_return_sequences=5, |
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num_beams=5) |
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sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules] |
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return sf_output |
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def greet2(name): |
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smis = [sf.decoder(i) for i in sf_output] |
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mols = [] |
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for smi in smis: |
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mol = Chem.MolFromSmiles(smi) |
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mols.append(mol) |
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img = Draw.MolsToGridImage( |
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mols, |
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molsPerRow=4, |
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subImgSize=(200,200), |
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legends=['' for x in mols] |
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) |
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return img |
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greeter_1 = gr.Interface(greet1, inputs="text", outputs="text") |
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greeter_2 = gr.Interface(greet2 , inputs="text", outputs="image") |
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demo = gr.Parallel(greeter_1, greeter_2) |
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demo.launch() |
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