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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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def greet(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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inputs = [ |
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gr.Textbox(label='input'), |
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gr.Textbox(label='output') |
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] |
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examples = [ |
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['[C][=C][C][=C][C][=C][Ring1][=Branch1]', |
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'[C][=C][C][=C][C][=C][Ring1][=Branch1]' |
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'[C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]' |
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'[C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][C][=C]' |
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'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C]' |
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'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C]'] |
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] |
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iface = gr.Interface(fn=greet, inputs="text", outputs="text", inputs=inputs, |
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examples=examples, ) |
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iface.launch() |
