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--- |
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license: mit |
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datasets: |
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- lisn519010/QM9 |
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language: |
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- en |
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- zh |
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metrics: |
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- mae |
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- accuracy |
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- r_squared |
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- mse |
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pipeline_tag: graph-ml |
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--- |
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|
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``` |
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pip install transformers gradio rdkit torch torch_scatter torch_sparse torch_geometric |
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``` |
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|
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```python |
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import gradio as gr |
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from transformers import AutoModel |
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|
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def predict_smiles(name): |
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device = 'cpu' |
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smiles = name |
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assert isinstance(smiles, str), 'smiles must be str' |
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|
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smiles = smiles.strip() |
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if ';' in smiles: |
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smiles = smiles.split(";") |
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elif ' ' in smiles: |
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smiles = smiles.split(" ") |
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elif ',' in smiles: |
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smiles = smiles.split(",") |
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else: |
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smiles = [smiles] |
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|
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model = AutoModel.from_pretrained("Huhujingjing/custom-mxm", trust_remote_code=True).to(device) |
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|
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output, df = model.predict_smiles(smiles) |
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|
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return output, df |
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|
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iface = gr.Interface(fn=predict_smiles, inputs="text", outputs=["text", "dataframe"]) |
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iface.launch(share=True) |
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``` |
