Create README.md

README.md

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