Update app.py

app.py

@@ -1,30 +1,30 @@
-import numpy as np # linear algebra
-import pandas as pd
-import torch
-from molscribe import MolScribe
-from huggingface_hub import hf_hub_download
-import gradio as gr
-from rdkit import Chem
-from PIL import Image
-import io
-
-ckpt_path = hf_hub_download('yujieq/MolScribe', 'swin_base_char_aux_1m.pth')
-model = MolScribe(ckpt_path, device=torch.device('cpu'))
-
-def make_smiles(img):
-  '''
-  '''
-  output = model.predict_image_file(img, return_atoms_bonds=True, return_confidence=True)
-  mol = Chem.MolFromSmiles(output['smiles'])
-  new_img_raw = Chem.Draw.MolsToGridImage([mol], molsPerRow=1, legends=[output['smiles']])
-  new_img = Image.open(io.BytesIO(new_img_raw.data))
-  return output['smiles'], new_img
-
-demo = gr.Interface(
-    fn = make_smiles,
-    inputs=gr.Image(type="filepath"), 
-    outputs=[gr.Textbox(lines=2, label="SMILES"), gr.Image(label="New Image")],
-    title="MolScribe",
-)
-    
-demo.launch(mcp=True)
+import numpy as np # linear algebra
+import pandas as pd
+import torch
+from molscribe import MolScribe
+from huggingface_hub import hf_hub_download
+import gradio as gr
+from rdkit import Chem
+from PIL import Image
+import io
+
+ckpt_path = hf_hub_download('yujieq/MolScribe', 'swin_base_char_aux_1m.pth')
+model = MolScribe(ckpt_path, device=torch.device('cpu'))
+
+def make_smiles(img):
+  '''
+  '''
+  output = model.predict_image_file(img, return_atoms_bonds=True, return_confidence=True)
+  mol = Chem.MolFromSmiles(output['smiles'])
+  new_img_raw = Chem.Draw.MolsToGridImage([mol], molsPerRow=1, legends=[output['smiles']])
+  new_img = Image.open(io.BytesIO(new_img_raw.data))
+  return output['smiles'], new_img
+
+demo = gr.Interface(
+    fn = make_smiles,
+    inputs=gr.Image(type="filepath"), 
+    outputs=[gr.Textbox(lines=2, label="SMILES"), gr.Image(label="New Image")],
+    title="MolScribe",
+)
+    
+demo.launch(mcp_server=True)