link to gpu model

.gitignore

@@ -0,0 +1,2 @@
+env/*
+__pycache__/*

app.py

@@ -49,7 +49,6 @@ def predict_single_label(smiles, property_name):
             return "NA", running_status
     
         #prediction = model.predict(smiles, property_name, adapter_id)
-        print("hello4")
         prediction = model.predict_single_smiles(smiles, dataset_task_types[property_id])
         if prediction is None:
             return "NA", "Invalid SMILES string"
@@ -165,6 +164,7 @@ def build_inference():
         #with gr.Row():
         print(property_names[0].lower())
         print(properties)
+        gr.Markdown(f"<span style='color: red;'>This space runs on a CPU, so predictions may take over 20 seconds. For faster performance, you can use the </span> <a href='https://huggingface.co/spaces/ChemFM/molecular_property_prediction_zero_gpu'>GPU-powered space</a>.")
         dropdown = gr.Dropdown(properties, label="Property", value=dataset_property_names[property_names[0].lower()])
         description_box = gr.Textbox(label="Property description", lines=5,
                                      interactive=False,

utils.py

@@ -266,7 +266,6 @@ class MolecularPropertyPredictionModel():
                 # handle error
                 return "error"
     
-    @spaces.GPU(duration=20)
     def predict(self, valid_df, task_type):
 
         with calculateDuration("predicting"):