update

app.py

@@ -17,7 +17,7 @@ last_result = {}
 # Function to clear all inputs
 def clear_inputs():
     # Return default or empty values to reset each input component
-    return None, 8, 8, 0
+    return None, 4, 8, 0
 
 def sample(smi, search_width, exhaustiveness):
     result_df = run_sampling_one_cpu(
@@ -45,9 +45,9 @@ def select_from_output(index):
     return df_results.iloc[index]["smiles"], df_results.iloc[index]["score"], df_results.iloc[index]["synthesis"]
 
 examples = [
+    "COc1ccc(-c2ccnc(Nc3ccccc3)n2)cc1",
     "Nc1cccc(S(=O)(=O)N2CCCN(S(=O)(=O)c3ccc4c(c3)OCCO4)CC2)c1",
     "CN1C[C@H](Nc2cnn(C)c(=O)c2)C[C@H](c2ccccc2)C1",
-    "COc1ccc(-c2ccnc(Nc3ccccc3)n2)cc1",
     "CC[C@@H]1OC[C@@]23Cc4cc(F)c(N)cc4-c4ccc5c(c42)C(=CC(F)(F)O5)[C@@H]1C3=O",
     "O=C(OCC(=O)N1[C@H](C(=O)O)C[C@@H]2CCCC[C@@H]21)[C@H](Cc1cbccc1)NC(I)c1bcccc1",
 ]
@@ -57,9 +57,10 @@ with gr.Blocks() as demo:
     gr.Markdown(f"""
         # Demo of [SynFormer](https://github.com/wenhao-gao/synformer/tree/main)
         This page demonstrates the SynFormer-ED model, which takes a molecule as input—regardless of its synthetic accessibility—and outputs 
-                identical or approximate molecules along with their associated synthetic paths. The demo runs on CPUs and typically takes about 
-                one minute per run on local machine, but can be accelerated by reducing the search width and exhaustiveness. The model may take longer if the server 
-                is busy. Since the sampling is stochastic, you may run the demo multiple times to explore different results, with a maximum of 
+                identical or approximate molecules along with their associated synthetic paths. 
+                This demo runs on CPUs and typically takes about one minute per run on local machine, but can be accelerated by reducing the 
+                search width and exhaustiveness. The model may take longer if the server is busy. 
+                Since the sampling is stochastic, you may run the demo multiple times to explore different results, with a maximum of 
                 30 molecules displayed at once.
         To learn more about SynFormer’s architecture and applications, check out [our paper](https://github.com/wenhao-gao/synformer/tree/main).
                 
@@ -68,8 +69,8 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column(scale=0.5):
             input_molecule = molecule2d(label="SMILES Input")
-            slider_1 = gr.Slider(minimum=1, maximum=100, step=1, label="Search Width", value=8)
-            slider_2 = gr.Slider(minimum=1, maximum=100, step=1, label="Exhaustiveness", value=8)
+            slider_1 = gr.Slider(minimum=1, maximum=16, step=1, label="Search Width", value=4)
+            slider_2 = gr.Slider(minimum=1, maximum=32, step=1, label="Exhaustiveness", value=8)
 
             with gr.Row():
                 with gr.Column(scale=0.5):