Update app.py

app.py

@@ -12,15 +12,6 @@ is_stopped = False
 seed = random.randint(0,100000)
 setup_seed(seed)
 
-device = torch.device("cpu")
-vocab_mlm = create_vocab()
-vocab_mlm = add_tokens_to_vocab(vocab_mlm)
-save_path = 'mlm-model-27.pt'
-train_seqs = pd.read_csv('C0_seq.csv')
-train_seq = train_seqs['Seq'].tolist()
-model = torch.load(save_path, map_location=torch.device('cpu'))
-model = model.to(device)
-
 def temperature_sampling(logits, temperature):
     logits = logits / temperature
     probabilities = torch.softmax(logits, dim=-1)
@@ -32,7 +23,16 @@ def stop_generation():
     is_stopped = True
     return "Generation stopped."
 
-def CTXGen(X0, X1, X2, τ, g_num):
+def CTXGen(X0, X1, X2, τ, g_num, model_name):
+    device = torch.device("cpu")
+    vocab_mlm = create_vocab()
+    vocab_mlm = add_tokens_to_vocab(vocab_mlm)
+    save_path = model_name
+    train_seqs = pd.read_csv('C0_seq.csv')
+    train_seq = train_seqs['Seq'].tolist()
+    model = torch.load(save_path, map_location=torch.device('cpu'))
+    model = model.to(device)
+    
     global is_stopped
     is_stopped = False
     X3 = "X" * len(X0)
@@ -178,6 +178,7 @@ with gr.Blocks() as demo:
                                   '<α6α3β2β3>', '<α1β1δ>', '<α6α3β4β3>', '<α2β2>','<α6β4>', '<α2β4>','<Na13>', '<Na15>', '<α4β4>', 
                                   '<α7α6β2>', '<α1β1γ>', '<NaTTXR>', '<K11>', '<Ca23>', '<α6α3β4>', '<NaTTXS>', '<Na17>'], label="Subtype")
         X2 = gr.Dropdown(choices=['<high>','<low>'], label="Potency")
+        model_name = gr.Dropdown(choices=['model_final','model_C1','model_C2','model_C3','model_C4','model_C5','model_mlm'], label="Model")
         τ = gr.Slider(minimum=1, maximum=2, step=0.1, label="τ")
         g_num = gr.Dropdown(choices=[1, 10, 20, 30, 40, 50], label="Number of generations")
     with gr.Row():
@@ -188,7 +189,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         output_file = gr.File(label="Download generated conotoxins")
     
-    start_button.click(CTXGen, inputs=[X0, X1, X2, τ, g_num], outputs=[output_df, output_file])
+    start_button.click(CTXGen, inputs=[X0, X1, X2, τ, g_num, model_name], outputs=[output_df, output_file])
     stop_button.click(stop_generation, outputs=None)
 
 demo.launch()