Update app.py

app.py

@@ -221,6 +221,10 @@ MODEL_OPTIONS = [
     "facebook/esm2_t33_650M_UR50D",
 ]  # models users can choose from
 
+PEFT_MODEL_OPTIONS = [
+    "AmelieSchreiber/esm2_t12_35M_lora_binding_sites_v2_cp3",
+]  # finetuned models 
+
 
 # Load the data from pickle files (replace with your local paths)
 with open("./datasets/train_sequences_chunked_by_family.pkl", "rb") as f:
@@ -309,33 +313,35 @@ with demo:
     gr.Markdown("# DEMO FOR ESM2Bind")
     #gr.Textbox(dubug_result)
     
+        
     with gr.Tab("Finetune Pre-trained Model"):
-        gr.Markdown("## Finetune Pre-trained Model")
-        with gr.Column():
-            gr.Markdown("## Load Inputs & Select Parameters")
-            gr.Markdown(
-                """ Pick a base model and press **Finetune Pre-trained Model!"""
-            )
-            with gr.Row():   
-                with gr.Column(scale=0.5, variant="compact"):
-                    base_model_name = gr.Dropdown(
-                        choices=MODEL_OPTIONS,
-                        value=MODEL_OPTIONS[0],
-                        label="Base Model Name",
-                        interactive = True,
-                    )   
-                    finetune_button = gr.Button(
-                        value="Finetune Pre-trained Model", 
-                        interactive=True, 
-                        variant="primary",
-                    )
-                    finetune_output_text = gr.Textbox(
-                        lines=1,
-                        max_lines=12,
-                        label="Finetune Status",
-                        placeholder="Finetune Status Shown Here",
-                    )    
-    # Tab "Finetune Pre-trained Model" actions
+    gr.Markdown("## Finetune Pre-trained Model")
+    with gr.Column():
+        gr.Markdown("## Select a base model")
+        gr.Markdown(
+            """ Pick a base model and press **Finetune Pre-trained Model!"""
+        )
+        with gr.Row():   
+            with gr.Column(scale=0.5, variant="compact"):
+                base_model_name = gr.Dropdown(
+                    choices=MODEL_OPTIONS,
+                    value=MODEL_OPTIONS[0],
+                    label="Base Model Name",
+                    interactive = True,
+                )   
+                finetune_button = gr.Button(
+                    value="Finetune Pre-trained Model", 
+                    interactive=True, 
+                    variant="primary",
+                )
+                finetune_output_text = gr.Textbox(
+                    lines=1,
+                    max_lines=12,
+                    label="Finetune Status",
+                    placeholder="Finetune Status Shown Here",
+                )               
+                    
+    # "Finetune Pre-trained Model" actions
     finetune_button.click(
         fn = train_function_no_sweeps,
         inputs=[base_model_name], #finetune_dataset_name],