Update app.py

app.py

@@ -57,8 +57,10 @@ def load_model(custom_model):
 
     print(f"Safetensors available: {sfts_available_files}")
 
-    return gr.update(choices=sfts_available_files, value=sfts_available_files[0], visible=True), gr.update(value=instance_prompt, visible=True)
+    return "Model Ready", gr.update(choices=sfts_available_files, value=sfts_available_files[0], visible=True), gr.update(value=instance_prompt, visible=True)
 
+def custom_model_changed(custom_model):
+    return "Model changed, you must reload before re-run"
 
 @spaces.GPU
 def infer (custom_model, weight_name, prompt, inf_steps, guidance_scale, seed, lora_weight, progress=gr.Progress(track_tqdm=True)):
@@ -187,52 +189,67 @@ with gr.Blocks(css=css) as demo:
                 )
             with gr.Column():
                 load_model_btn = gr.Button("Load my model")
+                model_status = gr.Textbox(
+                    label = "model status",
+                    show_label = False
+                )
                 trigger_word = gr.Textbox(label="Trigger word", interactive=False, visible=False)
         
-        prompt_in = gr.Textbox(label="Prompt")   
-        with gr.Row():
-            inf_steps = gr.Slider(
-                label="Inference steps",
-                minimum=12,
-                maximum=50,
-                step=1,
-                value=25
-            )
-            guidance_scale = gr.Slider(
-                label="Guidance scale",
-                minimum=0.0,
-                maximum=50.0,
-                step=0.1,
-                value=7.5
-            )
+        prompt_in = gr.Textbox(
+            label="Your Prompt",
+            info = "Dont' forget to include your trigger word if necessary"
+        )   
 
-        with gr.Row():
-            seed = gr.Slider(
-                label="Seed",
-                info = "-1 denotes a random seed",
-                minimum=-1,
-                maximum=423538377342,
-                step=1,
-                value=-1
-            )
-            last_used_seed = gr.Number(
-                label = "Last used seed",
-                info = "the seed used in the last generation",
+        with gr.Accordion("Advanced Settings", open=False):
+            with gr.Row():
+                inf_steps = gr.Slider(
+                    label="Inference steps",
+                    minimum=12,
+                    maximum=50,
+                    step=1,
+                    value=25
+                )
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=50.0,
+                    step=0.1,
+                    value=7.5
+                )
+    
+            with gr.Row():
+                seed = gr.Slider(
+                    label="Seed",
+                    info = "-1 denotes a random seed",
+                    minimum=-1,
+                    maximum=423538377342,
+                    step=1,
+                    value=-1
+                )
+                last_used_seed = gr.Number(
+                    label = "Last used seed",
+                    info = "the seed used in the last generation",
+                )
+            lora_weight = gr.Slider(
+                label="LoRa weigth",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.9
             )
-        lora_weight = gr.Slider(
-            label="LoRa weigth",
-            minimum=0.0,
-            maximum=1.0,
-            step=0.01,
-            value=0.9
-        )
         submit_btn = gr.Button("Submit")
         image_out = gr.Image(label="Image output")
 
+    custom_model.blur(
+        fn=custom_model_changed,
+        inputs = [custom_model],
+        outputs = [model_status],
+        queue = False
+    )
     load_model_btn.click(
         fn = load_model,
         inputs=[custom_model],
-        outputs = [weight_name, trigger_word],
+        outputs = [model_status, weight_name, trigger_word],
         queue = False
     )
     submit_btn.click(