Update app.py

app.py

@@ -15,25 +15,23 @@ base_model = "stabilityai/stable-diffusion-xl-base-1.0"
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16)
 pipe.to("cuda")
 
-def update_selection(selected_state: gr.SelectData):
-    selected_lora_index = selected_state.index
-    selected_lora = loras[selected_lora_index]
+def update_selection(evt: gr.SelectData):
+    selected_lora = loras[evt.index]
     new_placeholder = f"Type a prompt for {selected_lora['title']}"
     lora_repo = selected_lora["repo"]
     updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
     return (
         gr.update(placeholder=new_placeholder),
         updated_text,
-        selected_state
+        evt.index
     )
 
 @spaces.GPU
-def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_state, scheduler):
-    if not selected_state:
+def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, scheduler):
+    if selected_index is None:
         raise gr.Error("You must select a LoRA before proceeding.")
 
-    selected_lora_index = selected_state.index
-    selected_lora = loras[selected_lora_index]
+    selected_lora = loras[selected_index]
     lora_path = selected_lora["repo"]
     trigger_word = selected_lora["trigger_word"]
 
@@ -67,7 +65,7 @@ with gr.Blocks(css="custom.css") as app:
         "Special thanks to Hugging Face for their Diffusers library and Spaces platform."
     )
 
-    selected_state = gr.State()
+    selected_index = gr.State(None)
 
     with gr.Row():
         gallery = gr.Gallery(
@@ -92,11 +90,11 @@ with gr.Blocks(css="custom.css") as app:
             generate_button = gr.Button("Generate")
             result = gr.Image(label="Generated Image")
 
-    gallery.select(update_selection, outputs=[prompt, selected_info, selected_state])
+    gallery.select(update_selection, outputs=[prompt, selected_info, selected_index])
     
     generate_button.click(
         fn=run_lora,
-        inputs=[prompt, negative_prompt, cfg_scale, steps, selected_state, scheduler],
+        inputs=[prompt, negative_prompt, cfg_scale, steps, selected_index, scheduler],
         outputs=[result]
     )