Update app.py

app.py

@@ -39,6 +39,14 @@ else:
 with open('loras.json', 'r') as f:
     loras = json.load(f)
 
+# Create a list of options for the LoRAs.
+# You could use the index or a descriptive label.
+lora_options = [f"{idx}: {lora['title']}" for idx, lora in enumerate(loras)]
+
+# Create a CheckboxGroup that returns a list of selected options (as strings)
+selected_lora_indices = gr.CheckboxGroup(choices=lora_options, label="Select LoRAs to load", value=[])
+
+
 # Initialize the base model
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -81,6 +89,16 @@ class calculateDuration:
         else:
             print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
 
+def parse_selected_indices(selected_options):
+    indices = []
+    for option in selected_options:
+        try:
+            index = int(option.split(":")[0])
+            indices.append(index)
+        except Exception:
+            continue
+    return indices
+
 def update_selection(evt: gr.SelectData, width, height):
     selected_lora = loras[evt.index]
     new_placeholder = f"Type a prompt for {selected_lora['title']}"
@@ -143,6 +161,10 @@ def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps
 
 @spaces.GPU(duration=70)
 def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
+    print("Selected indices (raw):", selected_indices)
+    # Then parse them if needed
+    parsed_indices = parse_selected_indices(selected_indices)
+    print("Parsed indices:", parsed_indices)
     # Ensure at least one LoRA is selected
     if not selected_indices or len(selected_indices) == 0:
         raise gr.Error("You must select at least one LoRA before proceeding.")