update example

app.py

@@ -21,17 +21,17 @@ torch.manual_seed(1)
 counter = 0
 
 #Getting Lora fine-tuned weights
-def monkeypatching(alpha, in_prompt, example_wt): #, prompt, pipe): finetuned_lora_weights
+def monkeypatching(alpha, in_prompt, wt): #, prompt, pipe): finetuned_lora_weights
     print("****** inside monkeypatching *******")
     print(f"in_prompt is - {str(in_prompt)}")
     global counter
     if counter == 0 :
-        if example_wt is None : 
-            monkeypatch_lora(pipe.unet, torch.load("./output_example/lora_weight.pt")) #finetuned_lora_weights
+        if wt == "./lora_playgroundai_wt.pt" : 
+            monkeypatch_lora(pipe.unet, torch.load(wt)) #finetuned_lora_weights
             tune_lora_scale(pipe.unet, alpha) #1.00)
             counter +=1
         else:
-            monkeypatch_lora(pipe.unet, torch.load(example_wt)) #finetuned_lora_weights
+            monkeypatch_lora(pipe.unet, torch.load("./output_example/lora_weight.pt")) #finetuned_lora_weights
             tune_lora_scale(pipe.unet, alpha) #1.00)
             counter +=1
     else :
@@ -81,17 +81,17 @@ with gr.Blocks() as demo:
                 gr.Markdown("Advance settings for a number of Training Steps and Alpha. Set alpha to 1.0 to fully add LORA. If the LORA seems to have too much effect (i.e., overfitting), set alpha to a lower value. If the LORA seems to have too little effect, set the alpha higher. You can tune these two values to your needs.")
                 in_steps = gr.Number(label="Enter the number of training steps", value = 4000)
                 in_alpha = gr.Slider(0.1,1.0, step=0.01, label="Set Alpha level", value=0.5)
-            out_file = gr.File(label="Lora trained model weights", )
+            out_file = gr.File(label="Lora trained model weights" )
 
     gr.Examples(
         examples=[[0.65, "lion", "./lora_playgroundai_wt.pt" ]],
-        inputs=[in_alpha, in_prompt, example_wt],
+        inputs=[in_alpha, in_prompt, out_file],
         outputs=out_image,
         fn=monkeypatching,
         cache_examples=True,)
         
     b1.click(fn = accelerate_train_lora, inputs=in_steps, outputs=out_file) 
-    b2.click(fn = monkeypatching, inputs=[in_alpha, in_prompt, example_wt], outputs=out_image)
+    b2.click(fn = monkeypatching, inputs=[in_alpha, in_prompt, out_file], outputs=out_image)
 
 demo.queue(concurrency_count=3) 
 demo.launch(debug=True, show_error=True)