upd

app.py

@@ -22,6 +22,8 @@ counter = 0
 
 #Getting Lora fine-tuned weights
 def monkeypatching(alpha, in_prompt): #, prompt, pipe): finetuned_lora_weights
+    print("****** inside monkeypatching *******")
+    print(f"in_prompt is - {str(in_prompt)}")
     global counter
     if counter == 0 :
         monkeypatch_lora(pipe.unet, torch.load("./output_example/lora_weight.pt")) #finetuned_lora_weights
@@ -29,7 +31,7 @@ def monkeypatching(alpha, in_prompt): #, prompt, pipe): finetuned_lora_weights
         counter +=1
     else :
         tune_lora_scale(pipe.unet, alpha) #1.00)
-    prompt = "style of sks, " + in_prompt #"baby lion"
+    prompt = "style of sks, " + str(in_prompt)   #"baby lion"
     image = pipe(prompt, num_inference_steps=50, guidance_scale=7).images[0]
     image.save("./illust_lora.jpg") #"./contents/illust_lora.jpg")
     return image
@@ -63,7 +65,7 @@ with gr.Blocks() as demo:
         b1 = gr.Button(value="Train LORA model")
         b2 = gr.Button(value="Inference using LORA model")
     with gr.Row():
-        in_prompt = gr.Textbox(label="Enter a prompt for fine-tuned LORA model")
+        in_prompt = gr.Textbox(label="Enter a prompt for fine-tuned LORA model", visible=True)
         out_image = gr.Image(label="Image generated by LORA model")
         out_file = gr.File(label="Lora trained model weights")
     b1.click(fn = accelerate_train_lora, inputs=in_steps, outputs=out_file)