Update app.py

app.py

@@ -1,14 +1,20 @@
 import gradio as gr
 from diffusers import DiffusionPipeline
 
-# Load the pipeline and LoRA weights
+# Initialize the pipeline variable globally
+pipeline = None
 
+# Load the pipeline and LoRA weights
 def load_cust(base_model, models_sec):
+    global pipeline
     pipeline = DiffusionPipeline.from_pretrained(base_model)
     pipeline.load_lora_weights(models_sec)
 
 def generate_image(prompt, negative_prompt):
+    global pipeline
     # Generate the image with the provided prompts
+    if pipeline is None:
+        return "Pipeline not loaded. Please load the models first."
     image = pipeline(prompt, negative_prompt=negative_prompt).images[0]
     return image
 
@@ -18,10 +24,10 @@ with gr.Blocks() as demo:
     prompt = gr.Textbox(label="Prompt", placeholder="Enter your text prompt here")
     negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter your negative prompt here")
     submit_button = gr.Button("Generate Image")
-    with gr.Accordion('load your custom models first'):
-        basem = gr.Textbox(label="your models adapter")
-        secondm = gr.Textbox(label="your main models")
-        exports = gr.Button("load your models")
+    with gr.Accordion('Load your custom models first'):
+        basem = gr.Textbox(label="Your base model", value='John6666/pony-diffusion-v6-xl-sdxl-spo')
+        secondm = gr.Textbox(label="Your LoRA model", value='Blane187/miyako-saitou-s1-ponyxl-lora-nochekaiser')
+        exports = gr.Button("Load your models")
         exports.click(load_cust, inputs=[basem, secondm], outputs=[])
     output_image = gr.Image(label="Generated Image")
     submit_button.click(generate_image, inputs=[prompt, negative_prompt], outputs=output_image)