Update app.py

app.py

@@ -1,24 +1,11 @@
 import gradio as gr
 from diffusers import DiffusionPipeline
 
-# Initialize the pipeline variable globally
-pipeline = None
 
-# Load the pipeline and LoRA weights
-def load_cust(modelsyu, progress=gr.Progress()):
-    progress(10.4, desc="loading your lora models...")
-    global pipeline
-    pipeline = DiffusionPipeline.from_pretrained(modelsyu)
-    # Ensure the pipeline uses the CPU
-    pipeline.to("cpu")
-    return "Models loaded successfully"
-
-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."
-    # Make sure the pipeline is set to use the CPU
+def generate_image(modelsyu, prompt, negative_prompt):
+    
+    modelslo = [modelsyu]   
+    pipeline = DiffusionPipeline.from_pretrained(modelslo)
     pipeline.to("cpu")
     image = pipeline(prompt, negative_prompt=negative_prompt).images[0]
     return image
@@ -30,12 +17,9 @@ with gr.Blocks() as demo:
     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 Lora model")  
-        exports = gr.Button("Load your models")
-        outputid = gr.Textbox(label="Output", interactive=False)
-        exports.click(load_cust, inputs=[basem], outputs=[outputid])
+        basem = gr.Textbox(label="Your Lora model", value="John6666/pony-diffusion-v6-xl-sdxl-spo")  
     output_image = gr.Image(label="Generated Image")
-    submit_button.click(generate_image, inputs=[prompt, negative_prompt], outputs=output_image)
+    submit_button.click(generate_image, inputs=[basem, prompt, negative_prompt], outputs=output_image)
 
 # Launch the demo
 demo.launch()