Update app.py

app.py

@@ -1,34 +1,8 @@
 import gradio as gr
-from PIL import Image
-import torch
-import torch.cuda.amp as amp
-from diffusers import DiffusionPipeline
 
-device = torch.device("cpu")  # Default to CPU device
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-
-# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
-pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-pipeline.load_lora_weights("miittnnss/miittnnss-lora")
-pipeline.to(device)
-
-def generate(prompt):
-    with torch.no_grad(), amp.autocast(enabled=device != torch.device("cpu")):
-        image = pipeline(prompt, guidance_scale=8.5)["sample"][0]
-
-    image.save('generatedimage.png')
-    return image
-
-def predict(prompt):
-    image = generate(prompt)
-    return image
-
-iface = gr.Interface(
-    fn=predict,
+gr.load(
+    "models/miittnnss/miittnnss-lora",
     title="Miittnnss LoRA Diffusion",
-    inputs="text",
-    outputs="image",
-    capture_session=True,
-)
-iface.launch()
+    description="Type any text prompt to get started.",
+    theme="soft"
+).launch()