Update app.py

app.py

@@ -1,7 +1,7 @@
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 from diffusers import StableDiffusionPipeline
 import gradio as gr
+from PIL import Image
 
 # Load the model and pipeline
 model_id = "ares1123/virtual-dress-try-on"
@@ -9,8 +9,24 @@ pipeline = StableDiffusionPipeline.from_pretrained(model_id)
 pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
 
 def virtual_try_on(image, clothing_image):
+    # Convert images to proper format and get dimensions
+    width, height = image.size
+    
+    # Ensure dimensions are multiples of 8
+    width = (width // 8) * 8
+    height = (height // 8) * 8
+
+    # Resize images to fit the model's expected input
+    image = image.resize((width, height))
+    clothing_image = clothing_image.resize((width, height))
+
+    # Define a prompt describing what you want the model to do
+    prompt = "A person wearing new clothes"
+
     # Process the images using the model
-    try_on_image = pipeline(image, clothing_image).images[0]
+    result = pipeline(prompt=prompt, image=image, conditioning_image=clothing_image)
+    try_on_image = result.images[0]
+    
     return try_on_image
 
 # Set up a simple Gradio interface for testing