Update app.py

app.py

@@ -1,52 +1,45 @@
 import gradio as gr
-import torch
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-from PIL import Image
-import numpy as np
+from huggingface_hub import InferenceClient
+import PIL.Image
+import io
 
-def load_model():
-    controlnet = ControlNetModel.from_pretrained("Kwai-Kolors/Kolors-Virtual-Try-On")
-    pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        "Kwai-Kolors/Kolors-Virtual-Try-On",
-        controlnet=controlnet,
-        torch_dtype=torch.float16
-    )
-    if torch.cuda.is_available():
-        pipe = pipe.to("cuda")
-    return pipe
-
-# Model'i global olarak yükle
-try:
-    model = load_model()
-    print("Model başarıyla yüklendi!")
-except Exception as e:
-    print(f"Model yüklenirken hata: {str(e)}")
+client = InferenceClient(
+    model="Kwai-Kolors/Kolors-Virtual-Try-On"
+)
 
 def virtual_try_on(person_image, garment_image):
     """
-    Virtual try-on process
+    Process the virtual try-on request
+    Args:
+        person_image: PIL Image of the person
+        garment_image: PIL Image of the garment
+    Returns:
+        PIL Image of the result
     """
     try:
-        # Resimleri uygun formata dönüştür
-        if person_image is None or garment_image is None:
-            return None, "Error: Both images are required"
-            
-        # Model inference
-        output = model(
-            person_image,
-            garment_image,
-            num_inference_steps=30,
-            guidance_scale=7.5
+        # Convert images to bytes
+        person_bytes = io.BytesIO()
+        garment_bytes = io.BytesIO()
+        person_image.save(person_bytes, format='PNG')
+        garment_image.save(garment_bytes, format='PNG')
+        
+        # Make API request
+        response = client.post(
+            json={
+                "inputs": [
+                    {"image": person_bytes.getvalue()},
+                    {"image": garment_bytes.getvalue()}
+                ]
+            }
         )
         
-        # Sonuç resmini al
-        result_image = output.images[0]
+        # Convert response to image
+        result_image = PIL.Image.open(io.BytesIO(response))
         return result_image, "Success"
-        
     except Exception as e:
         return None, f"Error: {str(e)}"
 
-# Gradio arayüzü
+# Create Gradio interface
 demo = gr.Interface(
     fn=virtual_try_on,
     inputs=[
@@ -57,7 +50,7 @@ demo = gr.Interface(
         gr.Image(type="pil", label="Result"),
         gr.Text(label="Status")
     ],
-    title="Virtual Try-On",
+    title="Virtual Try-On API",
     description="Upload a person image and a garment image to see how the garment would look on the person."
 )