Update app.py

app.py

@@ -1,21 +1,29 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from diffusers import DiffusionPipeline
+from PIL import Image
 
-# Initialize the InferenceClient
-client = InferenceClient("models/black-forest-labs/FLUX.1-schnell")
+# Load the pipeline
+pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")
 
 def merge_images(model_image, cloth_image):
-    # Prepare the inputs for the model
-    inputs = {
-        "image_1": model_image,
-        "image_2": cloth_image,
-    }
+    # Save uploaded images temporarily
+    model_image.save("model_image.png")
+    cloth_image.save("cloth_image.png")
     
-    # Make the API call to the model
-    output = client.post(json=inputs)
+    # Create prompt
+    prompt = f"A model wearing the clothing from the second image model_image.png and cloth_image.png"
     
-    # The output should be the merged image
-    return output
+    # Generate the merged image
+    output_image = pipeline(
+        prompt,
+        guidance_scale=0.8,
+        num_inference_steps=4,
+        max_sequence_length=256,
+        generator=torch.Generator("cpu").manual_seed(0)
+    ).images[0]
+    
+    return output_image
 
 # Create the Gradio interface
 iface = gr.Interface(