Create app.py

app.py

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+import gradio as gr
+from diffusers import StableDiffusionPipeline
+import torch
+
+# ⚠️ You can replace this with any try-on model, e.g. akhaliq/TryOnDiffusion
+model_id = "akhaliq/TryOnDiffusion"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+
+def try_on(person_img, cloth_img):
+    # Minimal demo — replace with model-specific inference
+    prompt = f"A photo of this person wearing the clothes shown."
+    images = pipe(prompt, image=[person_img, cloth_img]).images
+    return images[0]
+
+demo = gr.Interface(
+    fn=try_on,
+    inputs=[gr.Image(label="Person"), gr.Image(label="Clothing")],
+    outputs=gr.Image(label="Result"),
+    title="Virtual Try-On (TryOnDiffusion)",
+    description="Upload a full-body photo and a clothing item to see a virtual try-on result."
+)
+
+if __name__ == "__main__":
+    demo.launch()