Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from PIL import Image
+import torch
+from diffusers import AutoPipelineForImage2Image
+
+# 1. Ładujemy model WindowSeat z Hugging Face
+MODEL_ID = "huawei-bayerlab/windowseat-reflection-removal-v1-0"
+
+pipe = AutoPipelineForImage2Image.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16,
+).to("cuda" if torch.cuda.is_available() else "cpu")
+
+# 2. Funkcja, która przyjmuje jedno zdjęcie i zwraca przetworzone
+def remove_reflection(img: Image.Image) -> Image.Image:
+    result = pipe(
+        image=img,
+        prompt="",          # nie potrzebujemy promptu, model wie co robić
+        strength=1.0,
+        num_inference_steps=4,
+        guidance_scale=1.0,
+    ).images[0]
+    return result
+
+# 3. Gradio – proste API: jedno wejście (image), jedno wyjście (image)
+demo = gr.Interface(
+    fn=remove_reflection,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="WindowSeat Reflection Removal - API",
+    description="Upload a photo with window reflections – get cleaned version.",
+)
+
+if __name__ == "__main__":
+    demo.launch()