Upload app.py

app.py

@@ -0,0 +1,66 @@
+# app.py  (HF Spaces: SDK=gradio)
+import io, base64, numpy as np, torch, gradio as gr
+from PIL import Image
+from transformers import AutoImageProcessor, DepthProForDepthEstimation
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+_proc = None
+_model = None
+
+def _lazy_init():
+    global _proc, _model
+    if _proc is None:
+        _proc = AutoImageProcessor.from_pretrained("apple/DepthPro-hf")
+    if _model is None:
+        _model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf").to(device).eval()
+
+def _infer(pil_img: Image.Image):
+    _lazy_init()
+    H, W = pil_img.height, pil_img.width
+    inputs = _proc(images=pil_img.convert("RGB"), return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = _model(**inputs)
+    post = _proc.post_process_depth_estimation(outputs, target_sizes=[(H, W)])[0]
+    depth = post["predicted_depth"].float().cpu().numpy()
+    fov   = float(post.get("field_of_view", 0.0))
+    focal = float(post.get("focal_length", 0.0))
+    return depth, H, W, fov, focal
+
+# (A) API 함수: JSON 반환
+def depth_api(img: Image.Image):
+    depth, H, W, fov, focal = _infer(img)
+    depth_b64 = base64.b64encode(depth.astype(np.float32).tobytes()).decode("ascii")
+    return {
+        "height": int(H),
+        "width": int(W),
+        "focal_px": float(focal),
+        "field_of_view": float(fov),
+        "depth_flat": depth_b64
+    }
+
+# (B) 프리뷰용 UI
+def preview(img: Image.Image):
+    depth, *_ = _infer(img)
+    v = depth[np.isfinite(depth)]
+    lo, hi = (np.percentile(v, 1), np.percentile(v, 99)) if v.size else (0, 1)
+    norm = np.clip((depth - lo) / max(1e-6, hi - lo), 0, 1)
+    return Image.fromarray((norm * 255).astype(np.uint8))
+
+# 🔹 Blocks(UI) 만들기
+with gr.Blocks() as ui:
+    gr.Markdown("## DepthPro-hf (CPU, Free Space)\n- REST API: **POST /api/predict/depth** (JSON base64)")
+    with gr.Row():
+        inp = gr.Image(type="pil", label="Input")
+        out = gr.Image(label="Depth (preview)")
+    gr.Button("Run").click(preview, inp, out)
+
+# 🔹 API 인터페이스 (REST 경로: /api/predict/depth)
+api = gr.Interface(
+    fn=depth_api,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.JSON(),
+    api_name="depth"
+)
+
+# ✅ 두 개를 하나의 앱으로 합치기
+demo = gr.TabbedInterface([ui, api], tab_names=["UI", "api"])