Update app.py

app.py

@@ -8,25 +8,32 @@ import io
 
 app = FastAPI()
 
+# Load MiDaS model
 midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
 midas.eval()
-transform = T.Compose([T.Resize((256, 256)), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+transform = T.Compose([T.Resize((256, 256)), T.ToTensor(), 
+                       T.Normalize(mean=[0.485, 0.456, 0.406], 
+                                   std=[0.229, 0.224, 0.225])])
 
 @app.post("/upload/")
 async def upload_image(file: UploadFile = File(...)):
-    image_bytes = await file.read()
-    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    try:
+        image_bytes = await file.read()  # ✔️ Đọc ảnh nhị phân
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        print("📷 Ảnh nhận được, kích thước:", image.size)
 
-    img_tensor = transform(image).unsqueeze(0)
-    with torch.no_grad():
-        depth_map = midas(img_tensor).squeeze().cpu().numpy()
+        # Chuyển đổi ảnh sang tensor
+        img_tensor = transform(image).unsqueeze(0)
+        with torch.no_grad():
+            depth_map = midas(img_tensor).squeeze().cpu().numpy()
 
-    depth_map = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
-    depth_img = cv2.resize(depth_map, (128, 64))
-    
-    _, buffer = cv2.imencode(".jpg", depth_img)
-    return {"depth_map": buffer.tobytes()}
+        # Chuẩn hóa depth map
+        depth_map = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
+        depth_resized = cv2.resize(depth_map, (128, 64))
+
+        _, buffer = cv2.imencode(".jpg", depth_resized)
+        print("✅ Depth Map đã được tạo!")
 
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+        return {"depth_map": buffer.tobytes()}  # ✔️ Gửi ảnh dạng nhị phân
+    
+    except Excep