Update app.py

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, UploadFile, File, Response
 import cv2
 import numpy as np
 import torch
@@ -8,18 +8,20 @@ import io
 
 app = FastAPI()
 
-# Load MiDaS model
+# Load AI Model MiDaS
 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(...)):
     try:
         image_bytes = await file.read()
-        print(f"📷 Nhận ảnh ({len(image_bytes)} bytes)")
+        print(f"📷 Ảnh nhận được ({len(image_bytes)} bytes)")
 
         image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
         print("✅ Ảnh mở thành công!")
@@ -33,11 +35,12 @@ async def upload_image(file: UploadFile = File(...)):
         depth_map = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
         depth_resized = cv2.resize(depth_map, (128, 64))
 
+        # Mã hóa ảnh thành JPEG
         _, buffer = cv2.imencode(".jpg", depth_resized)
         print("✅ Depth Map đã được tạo!")
 
-        return {"depth_map": buffer.tobytes()}  
-
+        return Response(content=buffer.tobytes(), media_type="image/jpeg")  # 🟢 Trả ảnh trực tiếp
+    
     except Exception as e:
         print("❌ Lỗi xử lý ảnh:", str(e))
         return {"error": str(e)}