Create app.py

app.py

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+from fastapi import FastAPI, UploadFile, File
+import uvicorn
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+import io
+
+app = FastAPI()
+
+# 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])])
+
+@app.post("/upload/")
+async def upload_image(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    
+    # Convert to tensor & run AI model
+    img_tensor = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        depth_map = midas(img_tensor).squeeze().cpu().numpy()
+
+    # Normalize depth map
+    depth_map = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
+    depth_img = cv2.applyColorMap(depth_map, cv2.COLORMAP_JET)
+
+    _, buffer = cv2.imencode(".jpg", depth_img)
+    return {"depth_map": buffer.tobytes()}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)