app.py

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)