app.j

//routes w streams MUST be above this !! content type application/octet-stream 
// client side posted photo as http w binary request.body (no form, just the file)
// middleware pushes inbound http readstream to an obj in .body
app.use(express.raw({
	inflate: true,
    limit: '9mb',
    type: 'application/octet-stream'}))
// path or route chosen by client upload is "labelai" && fileName of <img>
app.options('/labelai', cors());
app.post('/labelai/:fname',
  cors(),    
  verifyToken,  // JWT secure on the api 
  wrapAsync(async(req, resp, next) => { 
  	const filename = req.params.fname;  	
  	let imgnew = await resizeImage(req.body);
  	let base64 = imgnew.toString('base64')
// scaledown the img as upload from phonecamera = fullSz
// base64-encode downscaled img for openai classification     
// roboflow - openai project .com/${photoClassificationModel}
// https://app.roboflow.com/project/org311-clip-photos/2      
	let res = await axios.post('https://classify.roboflow.com/org311-clip-photos/2?api_key=1f7N'
	,base64 
	, { headers: {
	    'Content-Type': 'application/x-www-form-urlencoded'
	  }
	})
  	resp.set({'Content-Type': 'application/json'});
  	resp.end(JSON.stringify({ data: res.data })); //json response w openAi classification see below: 
  }
  )
)
...
import sharp from 'sharp';

async function resizeImage(img) {
  try {
    return sharp(img)
      .resize({
        width: 800
      })
      .toBuffer();
     
  } catch (error) {
    console.log(error);
  }
}
/*  Open Ai classification response == "Garbage" 
"data":{
  "time":0.19188385199959157,
  "image":{
    "width":800,
    "height":600
  },
"predictions":[{
  "class":"garbage",
  "confidence":0.6223},
{
  "class":"graffiti",
  "confidence":0.1344},
{
  "class":"encampment",
  "confidence":0.1305},
{
  "class":"mural",
  "confidence":0.1128}
],
"top":"garbage",
"confidence":0.6223
}
*/

fetchPost.js

<!-- client side to capture photo and to upload as binary file (.jpeg | .png)  
JWT is auth for serverSide api - standard "bearer token" 
api hosted on "railway" 
-->

<label for="files">Select Photo </label>
<input id="files" type="file"  accept="image/png, image/jpeg"  style="height: 320px; font-size:18px">
    
const uploader = document.getElementById('files');
var file = uploader.files[0]
const upload = (file) => {
	let name = file.name; 
  let url1 = "https://openAiImg-production.up.railway.app/labelai/" + name;
  console.log(url1)
  fetch(url1, { 
    method: 'POST',
    headers: {
      "application":"openAiImg-production.up.railway.app",
      "content-type":"application/octet-stream",
      "authorization":"Bearer epZ3ogNhLw" 
},
    body: file
  }).then(
    response => response.json()
  ).then(
resJson => {
  const ans = resJson.data.predictions
  bubble_fn_uploadResp(JSON.stringify(ans))
 }
  ).catch(
    error => console.log(error.message) 
  );
};

var resp = upload(file)