Deploy Multi-label classifier into production

Part 1 (2019)
2

I’m working on deploying every model :slight_smile: The Render walkthrough is a great way to deploy most every type of vision model I’ve found, as they all rely on the same bit of boilerplate code. Let’s take a look at what would be different from our analyze function between the two.
First, single classification based on the example from the course:

@app.route('/analyze', methods=["POST"])
  data = await request.form()
  bytes = await (data['file'].read())
  pred = learn.predict(bytes)[0]
  return JSONResponse({'result':str(pred)})

Now, how do we modify this for multi-label? The only thing that needs to be changed is what happens with our predictions, as our multi-labels (From the planets example) now have a _. All we have to do is something like so:

    for p in pred: p.replace('_', ' ')
    for p in pred: p.title()
    if pred == '[]':
		pred = 'Could not recognize any classes, perhaps try another photo?'
    return JSONResponse({
		'result' : pred
		})

All we changed was making it more readable for the end product. This is using Starlette based on the Render example: Documentation tutorial code

As you can see, the predict function of fastai is what makes this possible :slight_smile:

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