The front-end JavaScript code is available on GitHub. Here is the primary back-end Python code:

import os
import json
import uuid
from base64 import decodestring
import time
from random import choice as rc
from random import sample as rs
import re

import PIL
from PIL import Image
import requests
import exifread

from flask import Flask, request, abort, jsonify
from flask.ext.cors import CORS
from werkzeug import secure_filename

from clarifai.client import ClarifaiApi

app = Flask(__name__)
CORS(app)

app.config['UPLOAD_FOLDER'] = '/var/www/SoundCamera/SoundCamera/static/img'
IMGPATH = '/var/www/SoundCamera/SoundCamera/static/img/'

clarifai_api = ClarifaiApi()

@app.route("/")
def index():
    return "These aren't the droids you're looking for."

@app.route("/img", methods=["POST"])
def img():
	request.get_data()
	if request.method == "POST":
		f = request.files['file']
		if f:
			filename = secure_filename(f.filename)
			f.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
			new_filename = resize_image(filename)
			return jsonify(uri=main(new_filename))
		else:
			abort(501)

@app.route("/b64", methods=["POST"])
def base64():
	if request.method == "POST":
		fstring = request.form['base64str']
		filename = str(uuid.uuid4())+'.jpg'
		file_obj = open(IMGPATH+filename, 'w')
		file_obj.write(fstring.decode('base64'))
		file_obj.close()
		return jsonify(uri=main(filename))

@app.route("/url")
def url():
	img_url = request.args.get('url')
	response = requests.get(img_url, stream=True)
	orig_filename = img_url.split('/')[-1]
	if response.status_code == 200:
		with open(IMGPATH+orig_filename, 'wb') as f:
			for chunk in response.iter_content(1024):
				f.write(chunk)
		new_filename = resize_image(orig_filename)
		return jsonify(uri=main(new_filename))
	else:
		abort(500)


# def allowed_img_file(filename):
#     return '.' in filename and \
# 		filename.rsplit('.', 1)[1].lower() in set(['.jpg', '.jpeg', '.png'])

def resize_image(fn):
    longedge = 640
    orientDict = {
        1: (0, 1),
        2: (0, PIL.Image.FLIP_LEFT_RIGHT),
        3: (-180, 1),
        4: (0, PIL.Image.FLIP_TOP_BOTTOM),
        5: (-90, PIL.Image.FLIP_LEFT_RIGHT),
        6: (-90, 1),
        7: (90, PIL.Image.FLIP_LEFT_RIGHT),
        8: (90, 1)
    }

    imgOriList = []
    try:
        f = open(IMGPATH+fn, "rb")
        exifTags = exifread.process_file(f, details=False, stop_tag='Image Orientation')
        if 'Image Orientation' in exifTags:
            imgOriList.extend(exifTags['Image Orientation'].values)
    except:
        pass

    img = Image.open(IMGPATH+fn)
    w, h = img.size
    newName = str(uuid.uuid4())+'.jpeg'
    if w >= h:
        wpercent = (longedge/float(w))
        hsize = int((float(h)*float(wpercent)))
        img = img.resize((longedge,hsize), PIL.Image.ANTIALIAS)
    else:
        hpercent = (longedge/float(h))
        wsize = int((float(w)*float(hpercent)))
        img = img.resize((wsize,longedge), PIL.Image.ANTIALIAS)

    for val in imgOriList:
        if val in orientDict:
            deg, flip = orientDict[val]
            img = img.rotate(deg)
            if flip != 1:
                img = img.transpose(flip)

    img.save(IMGPATH+newName, format='JPEG')
    os.remove(IMGPATH+fn)
    
    return newName

def chunks(l, n):
    """Yield successive n-sized chunks from l."""
    for i in xrange(0, len(l), n):
        yield l[i:i+n]

def get_tags(fp):
    fileObj = open(fp)
    result = clarifai_api.tag_images(fileObj)
    resultObj = result['results'][0]
    tags = resultObj['result']['tag']['classes']
    return tags

def genius_search(tags):
    access_token = 'd2IuV9fGKzYEWVnzmLVtFnm-EYvBQKR8Uh3I1cfZOdr8j-BGVTPThDES532dym5a'
    payload = {
        'q': ' '.join(tags),
        'access_token': access_token
    }
    endpt = 'http://api.genius.com/search'
    response = requests.get(endpt, params=payload)
    results = response.json()
    hits = results['response']['hits']
    
    artists_titles = []
    
    for h in hits:
        hit_result = h['result']
        if hit_result['url'].endswith('lyrics'):
            artists_titles.append(
                (hit_result['primary_artist']['name'], hit_result['title'])
            )
    
    return artists_titles

def spotify_search(query):
    endpt = "https://api.spotify.com/v1/search"
    payload = {
        'q': query,
        'type': 'track'
    }
    response = requests.get(endpt, params=payload)
    result = response.json()
    result_zero = result['tracks']['items'][0]
    
    return result_zero['uri']

def main(fn):
    tags = get_tags(IMGPATH+fn)
    for tag_chunk in chunks(tags,3):
        artists_titles = genius_search(tag_chunk)
        for artist, title in artists_titles:
            try:
                result_uri = spotify_search(artist+' '+title)
            except IndexError:
                pass
            else:
                return result_uri


if __name__ == "__main__":
    app.run()

It uses the same algorithm discussed in my prior post. Now that I have the opportunity to test it more, I am not quite satisfied with the results it is providing. First of all, they are not entirely deterministic (you can upload the same photo twice and end up with two different songs in some cases). Moreover, the results from a human face — which I expect to be a common use case — are not very personal. For the next steps in this project, I plan to integrate additional data including GPS, weather, time of day, and possibly even facial expressions in order to improve the output.

The broken cameras I ordered from eBay have arrived, and I have been considering how to use them as cases for the new models. I also purchased a GPS module for my Raspberry Pi, so the next Sound Camera prototype, with new features integrated, will likely be a physical version. I’m planning to use this Kodak Brownie camera (c. 1916):