nwatab/Image-classification-API-with-FLASK-KERAS

## README.md

      
    Raw
  

              README.md
            
          
    Simple image classification API
Usage

$ python app.py
Example

$ curl -X POST -F file=@cat.jpg 'http://localhost:5000/predict'
{
  "predictions": [
    {
      "label": "tiger_cat", 
      "probability": 0.4184308648109436
    }, 
    {
      "label": "Egyptian_cat", 
      "probability": 0.3616541028022766
    }, 
    {
      "label": "tabby", 
      "probability": 0.1262882500886917
    }, 
    {
      "label": "lynx", 
      "probability": 0.02171700820326805
    }, 
    {
      "label": "cup", 
      "probability": 0.00845610722899437
    }
  ], 
  "success": true
}

Reference


https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html


## app.py
import os
from flask import Flask, request, redirect, url_for, jsonify, Response
from werkzeug.utils import secure_filename

from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from keras.preprocessing import image
import numpy as np
from PIL import Image
import io

app = Flask(__name__)
model = None

def load_model():
    global model
    model = VGG16(weights='imagenet', include_top=True)

@app.route('/predict', methods=['GET', 'POST'])
def upload_file():
    response = {'success': False}
    if request.method == 'POST':
        if request.files.get('file'): # image is stored as name "file"
            img_requested = request.files['file'].read()
            img = Image.open(io.BytesIO(img_requested))
            if img.mode != 'RGB':
                img = img.convert('RGB')
            img = img.resize((224, 224))
            img = image.img_to_array(img)
            img = np.expand_dims(img, axis=0)
            inputs = preprocess_input(img)

            preds = model.predict(inputs)
            results = decode_predictions(preds)

            response['predictions'] = []
            for (imagenetID, label, prob) in results[0]: # [0] as input is only one image
                row = {'label': label, 'probability': float(prob)} # numpy float is not good for json
                response['predictions'].append(row)
            response['success'] = True
            return jsonify(response)

    return '''
    <!doctype html>
    <title>Upload new File</title>
    <h1>Upload new File</h1>
    <form method=post enctype=multipart/form-data>
      <p><input type=file name=file>
         <input type=submit value=Upload>
    </form>
    '''

if __name__ == '__main__':
    load_model()
    # no-thread: https://github.com/keras-team/keras/issues/2397#issuecomment-377914683
    # avoid model.predict runs before model initiated
    # To let this run on HEROKU, model.predict should run onece after initialized
    app.run(threaded=False)

## Image-classification-API-with-FLASK-KERAS

      
    Raw
  

              Image-classification-API-with-FLASK-KERAS
	import os
	from flask import Flask, request, redirect, url_for, jsonify, Response
	from werkzeug.utils import secure_filename

	from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
	from keras.preprocessing import image
	import numpy as np
	from PIL import Image
	import io

	app = Flask(__name__)
	model = None

	def load_model():
	global model
	model = VGG16(weights='imagenet', include_top=True)

	@app.route('/predict', methods=['GET', 'POST'])
	def upload_file():
	response = {'success': False}
	if request.method == 'POST':
	if request.files.get('file'): # image is stored as name "file"
	img_requested = request.files['file'].read()
	img = Image.open(io.BytesIO(img_requested))
	if img.mode != 'RGB':
	img = img.convert('RGB')
	img = img.resize((224, 224))
	img = image.img_to_array(img)
	img = np.expand_dims(img, axis=0)
	inputs = preprocess_input(img)

	preds = model.predict(inputs)
	results = decode_predictions(preds)

	response['predictions'] = []
	for (imagenetID, label, prob) in results[0]: # [0] as input is only one image
	row = {'label': label, 'probability': float(prob)} # numpy float is not good for json
	response['predictions'].append(row)
	response['success'] = True
	return jsonify(response)

	return '''
	<!doctype html>
	<title>Upload new File</title>
	<h1>Upload new File</h1>
	<form method=post enctype=multipart/form-data>
	<p><input type=file name=file>
	<input type=submit value=Upload>
	</form>
	'''

	if __name__ == '__main__':
	load_model()
	# no-thread: https://github.com/keras-team/keras/issues/2397#issuecomment-377914683
	# avoid model.predict runs before model initiated
	# To let this run on HEROKU, model.predict should run onece after initialized
	app.run(threaded=False)