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from flask import Flask, request
from flask_cors import CORS
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsClassifier
import pickle
import os
import numpy as np
import pandas as pd
from io import BytesIO
import base64
import json
import re
import sys
app = Flask(__name__)
CORS(app)
app.config['MAX_CONTENT_LENGTH'] = 16 * 1000 * 1000
mnist_model = None
# 'tensorflow-local'
# cats_model = None
def load_mnist_model():
# load the pre-trained model (here we are using a model
# pre-trained of week 8, but you can
# substitute in your own networks just as easily)
global mnist_model
# create prediction
mnist_model = KNeighborsClassifier(n_neighbors=4, weights='distance')
model_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "knn_clf.pkl")
print(model_filename)
with open(model_filename, 'rb') as f:
mnist_model = pickle.load(f)
@app.route('/api/prediction/mnist', methods=['POST'])
def predict_mnist():
try:
image_data = re.sub('^data:image/.+;base64,', '', request.get_json()['image'])
im = Image.open(BytesIO(base64.b64decode(image_data)))
# im.save('canvas.png')
im28x28 = im.resize((28, 28)).convert('L') #resize the image to 28x28 and converts it to gray scale
# image to numpy
np_image = np.asarray(im28x28)
# assert(np_image.shape == (28, 28))
print(np.abs(np_image.reshape(28*28)-[255]))
# np.abs(np_image.reshape(28*28)-[255])
# reshapes the image to 28x28 pixes, substracts 255 from every value and applys abs to the matrix.
# this has to be done, becuase the mnist values are "Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black). " (s. http://yann.lecun.com/exdb/mnist/)
# Grayscale uses 0 for black and 255 for white.
predicted_number = mnist_model.predict([np.abs(np_image.reshape(28*28)-[255])])
print(predicted_number)
buffered = BytesIO()
im28x28.save(buffered, format="PNG")
im28x28.save('canvas2.png')
img_str = base64.b64encode(buffered.getvalue())
img_base64 = bytes("data:image/png;base64,", encoding='utf-8') + img_str
return json.dumps({'image': str(img_base64.decode('utf-8')), 'prediction': predicted_number[0]}), 200, {'ContentType': 'application/json'}
except Exception as err:
return json.dumps({'error': str(err)})
@app.route("/api/prediction/apartment", methods=['GET']) #URL for rest api
def predict():
bfs_number = int(request.args['bfs_number']) #transform them to numbers (int and float)
area = float(request.args['area'])
rooms = float(request.args['rooms'])
df_bfs_data = pd.read_csv(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bfs_municipality_and_tax_data.csv'),
sep=',', encoding='utf-8') #read CSV to get all the data from 'bfs_municipality_and_tax_data.csv'
df_bfs_data['tax_income'] = df_bfs_data['tax_income'].str.replace("'", "")
df = df_bfs_data[df_bfs_data['bfs_number']==bfs_number]
randomforest_model = RandomForestRegressor()
model_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "randomforest_regression.pkl")
with open(model_filename, 'rb') as f:
randomforest_model = pickle.load(f)
# ['rooms' 'area' 'pop' 'pop_dens' 'frg_pct' 'emp' 'tax_income' 'm2_per_rooms']
prediction = randomforest_model.predict([[rooms, area, df['pop'].iloc[0], df['pop_dens'].iloc[0], df['frg_pct'].iloc[0], df['emp'].iloc[0], df['tax_income'].iloc[0], area/rooms]])
#search these numbers for the respondig bfs_number
return str(round(prediction[0],2))
@app.route("/")
def hello_world():
print(request.args)
model_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), "mlp_clf.pkl")
return "<p>Hello, World!</p> " + model_filename
# if this is the main thread of execution first load the model and
# then start the server
if __name__ == "__main__" or __name__ == "app" or __name__ == "flask_app":
print(("* Loading model and Flask starting server..."
"please wait until server has fully started"))
load_mnist_model()
# 'tensorflow-local'
#load_cats_model()
print(sys.executable)
print('running')
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