Delete app.py

app.py

@@ -1,112 +0,0 @@
-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')
-