Upload app.py

app.py

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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')
+