app.py

import json
import os
import traceback

from flask import Flask, request, jsonify
from flask_cors import CORS
import pandas as pd
import joblib
from model.model import Model
from preprocessing.cleaning_data import preprocess

app = Flask(__name__)
app.config["DEBUG"] = True
CORS(app)


@app.route('/', methods=['GET'])
def home():
    return "Alive!!"


# Post request that receives the data of a house in JSON format and returns the prediction
@app.route('/predict', methods=['POST'])
def predict():
    if regressor:
        try:
            json_ = request.json  # Take all available entries
            print(type(json_))
            if type(json_) == list:
                json_ = preprocess(json_[0]) # preprocess the json file
            else:
                json_ = preprocess(json_)
            query = pd.DataFrame(json_)
            query = query.reindex(columns=model_columns, fill_value=0)
            # Convert column names to model's column names
            X_val_prep = load_poly.transform(query)
            prediction = list(regressor.predict(X_val_prep))  # Prediction
            return jsonify({'Prediction Price': list(prediction)})  # Return the result of prediction

        except Exception as e:
            return jsonify({'Error': str(e), "trace": traceback.format_exc()})

        except ValueError as err:
            return jsonify({'Error': str(err.args), "trace": traceback.format_exc()})
    else:
        print("Train the dataset first")
        return "No model found here!"


# Get request returning the explanation of expected data and format
@app.route('/predict', methods=['GET'])
def str_format():
    dict_of_expected_outcome = {
        "Living area": {'type': 'int', 'optional': False, 'default': []},
        "Bedroom": {'type': 'int', 'optional': False, 'default': []},
        "Province": {
            'type': 'str',
            'optional': False,
            'default': [
                'Brussels', 'Oost-vlaanderen', 'Vlaams-brabant', 'Luik', 'Namen',
                'Luxemburg', 'West-vlaanderen', 'Antwerpen', 'Henegouwen',
                'Waals-brabant', 'Limburg']},
        "Property Type": {'type': 'str', 'optional': False, 'default': ['Apartment', 'House']},
        "Property Subtype": {
            'type': 'str',
            'optional': True,
            'default': [
                'Apartment', 'Town-house', 'House', 'Villa', 'Penthouse',
                'Mansion', 'Studio', 'Exceptional property', 'Kot', 'Duplex',
                'Triplex', 'Ground floor', 'Bungalow', 'Loft', 'Chalet',
                'Service flat', 'Castle', 'Farmhouse', 'Country house',
                'Manor house', 'Other properties']
        },
        "Surface of the plot": {'type': 'int', 'optional': True, 'default': []},
        "HasGarden": {'type': 'str', 'optional': True, 'default': ['Yes', 'No']},
        "Garden surface": {'type': 'int', 'optional': True, 'default': []},
        "Kitchen Type": {
            'type': 'str',
            'optional': True,
            'default': ['Equipped', 'Semi-equipped', 'Not installed']},
        "Swimming pool": {'type': 'str', 'optional': True, 'default': ['Yes', 'No']},
        "Furnished": {'type': 'str', 'optional': True, 'default': ['Yes', 'No']},
        "HasFireplace": {'type': 'str', 'optional': True, 'default': ['Yes', 'No']},
        "HasTerrace": {'type': 'str', 'optional': True, 'default': ['Yes', 'No']},
        "Terrace surface": {'type': 'int', 'optional': True, 'default': []},
        "Number of frontages": {'type': 'int', 'optional': True, 'default': []},
        "Building condition": {'type': 'str', 'optional': True, 'default': ['As new', 'Good', 'To renovate']}
    }

    return dict_of_expected_outcome


if __name__ == '__main__':
    try:
        model_directory = 'model/'
        model = Model(model_directory)
        model_file_path = model.model_path
        model_columns_file_path = model.model_column_path
        poly_features_path = model.poly_features_model_path

        load_poly = joblib.load(poly_features_path)
        print("Poly features loaded")

        regressor = joblib.load(model_file_path)
        print("Model loaded")
        model_columns = joblib.load(model_columns_file_path)
        print("Model columns loaded")

    except Exception as e:
        print("No model found here! Need to train first.")
        print(str(e))
        regressor = None
    port = int(os.environ.get('PORT', 5000))
    app.run(host="0.0.0.0", threaded=True, port=port)