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app.py
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# This app serve Machine Learning to the templates
from flask import Flask, render_template, jsonify, send_from_directory, request
import json
import pandas as pd
import numpy as np
import os
from joblib import load
import pickle
# init app and class
app = Flask(__name__)
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0
# Flask routes
# Homepage
@app.route('/')
def home():
return render_template('index.html')
@app.route('/analysis')
def analysis():
return render_template('analysis.html')
@app.route('/ml')
def machine():
return render_template('machine_learning.html', predictions=0.00)
@app.route('/tables')
def tables():
return render_template('tables.html')
@app.route('/resources')
def resources():
return render_template('resources.html')
@app.route('/about')
def about():
return render_template('about.html')
"""
columns =
'total_volume', 'year', 'Albany', 'Atlanta', 'Baltimore/Washington',
'Boise', 'Boston', 'Buffalo/Rochester', 'California', 'Charlotte',
'Chicago', 'Cincinnati/Dayton', 'Columbus', 'Dallas/Ft. Worth',
'Denver', 'Detroit', 'Grand Rapids', 'Great Lakes',
'Harrisburg/Scranton', 'Hartford/Springfield', 'Houston',
'Indianapolis', 'Jacksonville', 'Las Vegas', 'Los Angeles',
'Louisville', 'Miami/Ft. Lauderdale', 'Midsouth', 'Nashville',
'New Orleans/Mobile', 'New York', 'Northeast', 'Northern New England',
'Orlando', 'Philadelphia', 'Phoenix/Tucson', 'Pittsburgh', 'Plains',
'Portland', 'Raleigh/Greensboro', 'Richmond/Norfolk', 'Roanoke',
'Sacramento', 'San Diego', 'San Francisco', 'Seattle', 'South Carolina',
'South Central', 'Southeast', 'Spokane', 'St. Louis', 'Syracuse',
'Tampa', 'Total U.S.', 'West', 'West Tex/New Mexico', '01', '02', '03',
'04', '05', '06', '07', '08', '09', '10', '11', '12'
'total_volume', 'year', 'Atlanta', 'Baltimore/Washington', 'Boise',
'Boston', 'Buffalo/Rochester', 'California', 'Charlotte', 'Chicago',
'Cincinnati/Dayton', 'Columbus', 'Dallas/Ft. Worth', 'Denver',
'Detroit', 'Grand Rapids', 'Great Lakes', 'Harrisburg/Scranton',
'Hartford/Springfield', 'Houston', 'Indianapolis', 'Jacksonville',
'Las Vegas', 'Los Angeles', 'Louisville', 'Miami/Ft. Lauderdale',
'Midsouth', 'Nashville', 'New Orleans/Mobile', 'New York', 'Northeast',
'Northern New England', 'Orlando', 'Philadelphia', 'Phoenix/Tucson',
'Pittsburgh', 'Plains', 'Portland', 'Raleigh/Greensboro',
'Richmond/Norfolk', 'Roanoke', 'Sacramento', 'San Diego',
'San Francisco', 'Seattle', 'South Carolina', 'South Central',
'Southeast', 'Spokane', 'St. Louis', 'Syracuse', 'Tampa', 'Total U.S.',
'West', 'West Tex/New Mexico', '02', '03', '04', '05', '06', '07', '08',
'09', '10', '11', '12'
"""
@app.route('/makePredictions', methods=['POST'])
def predictions():
post_data = request.form
#load the model:
filename = 'finalized_model.sav'
model = pickle.load(open(filename, 'rb'))
columns = ['total_volume', 'year',
# 'Albany', # Avoid perfect multicollinearity for all dummy variables notebook cell:19
'Atlanta', 'Baltimore/Washington',
'Boise', 'Boston', 'Buffalo/Rochester', 'California', 'Charlotte',
'Chicago', 'Cincinnati/Dayton', 'Columbus', 'Dallas/Ft. Worth',
'Denver', 'Detroit', 'Grand Rapids', 'Great Lakes',
'Harrisburg/Scranton', 'Hartford/Springfield', 'Houston',
'Indianapolis', 'Jacksonville', 'Las Vegas', 'Los Angeles',
'Louisville', 'Miami/Ft. Lauderdale', 'Midsouth', 'Nashville',
'New Orleans/Mobile', 'New York', 'Northeast', 'Northern New England',
'Orlando', 'Philadelphia', 'Phoenix/Tucson', 'Pittsburgh', 'Plains',
'Portland', 'Raleigh/Greensboro', 'Richmond/Norfolk', 'Roanoke',
'Sacramento', 'San Diego', 'San Francisco', 'Seattle', 'South Carolina',
'South Central', 'Southeast', 'Spokane', 'St. Louis', 'Syracuse',
'Tampa', 'Total U.S.', 'West', 'West Tex/New Mexico',
#'01', # Avoid perfect multicollinearity for all dummy variables notebook cell:19
'02', '03',
'04', '05', '06', '07', '08', '09', '10', '11', '12']
#Set to 0 every column in the model
data = [0 for i in range(len(columns))]
# format the volume as an integer
data[0] = int(post_data['total_volume'])
#Format year as integer
data[0] = int(post_data['year'])
# data[columns.index(post_data['year'])]=1
# Set the column requested as True
data[columns.index(post_data['region'])]=1
data[columns.index(post_data['month'])]=1
# input the the data to the model
predictions = model.predict(np.array(data).reshape(1,-1))
#round the predictions to get the needed format
out = round(predictions[0],2) # 1.24
# here we have two options 1: is to return the data to the route (this is good to work in JS)
# 2nd: ours, set predictions as out, so and render the template. predictions is previously set as a variable in html.
return render_template('machine_learning.html', predictions=out) #jsonify({"prediction": out})
@app.route('/data')
def data():
df = pd.read_csv('Tableau/avocado-updated-2020.csv')
out = []
for index, row in df.iterrows():
date = row['date']
price = row['average_price']
volume = row['total_volume']
PLU_4046 = row['4046']
PLU_4225 = row['4225']
PLU_4770 = row['4770']
total_bags = row['total_bags']
type = row['type']
year = row['year']
geography = row['geography']
out.append({
'date': date,
'price': price,
'volume': volume,
'PLU_4046': PLU_4046,
'PLU_4225': PLU_4225,
'PLU_4770': PLU_4770,
'total_bags': total_bags,
'type': type,
'year': year,
'geography':geography
})
return jsonify(out)
if __name__ == '__main__':
app.run(debug=True)