novus

# Novus Presents "BACK to the 90's"

## About Us
Novus is a project that aims to reduce climate change through the plantation and nurturing of fruit and flower trees through the help of the mass public. Together, we grow towards the future by returning back to the 90s!

## Predictive Analysis


### importing modules
import os
import math
import json
from flask import Flask, request
from flask import Flask, redirect, url_for, request, render_template, jsonify
#from flask_restplus import Api, Resource
#from flasgger import Swagger
#from flasgger.utils import swag_from

#from werkzeug.utils import secure_filename
#from flasgger import LazyString, LazyJSONEncoder

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

data = pd.read_csv('nasadata.csv')
data = data.rename(columns = {"Area":"country", "Population (Unit 1000 person)":"population","Forest Land (Unit 1000 ha)":"forestland","Forest CO2 emission rate (Tonnes C/ha)":"CO2emission","Burning Rate":"burn"})
#indo = data.iloc[54:81]
data['forestland'] = data['forestland'] * 1000
data['burning_rate'] = (data['burn'] * 100) / data['forestland']
average_rate = data['burning_rate'].mean()
average_rate = round(average_rate,2)
features = ['Year']
x = data[features]
y = data.forestland
indo_model = LinearRegression()
indo_model.fit(x,y)



def predict(year):
    test = np.array([[year]])
    pred_data = indo_model.predict(test)
    #final_pred = str(pred_data[0])
    pred_data  = pred_data/1000000
    final_pred = round(pred_data[0],2)
    #reduce = indo.iloc[0,3] - final_pred
    #years_left = year - 2019
    #per_year_forest = np.around((reduce/years_left), decimals= 2)
    #year_rate = np.around(((per_year_forest*100)/indo.iloc[26,3]), decimals=2)
    #per_tree_cost = 1.5
    #min_tree = 1000
    #number_of_trees = per_year_forest*min_tree
    #total_cost = math.ceil((number_of_trees*per_tree_cost)/1000000)

    #sentence = "Appoximate remaining forest land in " + str(year) + " : " + str(final_pred) + "\n\t\t" + "Total amount of plantations required per year: " + str(per_year_forest) + "\n\t\t\t" + "Total Cost per year" + str(total_cost)
    result = {'year': year, 'area': final_pred, 'amount': average_rate}
    return json.dumps(result)


# Create a Flask instance
app = Flask(__name__)
@app.route('/predict', methods=['POST'])
def upload():
    """
    Serve the inference request
    Pass the image in multipart/form-data to the inference function
    """
    if request.method == 'POST':

      # Get the file from post request
        num = request.form['year']
        new_num = int(num)
        return predict(new_num)
    return 'not success'

# Start the web server
@app.route('/', methods=['GET'])
def index():
    # Main page
    return render_template('index.html')
if __name__ == '__main__':
    app.run()