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import streamlit as st
import pandas as pd
import numpy as np
from streamlit.components.v1 import html
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import StandardScaler
import pickle
import io
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import base64
from functions import *
st.set_page_config(layout="wide",page_title="Carbon Footprint Calculator", page_icon="./media/favicon.ico")
def get_base64(bin_file):
with open(bin_file, 'rb') as f:
data = f.read()
return base64.b64encode(data).decode()
background = get_base64("./media/background_min.jpg")
icon2 = get_base64("./media/icon2.png")
icon3 = get_base64("./media/icon3.png")
with open("./style/style.css", "r") as style:
css=f"""<style>{style.read().format(background=background, icon2=icon2, icon3=icon3)}</style>"""
st.markdown(css, unsafe_allow_html=True)
def script():
with open("./style/scripts.js", "r", encoding="utf-8") as scripts:
open_script = f"""<script>{scripts.read()}</script> """
html(open_script, width=0, height=0)
left, middle, right = st.columns([2,3.5,2])
main, comps , result = middle.tabs([" ", " ", " "])
with open("./style/main.md", "r", encoding="utf-8") as main_page:
main.markdown(f"""{main_page.read()}""")
_,but,_ = main.columns([1,2,1])
if but.button("Calculate Your Carbon Footprint!", type="primary"):
click_element('tab-1')
tab1, tab2, tab3, tab4, tab5 = comps.tabs(["👴 Personal","🚗 Travel","🗑️ Waste","⚡ Energy","💸 Consumption"])
tab_result,_ = result.tabs([" "," "])
def component():
tab1col1, tab1col2 = tab1.columns(2)
height = tab1col1.number_input("Height",0,251, value=None, placeholder="160", help="in cm")
weight = tab1col2.number_input("Weight", 0, 250, value=None, placeholder="75", help="in kg")
if (weight is None) or (weight == 0) : weight = 1
if (height is None) or (height == 0) : height = 1
calculation = weight / (height/100)**2
body_type = "underweight" if (calculation < 18.5) else \
"normal" if ((calculation >=18.5) and (calculation < 25 )) else \
"overweight" if ((calculation >= 25) and (calculation < 30)) else "obese"
sex = tab1.selectbox('Gender', ["female", "male"])
diet = tab1.selectbox('Diet', ['omnivore', 'pescatarian', 'vegetarian', 'vegan'], help="""
Omnivore: Eats both plants and animals.\n
Pescatarian: Consumes plants and seafood, but no other meat\n
Vegetarian: Diet excludes meat but includes plant-based foods.\n
Vegan: Avoids all animal products, including meat, dairy, and eggs.""")
social = tab1.selectbox('Social Activity', ['never', 'often', 'sometimes'], help="How often do you go out?")
transport = tab2.selectbox('Transportation', ['public', 'private', 'walk/bicycle'],
help="Which transportation method do you prefer the most?")
if transport == "private":
vehicle_type = tab2.selectbox('Vehicle Type', ['petrol', 'diesel', 'hybrid', 'lpg', 'electric'],
help="What type of fuel do you use in your car?")
else:
vehicle_type = "None"
if transport == "walk/bicycle":
vehicle_km = 0
else:
vehicle_km = tab2.slider('What is the monthly distance traveled by the vehicle in kilometers?', 0, 5000, 0, disabled=False)
air_travel = tab2.selectbox('How often did you fly last month?', ['never', 'rarely', 'frequently', 'very frequently'], help= """
Never: I didn't travel by plane.\n
Rarely: Around 1-4 Hours.\n
Frequently: Around 5 - 10 Hours.\n
Very Frequently: Around 10+ Hours. """)
waste_bag = tab3.selectbox('What is the size of your waste bag?', ['small', 'medium', 'large', 'extra large'])
waste_count = tab3.slider('How many waste bags do you trash out in a week?', 0, 10, 0)
recycle = tab3.multiselect('Do you recycle any materials below?', ['Plastic', 'Paper', 'Metal', 'Glass'])
heating_energy = tab4.selectbox('What power source do you use for heating?', ['natural gas', 'electricity', 'wood', 'coal'])
for_cooking = tab4.multiselect('What cooking systems do you use?', ['microwave', 'oven', 'grill', 'airfryer', 'stove'])
energy_efficiency = tab4.selectbox('Do you consider the energy efficiency of electronic devices?', ['No', 'Yes', 'Sometimes' ])
daily_tv_pc = tab4.slider('How many hours a day do you spend in front of your PC/TV?', 0, 24, 0)
internet_daily = tab4.slider('What is your daily internet usage in hours?', 0, 24, 0)
shower = tab5.selectbox('How often do you take a shower?', ['daily', 'twice a day', 'more frequently', 'less frequently'])
grocery_bill = tab5.slider('Monthly grocery spending in $', 0, 500, 0)
clothes_monthly = tab5.slider('How many clothes do you buy monthly?', 0, 30, 0)
data = {'Body Type': body_type,
"Sex": sex,
'Diet': diet,
"How Often Shower": shower,
"Heating Energy Source": heating_energy,
"Transport": transport,
"Social Activity": social,
'Monthly Grocery Bill': grocery_bill,
"Frequency of Traveling by Air": air_travel,
"Vehicle Monthly Distance Km": vehicle_km,
"Waste Bag Size": waste_bag,
"Waste Bag Weekly Count": waste_count,
"How Long TV PC Daily Hour": daily_tv_pc,
"Vehicle Type": vehicle_type,
"How Many New Clothes Monthly": clothes_monthly,
"How Long Internet Daily Hour": internet_daily,
"Energy efficiency": energy_efficiency
}
data.update({f"Cooking_with_{x}": y for x, y in
dict(zip(for_cooking, np.ones(len(for_cooking)))).items()})
data.update({f"Do You Recyle_{x}": y for x, y in
dict(zip(recycle, np.ones(len(recycle)))).items()})
return pd.DataFrame(data, index=[0])
df = component()
data = input_preprocessing(df)
sample_df = pd.DataFrame(data=sample,index=[0])
sample_df[sample_df.columns] = 0
sample_df[data.columns] = data
ss = pickle.load(open("./models/scale.sav","rb"))
model = pickle.load(open("./models/model.sav","rb"))
prediction = round(np.exp(model.predict(ss.transform(sample_df))[0]))
column1,column2 = tab1.columns(2)
_,resultbutton,_ = tab5.columns([1,1,1])
if resultbutton.button(" ", type = "secondary"):
tab_result.image(chart(model,ss, sample_df,prediction), use_column_width="auto")
click_element('tab-2')
pop_button = """<button id = "button-17" class="button-17" role="button"> ❔ Did You Know</button>"""
_,home,_ = comps.columns([1,2,1])
_,col2,_ = comps.columns([1,10,1])
col2.markdown(pop_button, unsafe_allow_html=True)
pop = """
<div id="popup" class="DidYouKnow_root">
<p class="DidYouKnow_title TextNew" style="font-size: 20px;"> ❔ Did you know</p>
<p id="popupText" class="DidYouKnow_content TextNew"><span>
Each year, human activities release over 40 billion metric tons of carbon dioxide into the atmosphere, contributing to climate change.
</span></p>
</div>
"""
col2.markdown(pop, unsafe_allow_html=True)
if home.button("🏡"):
click_element('tab-0')
_,resultmid,_ = result.columns([1,2,1])
tree_count = round(prediction / 411.4)
tab_result.markdown(f"""You owe nature <b>{tree_count}</b> tree{'s' if tree_count > 1 else ''} monthly. <br> {f"<a href='https://www.tema.org.tr/en/homepage' id = 'button-17' class='button-17' role='button'> 🌳 Proceed to offset 🌳</a>" if tree_count > 0 else ""}""", unsafe_allow_html=True)
if resultmid.button(" ", type="secondary"):
click_element('tab-1')
with open("./style/footer.html", "r", encoding="utf-8") as footer:
footer_html = f"""{footer.read()}"""
st.markdown(footer_html, unsafe_allow_html=True)
script()
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