| 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() | |