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app.py.txt

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+import streamlit as st
+import requests
+from bs4 import BeautifulSoup
+import urllib3
+
+# Constants for electricity and carbon footprint calculations
+APPLIANCE_OPTIONS = {
+    "Fan": 75, "Air Conditioner (1 Ton)": 1500, "Air Conditioner (1.5 Ton)": 2200, "Refrigerator": 150, 
+    "LED Bulb (20W)": 20, "Tube Light": 40, "Iron": 1000, "Microwave Oven": 1200, "Washing Machine": 500, 
+    "Electric Heater": 1500, "Laptop": 50, "Desktop Computer": 200, "Television (LCD/LED)": 120, 
+    "Water Pump": 1000, "Geyser (Electric)": 3000
+}
+EMISSION_FACTORS = {
+    "car": 0.27, "bus": 0.08, "plane": 0.15, "electricity": 0.5, "meat_diet": 2.5, "veg_diet": 1.0, 
+    "shopping": 1.5, "heating": 0.2,
+}
+CITIES = {
+    "Karachi": (24.8607, 67.0011),
+    "Lahore": (31.5497, 74.3436),
+    "Islamabad": (33.6844, 73.0479),
+    "Rawalpindi": (33.6007, 73.0679),
+    "Faisalabad": (31.4187, 73.0791),
+    "Peshawar": (34.0151, 71.5249),
+    "Quetta": (30.1798, 66.9750),
+    "Multan": (30.1984, 71.4687),
+    "Hyderabad": (25.3960, 68.3773),
+    "Sialkot": (32.4945, 74.5229),
+    "Gujranwala": (32.1617, 74.1883),
+    "Sargodha": (32.0836, 72.6711),
+    "Bahawalpur": (29.3956, 71.6832),
+    "Sukkur": (27.7032, 68.8580),
+    "Larkana": (27.5608, 68.2126),
+    "Sheikhupura": (31.7131, 73.9783),
+    "Abbottabad": (34.1690, 73.2428),
+    "Mardan": (34.1989, 72.0457),
+    "Rahim Yar Khan": (28.4199, 70.3008),
+    "Dera Ismail Khan": (31.8323, 70.9024),
+    "Khuzdar": (27.8111, 66.6101),
+    "Chitral": (35.8508, 71.7864),
+    "Gilgit": (35.8818, 74.4640),
+    "Skardu": (35.3357, 75.5491),
+    "Muzaffarabad": (34.3706, 73.4716),
+    "Mirpur": (33.1445, 73.7519),
+    "Gujrat": (32.5737, 74.0789),
+    "Jhelum": (32.9471, 73.7284),
+    "Attock": (33.7680, 72.3602),
+    "Okara": (30.8138, 73.4458),
+    "Kasur": (31.1157, 74.4464),
+    "Vehari": (30.0452, 72.3527),
+    "Jhang": (31.2698, 72.3169),
+    "Tando Allahyar": (25.4627, 68.7171),
+    "Dadu": (26.7303, 67.7760),
+    "Nawabshah": (26.2483, 68.4096),
+    "Mingora": (35.2012, 72.4258),
+    "Kohat": (33.5560, 71.4356),
+    "Khanpur": (28.6455, 70.6598),
+    "Bannu": (32.9857, 70.6047),
+    "Turbat": (26.0023, 63.0485),
+    "Gwadar": (25.1236, 62.3228),
+    "Zhob": (31.3488, 69.4488),
+    "Swat": (35.2228, 72.4258),
+    "Mandi Bahauddin": (32.5836, 73.4907),
+    "Pakpattan": (30.3423, 73.3860),
+    "Chiniot": (31.7204, 72.9784),
+    "Bhakkar": (31.6336, 71.0657),
+    "Hafizabad": (32.0731, 73.6880),
+}
+OPEN_METEO_URL = "https://api.open-meteo.com/v1/forecast"
+EXCHANGE_RATE_API_URL = "https://open.er-api.com/v6/latest/PKR"
+
+# Initialize session state for appliance data and tariff
+if "appliance_data" not in st.session_state:
+    st.session_state.appliance_data = []
+if "tariff_rate" not in st.session_state:
+    st.session_state.tariff_rate = None
+
+# Function to calculate total energy consumption
+def calculate_total_units():
+    total_energy_wh = 0
+    for data in st.session_state.appliance_data:
+        total_energy_wh += data["quantity"] * data["load"] * data["usage_hours"]
+    return total_energy_wh / 1000  # Convert watt-hours to kilowatt-hours
+
+# Fetch tariff rate
+def fetch_tariff_from_url(url, load_type):
+    try:
+        http = urllib3.PoolManager()
+        response = http.request("GET", url)
+        if response.status == 200:
+            soup = BeautifulSoup(response.data, 'html.parser')
+            tariff_text = soup.get_text()
+
+            load_type_mapping = {
+                "Domestic": "A-1 GENERAL SUPPLY TARIFF RESIDENTIAL",
+                "Commercial": "A-2 GENERAL SUPPLY TARIFF COMMERCIAL",
+                "Industrial": "B - INDUSTRIAL SUPPLY TARIFFS",
+                "Agriculture": "D - AGRICULTURE TARIFF"
+            }
+
+            load_type_label = load_type_mapping.get(load_type, None)
+            if load_type_label:
+                start_idx = tariff_text.find(load_type_label)
+                if start_idx != -1:
+                    tariff_section = tariff_text[start_idx:start_idx + 1000] 
+                    rates = [float(word) for word in tariff_section.split() if word.replace(".", "").isdigit()]
+                    if rates:
+                        return rates[0]  
+        st.warning(f"Failed to fetch tariff data for {load_type}.")
+    except Exception as e:
+        st.error(f"Error fetching tariff: {e}")
+    return None
+
+# Get exchange rate
+def get_exchange_rate():
+    try:
+        response = requests.get(EXCHANGE_RATE_API_URL)
+        response.raise_for_status()
+        data = response.json()
+        return 1 / data["rates"]["USD"]
+    except Exception as e:
+        st.error(f"Error fetching exchange rate: {e}")
+        return 300
+
+# Get heating degree days (for carbon footprint)
+def get_heating_degree_days(latitude, longitude):
+    try:
+        response = requests.get(
+            OPEN_METEO_URL,
+            params={"latitude": latitude, "longitude": longitude, "current_weather": True, "temperature_unit": "celsius"}
+        )
+        data = response.json()
+        current_temp = data["current_weather"]["temperature"]
+        return max(18 - current_temp, 0)
+    except Exception as e:
+        st.error(f"Error fetching weather data: {e}")
+        return 0
+
+# Carbon footprint calculation
+def calculate_footprint(distance_car, distance_bus, distance_plane, electricity_usage, diet_type, shopping_spent_pkr, city, house_area, exchange_rate):
+    latitude, longitude = CITIES[city]
+    heating_degree_days = get_heating_degree_days(latitude, longitude)
+    car_emissions = distance_car * EMISSION_FACTORS["car"]
+    bus_emissions = distance_bus * EMISSION_FACTORS["bus"]
+    plane_emissions = distance_plane * EMISSION_FACTORS["plane"]
+    electricity_emissions = electricity_usage * EMISSION_FACTORS["electricity"]
+    diet_emissions = EMISSION_FACTORS[diet_type] * 30
+    shopping_emissions = (shopping_spent_pkr / exchange_rate) * EMISSION_FACTORS["shopping"]
+    total_emissions = car_emissions + bus_emissions + plane_emissions + electricity_emissions + diet_emissions + shopping_emissions
+    return total_emissions
+
+# Streamlit Interface
+st.title("Electricity Bill and Carbon Footprint Calculator")
+
+# Sidebar for electricity bill calculation
+st.sidebar.subheader("Electricity Bill Calculator")
+tariff_url = st.sidebar.text_input("Enter URL for Tariff Data:")
+load_type = st.sidebar.selectbox("Select Load Type", ["Domestic", "Commercial", "Industrial", "Agriculture"])
+if st.sidebar.button("Fetch Tariff Data"):
+    tariff_rate = fetch_tariff_from_url(tariff_url, load_type)
+    if tariff_rate:
+        st.session_state.tariff_rate = tariff_rate
+        st.sidebar.success(f"Tariff Rate: {tariff_rate} PKR/unit")
+
+appliance = st.sidebar.selectbox("Select Appliance", options=list(APPLIANCE_OPTIONS.keys()))
+quantity = st.sidebar.number_input("Quantity", min_value=1, value=1)
+usage_hours = st.sidebar.number_input("Usage Hours/Day", min_value=1.0, value=6.0)
+
+if st.sidebar.button("Add Appliance"):
+    st.session_state.appliance_data.append({
+        "appliance": appliance, 
+        "load": APPLIANCE_OPTIONS[appliance], 
+        "quantity": quantity, 
+        "usage_hours": usage_hours
+    })
+    st.sidebar.success(f"Added {quantity} {appliance}(s)")
+
+# Show added appliances and calculate bill
+st.subheader("Appliance List")
+if st.session_state.appliance_data:
+    for appliance_data in st.session_state.appliance_data:
+        st.write(f"{appliance_data['quantity']} x {appliance_data['appliance']} ({appliance_data['usage_hours']} hours/day)")
+else:
+    st.write("No appliances added.")
+
+if st.button("Calculate Electricity Bill"):
+    if not st.session_state.tariff_rate:
+        st.warning("Please fetch tariff data.")
+    else:
+        total_units_kwh = calculate_total_units()
+        monthly_bill = total_units_kwh * 30 * st.session_state.tariff_rate
+        st.subheader(f"Electricity Bill: PKR {monthly_bill:.2f}")
+
+# Sidebar for carbon footprint calculation
+st.sidebar.subheader("Carbon Footprint Calculator")
+distance_car = st.sidebar.number_input("Car Distance (km)", min_value=0, value=0)
+distance_bus = st.sidebar.number_input("Bus Distance (km)", min_value=0, value=0)
+distance_plane = st.sidebar.number_input("Plane Distance (km)", min_value=0, value=0)
+electricity_usage = st.sidebar.number_input("Electricity Usage (kWh)", min_value=0, value=0)
+diet_type = st.sidebar.selectbox("Diet Type", ["meat_diet", "veg_diet"])
+shopping_spent_pkr = st.sidebar.number_input("Shopping Spend (PKR)", min_value=0, value=0)
+city = st.sidebar.selectbox("Select the nearest city", options=list(CITIES.keys()))
+house_area = st.sidebar.number_input("House Area (m²)", min_value=0, value=0)
+
+if st.button("Calculate Carbon Footprint"):
+    exchange_rate = get_exchange_rate()
+    carbon_footprint = calculate_footprint(
+        distance_car, distance_bus, distance_plane, electricity_usage, diet_type, shopping_spent_pkr, city, house_area, exchange_rate
+    )
+    st.subheader(f"Carbon Footprint: {carbon_footprint:.2f} kg CO₂/month")

requirements.txt

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+streamlit
+requests
+beautifulsoup4
+urllib3