app.py

import os
import streamlit as st
from groq import Groq
import requests
from utils import calculate_bill

# Predefined appliance list with typical loads in watts
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
}

# Initialize session state for appliance data
if "appliance_data" not in st.session_state:
    st.session_state.appliance_data = []

# Groq Client Setup
client = Groq(api_key=os.getenv("EnergyGuru_PowerCalc"))

# App Configuration
st.set_page_config(page_title="EnergyGuru_PowerCalc", page_icon="⚡", layout="wide")
st.title("⚡ EnergyGuru_PowerCalc: AI-Driven Bill & Carbon Footprint Tracker")
st.sidebar.header("🔌 Add Appliances")
st.sidebar.write("Select appliances, quantity, and usage details to calculate electricity consumption.")

# Tariff Data
def fetch_tariff_data():
    """Fetch tariff data from the NEPRA website."""
    url = "https://nepra.org.pk/consumer%20affairs/Electricity%20Bill.php"  # Replace with actual URL
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching tariff data: {e}")
        return {"rate_per_unit": 25}  # Return mock data as fallback

with st.spinner("Fetching real-time tariff data..."):
    tariff_data = fetch_tariff_data()
st.success("Tariff data fetched successfully!")

# Appliance Selection
appliance = st.sidebar.selectbox("Select Appliance", options=list(APPLIANCE_OPTIONS.keys()))
default_load = APPLIANCE_OPTIONS[appliance]
quantity = st.sidebar.number_input("Number of Appliances", min_value=1, value=1)
load = st.sidebar.number_input("Load (Watts per Appliance)", min_value=1, value=default_load)
usage_hours = st.sidebar.number_input("Usage Time (Hours per Day)", min_value=0.0, value=6.0)

# Add Appliance Button
if st.sidebar.button("Add Appliance"):
    st.session_state.appliance_data.append({
        "appliance": appliance,
        "load": load,
        "quantity": quantity,
        "usage_hours": usage_hours
    })
    st.sidebar.success(f"Added {quantity} {appliance}(s) to the list!")

# Display Appliance List
st.header("📋 Appliance List")
if st.session_state.appliance_data:
    total_load = 0
    total_energy_wh = 0  # Total energy in watt-hours
    for idx, data in enumerate(st.session_state.appliance_data):
        st.write(f"{idx+1}. {data['appliance']} - {data['quantity']} units, {data['load']}W each, {data['usage_hours']} hours/day")
        total_load += data["quantity"] * data["load"]
        total_energy_wh += data["quantity"] * data["load"] * data["usage_hours"]
    st.write(f"**Total Load: {total_load} Watts**")
else:
    st.info("No appliances added yet.")

# Function to calculate carbon footprint (in kg CO2)
def calculate_carbon_footprint(monthly_energy_kwh):
    carbon_emission_factor = 0.75  # kg CO2 per kWh, adjust based on actual data
    return monthly_energy_kwh * carbon_emission_factor

# Calculate Monthly Bill Button
if st.button("Calculate Monthly Bill"):
    # Convert total energy consumption from watt-hours (Wh) to kilowatt-hours (kWh)
    total_energy_kwh = total_energy_wh / 1000  # Convert energy to kWh

    # Calculate total monthly energy consumption by multiplying by 30 (days)
    monthly_energy_kwh = total_energy_kwh * 30  # Total energy consumption in a month

    # Get the rate from tariff data or use a default rate if not found
    rate_per_unit = tariff_data.get("rate_per_unit", 25)  # Default rate of 25 PKR/kWh

    # Calculate Monthly Bill
    monthly_bill = monthly_energy_kwh * rate_per_unit

    # Calculate the carbon footprint based on monthly energy consumption
    carbon_footprint = calculate_carbon_footprint(monthly_energy_kwh)

    # Query AI Model for suggestions to reduce carbon footprint
    query = f"How can I reduce the carbon footprint of {carbon_footprint:.2f} kg CO2 for a monthly electricity consumption of {monthly_energy_kwh:.2f} kWh?"
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": query}],
        model="llama3-8b-8192",
    )

    # Display Results
    st.write(f"**Total Monthly Electricity Bill: PKR {monthly_bill:.2f}**")
    st.write(f"**Total Carbon Footprint: {carbon_footprint:.2f} kg CO2**")
    st.header("🌿 Tips to Reduce Carbon Footprint")
    st.write(response.choices[0].message.content)

# Footer
st.markdown("---")
st.markdown("### Designed by EnergyGuru ---- Powered by PowerCalc - AI Driven Tracker")