import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from fpdf import FPDF

# --- Appliance power ratings (in watts) ---
appliance_power = {
    "Fan": 75,
    "LED Light": 15,
    "Refrigerator": 150,
    "TV": 100,
    "Air Conditioner": 1000,
    "Washing Machine": 500,
    "Computer": 200
}

st.set_page_config(page_title="Solar Energy Planner", layout="wide")

st.title("☀️ Solar Energy Consumption & Planning App")

# --- Sidebar: User Inputs ---
st.sidebar.header("🔌 Appliance Load Input")

appliance_data = []
for appliance, watt in appliance_power.items():
    qty = st.sidebar.number_input(f"{appliance} Quantity", 0, 20, 0, key=f"{appliance}_qty")
    hours = st.sidebar.number_input(f"{appliance} Daily Hours", 0, 24, 0, key=f"{appliance}_hours")
    if qty > 0 and hours > 0:
        appliance_data.append({
            "Appliance": appliance,
            "Qty": qty,
            "Hours": hours,
            "Watt": watt,
            "Daily kWh": round(qty * hours * watt / 1000, 2)
        })

# Default values
total_daily_kwh = 0
total_monthly_kwh = 0
num_panels = 0

# --- Show appliance usage table ---
if appliance_data:
    st.subheader("🧮 Appliance-wise Energy Consumption")
    df = pd.DataFrame(appliance_data)
    df["Monthly kWh"] = df["Daily kWh"] * 30
    st.dataframe(df, use_container_width=True)

    total_daily_kwh = df["Daily kWh"].sum()
    total_monthly_kwh = df["Monthly kWh"].sum()

    st.metric("🔋 Total Daily Consumption (kWh)", round(total_daily_kwh, 2))
    st.metric("📅 Total Monthly Consumption (kWh)", round(total_monthly_kwh, 2))
else:
    st.info("Please enter appliance details in the sidebar to start.")

# --- Solar Panel Calculator ---
st.subheader("☀️ Solar Panel Requirement Calculator")
avg_sunlight_hours = st.number_input("Average Sunlight Hours/Day", 1.0, 12.0, 5.5)
panel_watt = st.number_input("Panel Wattage (W)", 100, 600, 300)

if total_daily_kwh > 0:
    kwh_per_panel = round((panel_watt * avg_sunlight_hours) / 1000, 2)
    num_panels = int(np.ceil(total_daily_kwh / kwh_per_panel))
    st.success(f"You need approximately **{num_panels}** panels of {panel_watt}W to cover {round(total_daily_kwh, 2)} kWh/day.")
    st.caption(f"Each panel generates approx. {kwh_per_panel} kWh/day.")

# --- Tilt Angle Calculator ---
st.subheader("📐 Recommended Tilt Angle")
latitude = st.number_input("Enter Latitude of Your Location", -90.0, 90.0, 30.0)
tilt_year = round(latitude * 0.9, 1)
tilt_summer = round(latitude * 0.7, 1)
tilt_winter = round(latitude * 1.1, 1)

st.markdown(f"""
- 📌 **Year-round Tilt Angle**: `{tilt_year}°`  
- 🌞 **Summer Tilt**: `{tilt_summer}°`  
- ❄️ **Winter Tilt**: `{tilt_winter}°`
""")

# --- Graphs: Weekly and Monthly Consumption ---
if total_daily_kwh > 0:
    st.subheader("📊 Energy Consumption Overview")

    week_days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
    weekly_kwh = [round(total_daily_kwh + np.random.uniform(-0.3, 0.3), 2) for _ in range(7)]

    fig1, ax1 = plt.subplots()
    ax1.bar(week_days, weekly_kwh, color='skyblue')
    ax1.set_ylabel("kWh")
    ax1.set_title("Weekly Consumption")
    st.pyplot(fig1)

    months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
              "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
    monthly_kwh = [round(total_daily_kwh * 30 + np.random.uniform(-5, 5), 2) for _ in range(12)]

    fig2, ax2 = plt.subplots()
    ax2.plot(months, monthly_kwh, marker='o', color='green')
    ax2.set_ylabel("kWh")
    ax2.set_title("Monthly Consumption")
    st.pyplot(fig2)

# --- Roof Area Estimator ---
st.subheader("🏠 Roof Area & Panel Capacity")
roof_area = st.number_input("Enter Available Roof Area (sq. ft)", 0, 1000, 200)
panel_area = 18  # Average panel size (sq. ft)
max_panels_fit = int(roof_area / panel_area)
max_capacity_kw = round((max_panels_fit * panel_watt) / 1000, 2)

st.markdown(f"""
- Max panels installable: `{max_panels_fit}`
- Max capacity: `{max_capacity_kw} kW`
""")

# --- Battery Estimator ---
st.subheader("🔋 Battery Backup Estimator")
if total_daily_kwh > 0:
    backup_hours = st.slider("Backup Hours Required", 1, 24, 6)
    avg_load_kw = total_daily_kwh / 24
    battery_size_kwh = round(avg_load_kw * backup_hours, 2)
    battery_ah_12v = round((battery_size_kwh * 1000) / 12, 0)

    st.markdown(f"""
    - Required Battery Size: **{battery_size_kwh} kWh**
    - Battery (12V): **{battery_ah_12v} Ah**
    """)
else:
    st.info("Add appliance details first to calculate battery backup.")

# --- Cost and ROI Estimator ---
st.subheader("💰 Cost & ROI Estimator")
unit_cost = st.number_input("Grid Cost per Unit (kWh)", 5.0, 50.0, 20.0)
panel_cost = st.number_input("Cost per Panel (PKR)", 10000, 100000, 40000)
installation_cost = st.number_input("Installation Cost (PKR)", 0, 100000, 20000)

if num_panels > 0:
    total_cost = int(num_panels) * panel_cost + installation_cost
    monthly_saving = round(total_daily_kwh * 30 * unit_cost, 0)
    roi_months = round(total_cost / monthly_saving, 1)

    st.markdown(f"""
    - 💸 Total System Cost: **PKR {total_cost:,}**
    - 💵 Estimated Monthly Savings: **PKR {monthly_saving:,}**
    - 📈 ROI / Break-even in: **{roi_months} months**
    """)
else:
    st.info("Add appliances and sunlight hours to calculate panel requirements.")

# --- Footer ---
st.markdown("---")
st.caption("Developed with ❤️ using Streamlit | Ready for Hugging Face Deployment")