app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px

# ----------------------------
# Helper functions
# ----------------------------

def calculate_daily_load(appliances):
    return (appliances['Power (W)'] *
            appliances['Qty'] *
            appliances['Hours/day']).sum()

def calculate_panels_needed(daily_load_wh, panel_watt, psh, perf_ratio, coverage):
    energy_per_panel_wh = panel_watt * psh * perf_ratio
    if energy_per_panel_wh == 0:
        return 0
    return int(np.ceil((daily_load_wh / coverage) / energy_per_panel_wh))

def tilt_suggestions(latitude):
    return {
        "Rule of Thumb (year-round)": round(latitude, 1),
        "Summer (lat - 15°)": round(latitude - 15, 1),
        "Winter (lat + 15°)": round(latitude + 15, 1)
    }

def battery_sizing(daily_load_wh, autonomy_days, dod, inverter_eff, battery_capacity_Wh):
    required_storage_Wh = daily_load_wh * autonomy_days
    usable_per_battery = battery_capacity_Wh * dod * inverter_eff
    if usable_per_battery == 0:
        return 0
    return int(np.ceil(required_storage_Wh / usable_per_battery))

def simulate_weekly_monthly(daily_load_wh, daily_prod_wh):
    days = pd.date_range("2025-01-01", periods=365, freq="D")
    df = pd.DataFrame({
        "date": days,
        "load_Wh": daily_load_wh,
        "prod_Wh": daily_prod_wh
    })
    weekly = df.resample("W-MON", on="date").sum()
    monthly = df.resample("M", on="date").sum()
    return weekly, monthly

# ----------------------------
# Streamlit UI
# ----------------------------

st.set_page_config(page_title="☀️ Solar Sizing App", layout="wide")
st.title("☀️ Solar Energy Consumption & Sizing App")

st.sidebar.header("Inputs")

# Location & solar input
latitude = st.sidebar.number_input("Latitude (°)", -90.0, 90.0, 30.0)
psh = st.sidebar.number_input("Peak Sun Hours (hrs/day)", 0.0, 10.0, 5.0)

# Panel specs
panel_watt = st.sidebar.number_input("Panel STC Wattage (W)", 50, 1000, 400)
perf_ratio = st.sidebar.slider("Performance Ratio", 0.5, 0.9, 0.75)
coverage = st.sidebar.slider("System Coverage (%)", 10, 100, 100) / 100

# Battery specs
autonomy_days = st.sidebar.number_input("Battery Autonomy (days)", 0.0, 7.0, 1.0)
dod = st.sidebar.slider("Battery Depth of Discharge (DoD)", 0.1, 1.0, 0.8)
inverter_eff = st.sidebar.slider("Inverter Efficiency", 0.5, 1.0, 0.95)
battery_capacity_Wh = st.sidebar.number_input("Battery Capacity (Wh)", 100, 20000, 5000)

# Appliances table
st.subheader("Appliances")
appliances = st.data_editor(
    pd.DataFrame(columns=["Appliance", "Power (W)", "Qty", "Hours/day"]),
    num_rows="dynamic"
)

if not appliances.empty:
    daily_load_wh = calculate_daily_load(appliances)
    st.write(f"**Total Daily Load:** {daily_load_wh:,.0f} Wh")

    # Panel count
    panels_needed = calculate_panels_needed(daily_load_wh, panel_watt, psh, perf_ratio, coverage)
    daily_prod_wh = panels_needed * panel_watt * psh * perf_ratio

    st.metric("Panels Needed", panels_needed)
    st.metric("Estimated Daily Production (Wh)", f"{daily_prod_wh:,.0f}")

    # Tilt suggestion
    tilts = tilt_suggestions(latitude)
    st.subheader("Tilt Angle Suggestions (°)")
    st.table(pd.DataFrame(list(tilts.items()), columns=["Case", "Tilt"]))

    # Battery sizing
    num_batteries = battery_sizing(daily_load_wh, autonomy_days, dod, inverter_eff, battery_capacity_Wh)
    st.metric("Recommended Batteries", num_batteries)

    # Graphs
    weekly, monthly = simulate_weekly_monthly(daily_load_wh, daily_prod_wh)

    tab1, tab2 = st.tabs(["📅 Weekly", "📆 Monthly"])
    with tab1:
        fig = px.bar(weekly, x=weekly.index, y=["load_Wh", "prod_Wh"],
                     labels={"value":"Energy (Wh)", "date":"Week"},
                     barmode="group", title="Weekly Load vs Production")
        st.plotly_chart(fig, use_container_width=True)
    with tab2:
        fig = px.bar(monthly, x=monthly.index, y=["load_Wh", "prod_Wh"],
                     labels={"value":"Energy (Wh)", "date":"Month"},
                     barmode="group", title="Monthly Load vs Production")
        st.plotly_chart(fig, use_container_width=True)

    # Export
    st.download_button("Download Monthly Data (CSV)", monthly.to_csv().encode("utf-8"),
                       "monthly_results.csv", "text/csv")
else:
    st.info("➕ Add appliances above to calculate system sizing.")