Create app.py

app.py

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+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.")