Create app.py

app.py

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+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")
+